Hydrological Dynamics of Two Seasonal Floodplain Wetlands in the South Carolina Piedmont

doi:10.22186/jyi.33.4.90-98

Abstract | Introduction | Methods | Results | Discussion | Conclusions |Acknowledgements | 
References | PDF

Abstract

Seasonal floodplain wetlands occur throughout the Piedmont Region of South Carolina, providing a plethora of ecosystem services. As a result of extensive soil erosion during the agricultural period from mid-1700s until mid-1900s, Piedmont floodplains have accreted significantly, altering their natural flood regime. The purpose of this study was to better understand the impacts of land use change and seasonality on the hydrology of two adjacent, seasonal floodplain wetlands. This was done by evaluating water levels in each wetland for a period of twelve months using wells, monitoring groundwater through a piezometer, characterizing sediments from 0 to 21cm, and comparing rainfall events to the Lawson’s Fork Creek stream gage and wetland water levels. It was found that the type and intensity of rainfall events were key components driving wetland water level changes during each season. During storm events, the wetlands aided in flood control; results show that they may be hydraulically connected during intense overbank flooding events. Groundwater was not found to recharge the surface during dry periods, as proposed, which could be due to low sample size and only one piezometer. The results of this study demonstrate the need for long term hydrological research of small seasonal wetlands in order to better protect and manage the ecological services they provide.

Introduction

Wetlands are a common feature in floodplain ecosystems and are known to aid in flood control, act as land buffers to prevent erosion, improve water quality by filtering nutrient-laden sediments, and provide a habitat for unique species of plants and animals (Welsch et al., 1995; Yu et al., 2015). They are also important breeding sites for amphibians and critical foraging locations for reptiles and birds (Semlitsch, 2008; Yu et al., 2015). Since they are found in transitional areas between aquatic and terrestrial ecosystems, floodplain wetlands are largely influenced by rivers and streams (Tockner and Stanford, 2002). Ecological communities that depend on these wetlands are limited by their ability to live in seasonally saturated conditions and are directly impacted by the dynamic hydrology of the wetlands. Since the wetland’s hydrology dictates its ecology, soil type, and community composition, understanding the processes that drive water level changes in these areas is crucial for developing management strategies of watersheds that contain wetlands (Stratman, 2002). More research that solely focuses on the hydrology of floodplain wetlands is needed to supplement and enhance what is already known biologically (Connor & Gabor, 2006).
In contrast to other wetland types such as glacial or coastal wetlands, fluvial or floodplain wetlands tend to have hydroperiods that correspond to stream discharge (Kirkman et al., 1999). A hydroperiod is a seasonal pattern of surface water levels that affects the storage capacity and the water budget of the wetland; they are directly influenced by precipitation, the area’s microtopography, and surface-groundwater interactions (Welsch et al., 1995; Yu et al., 2015). Since hydroperiods drive the biota and biogeochemical processes of a wetland, it is important to characterize and monitor them seasonally (Kingsford, 2000; Kirkman et al., 1999).
Seasonal floodplain wetlands occur throughout the Piedmont Region of South Carolina (Yu et al., 2015). They are characterized by their small area, depressional topography, and shallow depth (Hayashi & van der Kamp, 2000). Although the geomorphic origin of floodplain wetlands varies throughout the world, land use changes tremendously impact a region’s topographical character and function (Pitt et al., 2012). Extensive soil erosion during the agricultural period, from mid-1700s until mid-1900s, had caused the Piedmont floodplains to accrete significantly with an average of 1m to 2m (Happ, 1945). This accumulation of deposited sand and micaceous sandy silt has altered the area’s microtopography and natural flood regime (Happ, 1945).
Such is the case with the Lawson’s Fork creek, a third order stream that discharges into the Pacolet River downstream of Glendale, South Carolina (USGS-SC #02156300). Glendale was the location of the earliest textile mill in this region of the Piedmont and operated from 1832 to 1960. Historic Records indicate that a dam was present at Glendale since the 1830s. After the mill ceased its operation in 1960s, the dam was no longer regulated. Over time, the 22.3-hectare mill pond has been filled in with deltaic sediments, initially transforming the mill pond into a network of distributary channels and islands, which were eventually filled in to become a forested floodplain with seasonal wetlands.
Two neighboring, seasonal wetlands in the Lawson’s Fork floodplain, named Beaver and Dragonfly for the purpose of this study, have an observable impact in the area ecologically. However, little is known about their hydrology. Beaver wetland measured 1.9 acres in area in May 2015, and is depressed 0.6m below the surrounding floodplain. An ephemeral stream on the north side of the wetlands feeds it during inundated conditions. Vegetation is seasonally variable, consisting mainly of water starworts and duckweed in the winter months, and blue joint grass, broadleaf arrowhead, wild proso millet, nut grasses and marsh ferns in the spring/summer months. Scattered hardwood deciduous trees, such as river birch and sweet gum, persist year-round. Dragonfly wetland measured 2.35 acres in May 2015. Seasonal vegetation is similar to that described for Beaver wetland.
Small fluvial wetlands like Beaver and Dragonfly are largely overlooked and understudied, in part due to their small size, the difficulty associated with their delineation, and their transient nature (Pitt et al., 2012). Previous research on seasonal floodplain wetlands has focused on understanding their roles in water quality and purification, their potential as a habitat, and the pertinence of including them in conservation legislation (Coleman, Diefenderfer, Ward, & Borde, 2015; Pitt et al., 2012; Yu et al., 2015). Few studies have focused solely on their hydrology. Thus, the purpose of this study was to better understand the impacts of land use change and seasonality on the hydrology of the area. Our main objective was to observe the response of Beaver and Dragonfly to rainfall events and groundwater, and to evaluate the seasonal patterns of inundation and subsequent water efflux over a twelve-month period. Due to the geographical proximity between Beaver and Dragonfly, we proposed that their water levels would fluctuate similarly in response to temperature changes and storm events. We also expected the groundwater to recharge the surface during seasonally dry periods. Through analysis of wetland water levels, stream gage readings for storm events, groundwater monitoring, and sediment characterization, this study provides key baseline data for the long-term management of these riverine wetlands, and other riverine wetlands around the world. A recent study in Southeast Australia found that floodplain wetlands could act as long-term sinks of atmospheric carbon, suggesting that they may play a role in alleviating the effects of climate change by sequestering CO2 (Kobayashi, Ralph, Ryder, & Hunter,2013). Proper management of seasonal floodplain wetlands and a thorough understanding of their hydrological regime could enhance their function as a sink for CO2, and as critical hubs for biodiversity. Understanding their hydrology is a crucial step towards creating a comprehensive plan to ensure their future survival. Locally, the Spartanburg Area Conservancy is currently building a hiking trail adjacent to the Lawson’s Fork Creek and the two wetlands. The findings of this study will have a direct impact on the management of the area by restricting trail usage when heavy storms are expected to hit the watershed, thus contributing to the trail’s success and the safety of the hikers.

Methods

Field Site

This research was conducted in the Lawson’s Fork Creek watershed in Glendale, SC (34.9450° N, 81.8364° W). The Lawson’s Fork feeds into the Pacolet River in the upstate of South Carolina (Figure 1). The study site contains two neighboring wetlands, named Beaver and Dragonfly for the purpose of this study. They were chosen due to their size, proximity to the Lawson’s Fork creek (Figure 1), and their location downstream of a USGS stream gage (USGS #02156300). The wetland downstream and closest to Glendale was designated as “Beaver,” while the wetland upstream was designated as “Dragonfly” (Figure 1).

Figure 1. ESRI generated map of Lawson’s Fork Creek and the town of Glendale.  The red mark indicates the location of the USGS stream gage (USGS #02156300). The blue marks indicate the location of the neighboring wetlands, designated as Beaver and Dragonfly for the purpose of the study. Map of South Carolina in bottom right corner indicates the location of Spartanburg County.

Figure 1. ESRI generated map of Lawson’s Fork Creek and the town of Glendale. The red mark indicates the location of the USGS stream gage (USGS #02156300). The blue marks indicate the location of the neighboring wetlands, designated as Beaver and Dragonfly for the purpose of the study. Map of South Carolina in bottom right corner indicates the location of Spartanburg County.

In this study, the Army Corps of Engineers’ definition of a wetland was used. It is defined as an area inundated or flooded by surface or groundwater that supports vegetation and fauna adapted to saturated soil conditions (Corps of Engineers Wetlands Delineation Manual, Technical Report Y-87-1, 1987). This definition was chosen because it is used by the Clean Water Act for regulatory and management purposes. However, due to the dynamic nature of the wetlands’ hydroperiod, delineating a specific boundary for each wetland was difficult. The functional boundaries used in this study were delineated when standing water was present in May of 2015, and the boundary was defined at the edge of the ponded water. Thus, the wetland boundary may be an underestimate. The Fish and Wildlife Service (FWS) delineation of the area was used as a template boundary (Figure 2). Google Earth Pro was used to plot waypoints on the FWS map, then those points were ground-truthed by physically walking the wetland perimeter. If the waypoint on the FWS map matched the boundary of standing water in the field, then it was recorded as “present”. If the waypoint was not accurate, it was marked as “absent” and the GPS coordinates were recorded at the observed water boundary (Figure 2).

Figure 2. Google Earth map derived from Fish and Wildlife Service wetland delineation database. Yellow outline represents area of Glendale Mill pond in 1921. Turquoise area represents delineated outer boundary of wetlands, based on FWS delineation. Circle on the right refers to the Beaver Wetland, circle on the left refers to the Dragonfly wetland.

Figure 2. Google Earth map derived from Fish and Wildlife Service wetland delineation database. Yellow outline represents area of Glendale Mill pond in 1921. Turquoise area represents delineated outer boundary of wetlands, based on FWS delineation. Circle on the right refers to the Beaver Wetland, circle on the left refers to the Dragonfly wetland.

Wells and Piezometer

To monitor the water levels, a PVC pipe which is 1.5m in length, 2.54cm in diameter, and screened all the way to the top was installed in each wetland. The wells were protected by a synthetic sock to keep out plant material and fine sediments. Both wells were implanted into roughly 1m of sediment. Each well was equipped with a water level logger (Solinist Canada Ltd., Ontario) hanging from a metal wire attached to a metal cap. A barometric pressure logger (Solinist Canada Ltd., Ontario) was installed on a nearby tree in Dragonfly wetland, approximately 5m away from the well, to monitor the surrounding air pressure. Water level and barometric pressure data were collected from May 29, 2015 to May 14, 2016 at 15-minute intervals. The water level data were downloaded and compensated for pressure using Solinist Levelogger software. Water level was measured in meters to the nearest hundredth. A Welsch two sample t-test was used to determine the differences in water level means. Test was performed using R software.
Rainfall data was collected at the Glendale weather station, and analyzed for 2015 and 2016. Total rainfall in each month was plotted and compared in a bar graph (Figure 3). Gage height data from a major storm event in October 2015 were extracted from the USGS stream gage database. The data were compared to the water levels in each wetland during a four-day storm event, from October 2 to October 6, 2015, and a Welsch two-sample t-test was performed in R.
A piezometer was installed at a depth of 2m in the Dragonfly wetland (Solinist Canada Ltd., Ontario). Piezometers measure the pressure head of groundwater at a specific point. In relation to other water level measurements, they indicate the direction of groundwater flow (Dodds & Whiles, 2010). Piezometer water levels were monitored in relation to the well in Dragonfly, and the direction of groundwater flow was determined. Water levels were measured manually using an electric water level meter six times between May and October of 2015 (Solinist Canada Ltd., Ontario). For analysis, the times when water levels were recorded in the piezometer were matched to the closest 15-minute interval in the well water level logger. Statistical analysis of water levels and piezometer readings was performed in R.

Figure 3. Total monthly rainfall in 2015 and 2016. Data extracted from the Glendale weather station. Numbers above bar show total monthly rainfall, and error bars show standard error. Data reported in inches.

Figure 3. Total monthly rainfall in 2015 and 2016. Data extracted from the Glendale weather station. Numbers above bar show total monthly rainfall, and error bars show standard error. Data reported in inches.

Sediment Analysis

Due to hunting season restrictions, only a single sediment sample was collected in the Beaver wetland using a universal core head sediment sampler (WaterMark®, Canada), chosen because it is specifically designed for saturated, fine sediments. A total of 0.55m of sediment was recovered. The sediments were separated into four observable segments (or depositional layers), weighed, and dried for 3 days. A LaMotte Soil Texture Kit (LaMotte Company, Maryland, USA) was used to determine, via precipitation, the percentage of sand, silt and clay by volume and weight for each segment of the core. The hydraulic conductivity of each segment was then estimated based on a table of values published in Ground and Surface Water Hydrology (Figure 3.7; Mays, 2012).

Results

Wetland Water Levels and the Storm Event in October 2015

Results show that water levels in each wetland fluctuate seasonally in response to rainfall events and temperature changes (Figure 4). While water levels in Dragonfly and Beaver appear to respond similarly to rain events, as shown by the overlapping spikes (Figure 4), mean water levels during the twelve-month period were found to be significantly different (M = 0.52±0.004, SD = 0.411; M = 0.584±0.005, SD = 0.320, p < .001, df = 61448). An inverse relationship was observed between the changes in water levels and the changes in temperature during the year from 2015 to 2016 (Figure 4).

Figure 4. Water level fluctuations (m) and Temperature (C) from May of 2015 to May of 2016. Blue line indicates the Dragonfly wetland, and red line indicates the Beaver wetland water levels (primary vertical axis). Green line indicates Temperature in C (secondary vertical axis).

Figure 4. Water level fluctuations (m) and Temperature (C) from May of 2015 to May of 2016. Blue line indicates the Dragonfly wetland, and red line indicates the Beaver wetland water levels (primary vertical axis). Green line indicates Temperature in C (secondary vertical axis).

During the October 2015 overbank-flooding event, wetland’s water level fluctuations reflected the pattern of water level peaks and lows in the stream (Figure 5). Water levels in the wetlands ranged from 0.5m to 0.95m (Dragonfly: M = 0.838±0.004, SD = 0.091; Beaver M = 0.749±0.005, SD = 0.118), while water levels in the stream ranged from 1.5m to 4.5m (M = 2.593±0.038, SD = 0.829). Peak discharge was approximately 13.0ft3/sec (0.37m3/sec) on October 3, which is also the day in which the highest water levels in the well of each wetland were recorded (0.93m in Dragonfly, 0.87m in Beaver). Water levels in the Dragonfly’s well remained higher than those of the Beaver’s well for the duration of the storm event, and their means were significantly different (p < .001) (Figure 5). A significant difference was also found in the mean water levels of both wetlands when compared to the stream gage mean water levels (p < .001) (Figure 5).

Total Monthly Rainfall

October and November were the wettest months of 2015, while February and August were the wettest months of 2016 (Figure 3). October 2015 was unusually wet, with a total rainfall of 8.85in (22.5cm). Total rainfall during the four-day October storm event was 3.35in (8.5cm). October 2016 was unusually dry, with a total rainfall of only 0.64in (1.6cm). No seasonal pattern of rainfall was found between 2015 and 2016.

Piezometer Readings

Water levels in the piezometer were measured six times during the twelve-month study period and compared to the water levels in Dragonfly (Figure 6). When the wetland was ponded, water levels in the Dragonfly’s well were above 0m. It was found that water levels in the piezometer were lower than water levels in the well: 0.75m in the well and 0.63m in the piezometer in June 2015. When water levels in the Dragonfly’s well were either approaching 0m or below 0m, it was found that water levels in the piezometers were higher than the bottom of the dry well, which is 0.02m. No significant differences were found between the wetland and piezometer water levels (M = 0.422±0.169; M = 0.487±0.117; p = .373).

Sediment Analysis

Estimations of the hydraulic conductivities according to amounts of sand, silt and clay in each sediment interval are given in Table 1 (Mays, 2012). The sediment intervals [10-15cm] and [15-21cm], which contained a higher percentage of sand, had the highest hydraulic conductivity. The sediments at the top of the sediment interval and closest to the surface, [0-6cm], were composed mostly of silty clay and is therefore estimated to have a lower hydraulic conductivity (Table 1).

Table 1. Percent sand, silt, and clay for each sediment interval, determined using a LaMotte Soil Texture Unit. The hydraulic conductivities are estimated according to percentages of sand, silt, and clay.

Table 1. Percent sand, silt, and clay for each sediment interval, determined using a LaMotte Soil Texture Unit. The hydraulic conductivities are estimated according to percentages of sand, silt, and clay.

Discussion

Due to the geographical proximity between Beaver and Dragonfly, we hypothesized that their water levels would fluctuate similarly in response to temperature changes and storm events. We also expected the groundwater to recharge the surface during seasonally dry periods. The study suggests that the type, duration, and intensity of rainfall events were key components driving wetland water level changes during each season. Rainfall in 2015 and 2016 varied monthly and no pattern could be deduced, suggesting that rainfall in this area is variable (Figure 3). The October 2015 overbank-flooding event was not repeated in October of 2016, and occurred instead in the early August of 2016 (USGS, NWISD). The ecological community of each wetland depends on this influx of water; differences in timing of annual overbank events directly impact the phenology of the organisms that require a ponded wetland for their ecological need (Pitt et al., 2012). High volume rainfall events that result in overbank flooding carry nutrient-laden sediments that make the soil extremely fertile when deposited in wetlands (Welsch et al., 1995). Seasonal plants are adapted to these highly variable conditions, and their establishment depends on these overbank-flooding events and seasonal rain events (Tockner Malard, & Ward, 2000). Although excluded from this study, rainfall and temperature changes also contribute to the rates of evapotranspiration, which in turn, impact the water levels in each wetland (Kirkman et al., 1999; Yu et al., 2015).
Lawson’s Fork’s stream discharge during the October 2015 storm event exhibited a flashy hydroperiod with characteristic peaks fluctuating up and back down in hours (Figure 5). Although the graph visually depicts that Beaver and Dragonfly water levels are fluctuating similarly, results show that mean wetland water levels are significantly different during the storm event (Figure 5). While we expected wetland water levels to differ from Lawson’s Fork’s water levels, a statistical difference in the mean water levels of Beaver and Dragonfly during the storm event was surprising, and indicates that the wetlands respond differently to over-bank flooding events, which rejects our original hypothesis. Despite their geographical proximity, this difference in response could largely be attributed to the size of the wetlands. Dragonfly has a greater surface area, and thus maintained a higher water level than Beaver during the storm event. A larger basin allows a wetland to store more water, thus increasing its water level over time. A second explanation for why our hypothesis was not supported is the presence of an ephemeral stream north of the Beaver wetland. Ephemeral streams are common in floodplain wetlands, and can act as regulatory mechanisms for controlling the size and extent of a wetland (Junk, Bayley, & Sparks, 1989). Pitt et al. (2012) also found that seasonal wetlands in the Piedmont region are associated with ephemeral or non-permanent streams, and actually confound remote sensing instrumentation. This outflow of water is likely to be contributing to the water level differences during large storm events.

Figure 5. Lawson’s Fork Creek Gage height (m) and wetland water levels (m) during the October 2, 2015-October 6, 2015 storm event. Blue line is the gage height, and peaks reflect the height of the stream at a particular point in time during the event (primary vertical axis). Red line indicates water levels in Dragonfly, and green line indicates water levels in Beaver (secondary vertical axis).

Figure 5. Lawson’s Fork Creek Gage height (m) and wetland water levels (m) during the October 2, 2015-October 6, 2015 storm event. Blue line is the gage height, and peaks reflect the height of the stream at a particular point in time during the event (primary vertical axis). Red line indicates water levels in Dragonfly, and green line indicates water levels in Beaver (secondary vertical axis).

Higher water levels in the Lawson’s Fork creek when compared to the wetlands can be explained by a floodplain wetland’s hydroperiod, which typically tends to be prolonged (Figure 5). Since wetlands act as flood storage basins, they tend to have longer response times and lower peak stormflows (Welcsh et al., 1995). A lower peak in the wetland water levels was observed when compared to the USGS gage height in Lawson’s fork, indicating that Beaver and Dragonfly wetlands aid the floodplain in flood control, and regulate the influx of water during overbank events (Figure 5). A lower peak in wetland water level could also be explained by the area’s depressional topography, which could be slowing down the rate at which water inundates each wetland, causing the peak to culminate at a slower rate.
Beaver’s lower water levels than Dragonfly’s during and after the October 2015 storm event may be due to the presence of an ephemeral stream north of the Beaver wetland. Not only could this stream contribute to the differences in response to storm events, as discussed above, it may also be subsidizing water in Beaver and regulating how much water that can be stored in the wetland. This water outflow may act as a regulatory mechanism for the wetland’s water levels, providing an outlet for flood waters that is not present in Dragonfly wetland (Welsch et al., 1995).
While Beaver and Dragonfly wetlands responded similarly to rainfall events of low intensity, they ponded at different rates after the major storm event in October of 2015 (Figure 4 & Figure 5). Dragonfly ponded rapidly, while Beaver ponded more slowly and maintained relatively lower water levels than Dragonfly after the storm event. One possible explanation is that Beaver and Dragonfly may differ in storage capacity, determined by how well the soils drain and the micro-topography of the area (Welcsh et al., 1995). Dragonfly is at a slightly higher elevation compared to Beaver, so the lag time in Beaver’s inundation could be due to the fact that the wetlands are hydraulically connected. When the Dragonfly wetland reached a certain threshold of inundation, water runs off. This outflow of water becomes the inflow into the Beaver wetland. Since water flows from high to low elevation, the delay in inundation of Beaver could be attributed to this difference in elevation. Beaver’s and Dragonfly’s possible hydraulic connection could have important ecological implications, such as the habitat expansion of aquatic wildlife by creating a channel that allows for movement between wetlands, enhance spore or seed dispersal of plants, and can increase the water’s nutrient availability (Weber, 2012; Welcsh et al., 1995).
 Along with surface water dynamics, it was inferred that groundwater played a significant role in the wetlands during seasonally dry periods. While results showed that groundwater levels were higher in the piezometer than in the Dragonfly well when the wetlands were dry, a lack of significant differences between piezometer water levels and Dragonfly water levels indicate that the differences were likely to be due to chance and/or a small sample size. Higher water levels in the piezometer relative to water levels in the well suggest that groundwater is recharging the surface water, while higher water levels in the Dragonfly well relative to levels in the piezometer indicate that surface water is probably also recharging the groundwater, suggesting that groundwater flows toward the wetland and recharges the surface during dry periods (Leopold, 1997). With a larger sample size, we would expect to conclude that groundwater recharge possibly kept the soil moist and the root zone fertile, sustaining large amounts of seasonal grasses and other plants during the hot summer months (McCarthy, 2005).
In this study, the hydraulic conductivity of the sediments increased with depth, which is typical in wetland soils (McCarthy, 2005). Clay sediments, which were found mostly at 0cm to 6cm below the ground surface, are finer in grain and more tightly compacted, causing them to retain water due to their low permeability and high microscale porosity. From 6cm to 21cm below the ground surface, the sediments tended to be silty sands, which have a higher hydraulic conductivity due to larger pore space between grains (Table 1). Since clay sediments have a smaller pore size between grains, the sediments retained more water near the surface and drained better with increasing depth.

Figure 6. Water levels in the piezometer (red line) compared to water levels in the Dragonfly well (blue line) between May and October of 2015. Water levels are in meters. Error bars show standard error.

Figure 6. Water levels in the piezometer (red line) compared to water levels in the Dragonfly well (blue line) between May and October of 2015. Water levels are in meters. Error bars show standard error.

Due to the use of only one piezometer, more research involving multiple piezometers is needed to quantify and clarify the role of groundwater in these neighboring wetlands. Quantifying and modeling water table configurations with a small sample size can lead to highly variable data, and produce inconclusive results (Rosenberry & Winter, 1997). The lack of significance found in the groundwater measurements is likely due to a small sample size. For this reason, a lateral transect of multiple piezometers covering areas in and between each wetland, as well as frequent measurements for a longer period of time, are needed to get a more holistic understanding of the role of groundwater in these wetlands. A second source of error is the possible underestimation of wetland size. The defined boundary includes only where standing water was present during that particular day when the observation was made, and not at the point where hydric soils, which are soils formed under conditions of saturation, transition into land. Thus, the area of each wetland is possibly larger. A final source of error is that the sediment analysis came from a single soil sample in Beaver wetland. A second soil sample from Dragonfly wetland, as well as one between the wetlands, is needed in future studies to determine whether or not the differences in soil characteristics, such as moisture and hydraulic conductivity, impact the results of this study. More soil samples across the wetlands could potentially explain how and if groundwater recharges the wetlands. Therefore, a future study of groundwater would incorporate a more in-depth soil study to supplement the data.
The results found in this study are not only informative on a local scale, but can be applied and expanded to floodplain wetlands across the Southeastern USA. Due to the high variability of rainfall events from year to year in the upstate of South Carolina, annual flood frequencies are difficult to be estimated (Feaster & Tasker, 2002). This study is a starting point for further research into the response of surface and groundwater in wetlands, the influence that seasonality has on water influx, the effects of evapotranspiration rates on wetland water level, habitat heterogeneity, and the protection or management of small floodplain wetlands. Long term study of these wetlands, as well as other riverine wetlands in the Southeast and the world, can lead to a more informed management of these dynamic areas. Future studies should focus on characterizing the ecological community of the wetlands, quantifying the role of groundwater, evaluating the role of evapotranspiration, and monitor wetland water levels long term to determine flood frequency rates.

Acknowledgements

The authors would like to thank the Environmental Studies Department at Wofford College for making this research possible, and to Dr. Savage and Dr. Ferguson for the constant support, wisdom and guidance during the experimental process.

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Correlations between Gray-White Matter Contrast in Prefrontal Lobe Regions and Cognitive Set-Shifting in Healthy Adults

doi:10.22186/jyi.33.4.98-107

Abstract | Introduction | Methods | Results | Discussion | Conclusions |Acknowledgements | 
References | PDF

Abstract

Humans have a unique capacity for higher order cognition such as planning and multitasking. These abilities are collectively referred to as executive functions. This study investigates cognitive set-shifting, a type of executive function that involves shifting from one task to another. Advances in neuroimaging have allowed for the structural integrity of specific frontal lobe subregions to be probed with greater resolution. One such measure is the intensity contrast between cortical gray and white matter, with greater contrast indicating better development. This study tested whether the gray-white matter contrast (GWC) in eight subregions of the prefrontal cortex (PFC) was associated with set-shifting abilities in 61 healthy participants. Set-shifting abilities were measured using two neuropsychology tests: Trail Making Test B (TMT-B) and Wisconsin Card Sorting Test-Perseverative Errors (WCST-PE), with a third test, the Boston Naming Test (BNT), used to determine the discriminant validity of set-shifting findings. Cognitive set shifting was significantly correlated with GWC in the left ventrolateral PFC (Broca’s area), the left and right middle frontal gyri (dorsolateral PFC), and the left and right superior frontal gyri. These findings indicate that successful set shifting relies on the structural integrity of ventrolateral and dorsolateral PFC, but not the basal orbitofrontal  regions.

Executive functions are a set of cognitive processes essential in organizing and monitoring behaviors conducive to the attainment of a goal. There are three core executive functions: working memory (short term memory that is manipulated), response inhibition (self-control) and cognitive flexibility (the ability to think about multiple concepts simultaneously) (Miyake et al., 2000). Some of the basic executive functions, such as working memory and inhibitory control, can be observed early in infants. However, the development of more complex executive functions, including cognitive flexibility (also referred to as “cognitive set shifting”), is what allow adults to complete challenging tasks. Many of these occupational tasks are coordinated and completed in the prefrontal cortex (PFC) (Miskin et al., 2015).
Structurally, the PFC consists of both gray and white matter. Gray matter is mainly comprised of cell bodies, dendrites and unmyelinated axons (Budday et al., 2015). It enables muscle movement by directing motor stimuli to neurons in the central nervous system (CNS) and contains glial cells (astrocytes, oligodendrocytes, etc.), which are responsible for providing nutrients and support to neurons. White matter is tissue made mostly of neuronal axons that are insulated by a lipid sheath known as myelin. Myelin allows for saltatory conduction, enabling the brain to send action potentials at higher speeds. Thus, gray- and white-matter regions complement each other and work together to relay impulses efficiently and quickly.
Brain lesion studies suggest that the PFC plays an important role in executive functioning (Bissonette, Powell, & Roesch, 2013), but the specific regions within the PFC that are relevant have yet to be fully identified. This project used quantitative magnetic resonance imaging (qMRI) to obtain grey-white matter contrast (GWC as a measurement of prefrontal lobe brain structure integrity. The less contrast there is, the more blurring occurs at the junction between the cortical gray matter and adjacent white matter. Blurring can occur when neurons designated for gray matter get stuck in the white matter during cortical development. GWC is measured by computing a ratio of signal intensity values in the gray matter above the gray-white junction to signal intensity values in the white matter below. It was chosen as a measure of cortical structural integrity due to previous findings that it is linearly related to decreased language function bilaterally in the temporal, parietal and frontal regions and that it is a mediator of group differences in cognitive performance between patients with epilepsy and healthy controls (Blackmon et al., 2014). While the ratio of grey matter to white matter volume has proven to be a useful brain image modality, previous studies mostly used this for the investigations involving brain aging and Alzheimer’s disease (Taki, Thyreau, Kinomura, Sato, & Goto, 2011).
This study analyzes cognitive performance based on cognitive set shifting, which involves alternating between one task and another. An example is switching back and forth from solving a math problem to answering an email. Shifting from one activity to another can be difficult for some people, especially if the tasks require close attention. Difficulty in shifting between tasks is known as cognitive rigidity, which can be an indication of many different human psychiatric disorders and a lack of sufficient executive function (Chan, Shum, Toulopoulou, & Chen, 2008). Difficulty in set-shifting is often noticed in conditions such as autism spectrum disorder, Alzheimer’s dementia, major depression disorder and other neuropsychiatric conditions (Elliott, 2003). On the other hand, cognitive flexibility enables individuals to focus their attention on a number of different tasks.
Neuropsychological tests have been shown to be useful in assessing higher order functioning (Lezak, Howieson, Bilger, & Tranel, 2012). In this study, two neuropsychological tests were used to assess set-shifting abilities: Trail Making Test-B (TMT-B) (created by the Army for the Individual Tests of General Ability) and the Wisconsin Card Sorting Test Perseverative Errors (WCST-PE) . The TMT-B requires participants to connect dots that are labeled either numerically or alphabetically in an ascending alphanumeric fashion. WCST-PE requires participants to hold in mind three different criteria: shape, number and color, as they try to find the rule set by the test proctor. In both TMT-B and WCST-PE, having to switch between more than one category of thought requires participants to tap into their executive functioning skills. Struggling with these set-shifting tasks may signify problems with participants’ PFC and executive functioning abilities (Bissonette et al., 2013).
Previous studies have demonstrated a correlation between GWC in the PFC and decreased cognitive functioning, but the specific subregions within the PFC are yet to be fully elucidated. This investigation used both qMRI and neuropsychological measures to investigate which PFC subregions have the strongest relationships with cognitive set shifting. Specifically, this study explored the correlation between healthy participants’ TMT-B and WCST-PE scores and average GWC in eight regions of interest (ROI): the left (Broca’s area) and right ventrolateral PFC, the left and right middle frontal gyri (dorsolateral PFC), the left and right superior frontal lobes, and the left and right orbitofrontal cortices (OFC). The OFC was used as a negative control as it is known to function in reward and emotion processing, but not in set shifting (Kringelbach, 2004).
The following hypotheses were tested in this investigation: 1) there will be a positive correlation of GWC of the left- and right-hemisphere superior frontal gyri with TMT-B and WCST-PE scores (higher scores indicate worse cognitive set-shifting abilities); 2) there will be no correlation of GWC in the left or right orbitofrontal cortex (OFC) with TMT-B and WCST-PE scores; 3) there will be a positive correlation of GWC of the left ventrolateral PFC with TMT-B and WCST-PE scores; 4) there will be a positive correlation of GWC in the left and right middle frontal gyri (dorsolateral PFC) with TMT-B and WCST-PE scores; and 5) there will be no correlations of GWC in any of the prefrontal lobe brain regions with performance on the BNT. This study will potentially pinpoint the exact area of the brain that malfunctions in people who struggle with set-shifting, for example, those who are affected with autism, Down’s syndrome and attention deficit hyperactivity disorder (ADHD).

Materials and Methods

Ethics Statement

The study had current approval by the Institutional Review Board (IRB) at New York University and was conducted in accordance to the Declaration of Helsinki (1964, 2008). All subjects participated voluntarily were given detailed information about the study and gave written consent before participating in the study.

Participants

61 healthy adults (31 males/30 females) with no history of neurological disease, psychiatric illness, developmental learning disorders or traumatic brain injury volunteered to take a series of tests to measure their cognitive set-shifting abilities and to undergo MRI scanning at the New York University Center for Brain Imaging. Their ages ranged from 15 to 70 years at the time of scanning (M = 31.94 years, SD = 13.34). Group education levels were similar across subjects (M = 15.96 years, SD = 1.91). There were 56 right-handed participants, 4 left-handed participants and 1 ambidextrous participant.

MRI Scanning

Imaging was performed at the NYU Center for Brain Imaging on a 3T head-only MRI scanner (Siemens, New York). Image acquisition included a conventional three-plane localizer and two T1-weighted gradient-echo sequence (MPRAGE) volumes (TE = 3.25ms, TR = 2530ms, TI = 1.100ms, flip angle = 7°, FOV = 256mm, voxel size = 1×1×1.33mm). Acquisition parameters were optimized for increased gray-white matter image contrast.

Gray-White Matter Contrast (GWC)

GWC values were obtained by sampling T1 image intensity contrast at both 0.5mm above and below the gray-white interface with trilinear interpolation. These values were used to create a ratio score: (gray – white)/(gray + white) (Figure 1). Four main processes were involved: (1) segmentation of the white matter; (2) patchwork of the gray-white matter surfaces; (3) inflation of the folded surface; and (4) automatic correction of topological defects (Dale, Fischl, & Sereno, 1999). GWC values ranged from -1 to 0, where scores closer to zero represent higher degrees of blurring around the gray-white inner surface. Mean GWC values were extracted for each participant for each of the following ROIs: the left (Broca’s) and right ventrolateral PFC, the left and right middle frontal gyri (dorsolateral PFC), the left and right superior frontal gyri, and the left and right orbitofrontal cortices (OFC). Images were further processed with the FreeSurfer (4.0.2) software package (http://surfer.nmr.mgh.harvard.edu). Mean signed curvature was estimated at each vertex using standard FreeSurfer, giving a measure of the “sharpness” of cortical folding, differentiating between gyral and sulcal regions.

Figure 1. Computing GWB: Sampling points on T1-weighted MPRAGE image with gray-white (GW) junction surface (yellow line) and pial surface (red line). The blue dot represents the sampling location of the gray matter intensity value at 0.5mm into the gray matter relative to the GW junction. The purple dot shows the sampling location of the white matter intensity value at 0.5mm into the white matter relative to the GW junction (adapted from Blackmon et al., 2014).

Figure 1. Computing GWB: Sampling points on T1-weighted MPRAGE image with gray-white (GW) junction surface (yellow line) and pial surface (red line). The blue dot represents the sampling location of the gray matter intensity value at 0.5mm into the gray matter relative to the GW junction. The purple dot shows the sampling location of the white matter intensity value at 0.5mm into the white matter relative to the GW junction (adapted from Blackmon et al., 2014).

Cognitive Assessments

Trail Making Test B (TMT-B)

TMT-B was designed to test an individual’s set-shifting ability through a task that involves connecting dots in an alphanumeric manner (Figure 2). As this test involves continually switching between the letters and numbers very quickly, it has been shown to be effective in determining cognitive set-shifting ability. The participant is given a sheet of paper with both numerically and alphabetically labeled dots, and the goal is to connect them as quickly as possible in ascending order (1-A-2-B-3-C…, etc.). Scoring is based on the time it takes for the participant to complete the test. Longer times of test completion are represented by higher scores, indicating lower performance in the test and, thus, poorer set-shifting ability (Spreen & Strauss, 1991).

Figure 2. Example of Trail Making Test-B. Participants trace a sequence alternating between numbers and letters in ascending order (set-shifting).

Figure 2. Example of Trail Making Test-B. Participants trace a sequence alternating between numbers and letters in ascending order (set-shifting).

Wisconsin Card Sorting Test-Perseverative Errors (WCST-PE)

The WCST-PE was designed to test cognitive set-shifting abilities by having the participant match cards according to concealed rules set by a test conductor (Figure 3). The test conductor places four cards in a line in front of the participant and then sets a concealed organizational rule based on color, pattern, number or type of shape. The participant is given several stimulus cards with images of various shapes, colors and numbers, and has to place each in one of the four piles set by the test conductor. Through trial and error, the participant attempts to place the cards into the piles according to the hidden rule. The test conductor only tells the participant whether the match is correct or incorrect. Once the participant correctly identifies the rule, the test proctor changes it without telling the participant. For instance, the rule can change from matching color to matching shape. Scoring is based on the participant’s number of perseverative errors: the number of times the participant puts down a card not in line with the conductor’s current rule, but consistent with a previously successful rule. In other words, these errors reflect difficulty in switching from a previously successful rule to a new rule. Higher numbers of perseverative errors on this task indicate higher total scores and poorer test performance (Spreen et al., 2006).

Figure 3. Reproduction of cards used for the Wisconsin Card Sorting Test-Perservative Errors (WCST-PE). Participants match their cards with other cards according to a hidden rule determined by the test proctor.

Figure 3. Reproduction of cards used for the Wisconsin Card Sorting Test-Perservative Errors (WCST-PE). Participants match their cards with other cards according to a hidden rule determined by the test proctor.

Boston Naming Test (BNT)

The BNT was the only non-set-shifting test administered and was used to measure a type of language ability known as word retrieval. The test consists of 60 pictures of various objects shown to the participant in order of increasing difficulty (high- to low-frequency objects) (Figure 4). Each participant is given a time limit of 20s to correctly name all 60 images. If the participant fails to give the correct response, the examiner may give the participant the initial sound of the target word. The examiner scores each item + or – according to the scoring procedures (max score = 60). Higher scores indicate better performance in this test (Spreen et al., 1991). The Boston Naming Test (BNT) was used as a measure of discriminant validity to determine whether PFC findings are specific to cognitive set-shifting abilities and not cognitive functioning in general. The BNT is considered a measure of language ability that does not rely on PFC to the same extent as executive functioning measures (Lezak et al., 2012). Thus, the BNT was used as a negative control to ensure that GWC in PFC subregions were correlated only with set-shifting abilities.

Figure 4. Reproduction of 6 images used for the Boston Naming Test (BNT). Participants are given 20 seconds to identify each individual object.

Figure 4. Reproduction of 6 images used for the Boston Naming Test (BNT). Participants are given 20 seconds to identify each individual object.

Statistical Analysis

GWC averages from each participant were calculated for the eight PFC regions of interest. TMT-B, WCST-PE and BNT test scores were available for each participant. Two-tailed Pearson correlation r-tests were run between mean GWC values in each ROI and scores from each neuropsychological test. Results were evaluated for statistical significance using a threshold of p < .05. This threshold was adjusted to account for multiple comparisons using the Bonferroni correction, requiring division of the p-value threshold by the number of tests administered for each dependent variable. Given that eight different ROIs were tested for each dependent variable, the p-value of .05 was divided by eight to determine a Bonferroni threshold of p < .00625.

Results

Five regions were found to have GWC values significantly correlated with at least one set-shifting test (Figures 5-7). All significant correlations were positive and linear; increased GWC was associated with increased set-shifting scores, indicating worse performance. Out of the eight PFC subregions, four regions had GWC values that were significantly correlated with WCST-PE scores (M = 8.4 perseverative errors, SD = 6.3): the left superior frontal gyrus, right superior frontal gyrus, left middle frontal gyrus (dorsolateral PFC) and left ventrolateral PFC (Broca’s). In addition, four regions had GWCs that were significantly correlated with TMT-B scores (M = 71.1 seconds, SD = 41.2): the left ventrolateral PFC (Broca’s), right middle frontal gyrus (dorsolateral PFC), left superior frontal gyrus and the right superior frontal gyrus. No correlations were found between the Boston Naming Test scores (BNT) (M = 53.2 correct identifications, SD = 5.0) and any of the eight PFC subregions (M GWC of all eight ROIs = -0.13, SD = 0.011). Table 4 shows the mean GWC of all eight ROIs and the standard deviation.

Figure 5. The scatter plots show the relationship between the time for the participants to complete TMT-B. (A) left superior frontal gyrus, (B) right superior frontal gyrus, (B) left ventrolateral gyrus, and (D) right middle frontal gyrus GWB. Longer time for TMT-B completion reflects poorer performance and greater values for GWB.

Figure 5. The scatter plots show the relationship between the time for the participants to complete TMT-B. (A) left superior frontal gyrus, (B) right superior frontal gyrus, (B) left ventrolateral gyrus, and (D) right middle frontal gyrus GWB. Longer time for TMT-B completion reflects poorer performance and greater values for GWB.

 

Figure 6. The scatter plots show the relationship between the time for the participants to complete WCST-PE. (A) left superior frontal gyrus, (B) right superior frontal gyrus, (C) left ventrolateral gyrus, and (D) left middle frontal gyrus GWB. Greater number of errors on WCST-PE reflects poorer performance and greater values for GWB.

Figure 6. The scatter plots show the relationship between the time for the participants to complete WCST-PE. (A) left superior frontal gyrus, (B) right superior frontal gyrus, (C) left ventrolateral gyrus, and (D) left middle frontal gyrus GWB. Greater number of errors on WCST-PE reflects poorer performance and greater values for GWB.

 

Figure 7. The scatterplot shows the relationship between the number of participants’ WCST preservative errors and their time to complete TMT-B. Greater number of errors on WCST-PE reflects poorer performance and longer time for TMT-B completion reflects poorer performance.

Figure 7. The scatterplot shows the relationship between the number of participants’ WCST preservative errors and their time to complete TMT-B. Greater number of errors on WCST-PE reflects poorer performance and longer time for TMT-B completion reflects poorer performance.

GWC and Trail Making Test B (TMT-B)

Correlations between GWC values from the eight PFC subregions and each participant’s TMT-B score were analyzed. GWC values in four regions were found to have significant correlations with TMT-B performance after adjustment for multiple comparisons: the left ventrolateral PFC (Broca’s area) (r = .36, p = .005), the right middle frontal gyrus (dorsolateral PFC) (r = .39, p = .002), the left superior frontal gyrus (r = .40, p = .002) and the right superior frontal gyrus (r = .42, p = .001). GWC values from the four remaining ROIs did not have significant correlations with TMT-B performance after adjustment for multiple comparisons: the right ventrolateral PFC (r = .33, p = .009), left middle frontal gyrus (r = .34, p = .007), left orbitofrontal cortex (r = .13, p = .341) and right orbitofrontal cortex (r = .17, p = .190) (Figure 5).

Table 1. Correlation coefficients r and p-values for correlations between GWC of all brain regions tested with TMT-B and WCST-PE neuropsychological test performance. Values with asterisks are significant after Bonferroni correction for multiple comparisons.

Table 1. Correlation coefficients r and p-values for correlations between GWC of all brain regions tested with TMT-B and WCST-PE neuropsychological test performance. Values with asterisks are significant after Bonferroni correction for multiple comparisons.

GWC and Wisconsin Card Sorting Test-Perseverative Errors (WCST-PE)

Correlations between GWC values from the eight PFC subregions and each participant’s WCST-PE score were analyzed. GWC values in four regions were found to have significant correlations with WCST-PE performance after adjustment for multiple comparisons: the left ventrolateral gyrus (Broca’s area) (r = .38, p = .003), the left middle frontal gyrus (r = .35, p = .006), the left superior frontal gyrus (r = .37, p = .004) and the right superior frontal gyrus (r = .36, p = .004). GWC values from the four remaining ROIs did not have significant correlations with WCST-PE performance after adjustment for multiple comparisons: right ventrolateral PFC (r = .27, p = .039), right middle frontal gyrus (r = .33, p = .009), left orbitofrontal cortex (r = .27, p = .038) and right orbitofrontal cortex (r = .24, p = .067) (Figure 6).

GWC and Boston Naming Test (BNT)

There were no significant correlations between GWC values from the eight ROIs and BNT performance: the left ventrolateral gyrus (Broca’s area) (p = .547), left middle frontal gyrus (p = .570), left superior frontal gyrus (p = .618), right superior frontal gyrus (p = .625), right ventrolateral gyrus (p = .501), right middle frontal gyrus (p = .754), left orbitofrontal cortex (p = .355), and right orbitofrontal cortex (p = .836) (Table 3).

Table 2. Mean and standard deviation of demographics: age at time of scan, age at time of neuropsychology examination, and years of education of all the subjects.

Table 2. Mean and standard deviation of demographics: age at time of scan, age at time of neuropsychology examination, and years of education of all the subjects.

 

Table 3. Correlations between the three neuropsychology tests; values with asterisks are significant after Bonferroni correction for multiple comparisons.

Table 3. Correlations between the three neuropsychology tests; values with asterisks are significant after Bonferroni correction for multiple comparisons.

TMT-B & WCST-PE and BNT

It was found that TMT-B performance was significantly correlated with WCST-PE performance (r = .392, p = .002) (Figure 7). Both TMT-B performance (p = .088) and WCST-PE performance (p = .266) were not significantly correlated with BNT.

Discussion

Correlations between PFC GWC and set-shifting abilities were analyzed in 61 healthy participants. This study tested for five factors: 1) a positive correlation of GWC in the left and right hemisphere superior frontal gyri with TMT-B and WCST-PE test scores; 2) no correlation of GWC in the left or right OFC with TMT-B and WCST-PE test scores; 3) a positive correlation of GWC in the left ventrolateral PFC with TMT-B and WCST-PE test scores; 4) a positive correlation of GWC in the left and right middle frontal gyri (dorsolateral PFC) with performance TMT-B and WCST-PE test scores; and 5) no correlations of GWC in any PFC region with performance on the BNT. The results are consistent with these hypotheses. One slight incongruence between our hypotheses and results is that the left and right middle frontal gyri (dorsolateral PFC) were split in their correlations with set-shifting performance. GWC in the left middle frontal gyrus was correlated with only WCST-PE scores, whereas GWC from the right middle frontal gyrus was correlated with only TMT-B scores. These findings suggest that set shifting is not controlled by the entire PFC, but by certain PFC subregions instead, and that different types of set shifting are correlated with different patterns of PFC subregion involvement.
Few existing studies map PFC subregions to specific functions. One domain of cognition that is thought to be localized to the PFC is a set of processes known as executive functions. This study focused on a type of executive function known as set shifting, or the ability to alternate between two or more tasks. There are multiple types of set shifting that vary based on the additional component of processes involved (i.e., visual or motor). Some studies have localized set-shifting ability to the frontal parietal area; however, findings have been inconsistent due to methodological differences across studies (Pa et al., 2010). The effects of these differences are particularly amplified in studies of higher-order cognition due to the vast and relatively unknown networks involved. In order to maintain a narrower focus, this study concentrated on a few subregions within the PFC and just two types of set shifting. Although this decreased the scope of the study, it allowed for a more thorough analysis of a brain region previously implicated in set-shifting abilities, and the Desikan parcellation method allowed for increased localization specificity. Previous functional neuroimaging studies have found that lateral frontal lobe areas are most vital to set shifting (Pa et al., 2010). This study analyzed six lateral frontal lobe areas and two orbital frontal lobe areas. Orbital regions of the frontal lobe have not been found to correlate with set-shifting ability and thus served as negative controls in the set shifting correlation analyses (Bissonette et al., 2013).
GWC was used to measure the structural integrity of the PFC regions, as it is a marker of cortical development and myelin density. Interruptions during normal brain development can cause neurons to get stuck in the white matter during neuronal migration, resulting in increased GWC. Blurring of gray and white matter in certain brain regions has been correlated with decreased performance on neuropsychological tests of cognitive performance, such as the Wechsler Adult Intelligence Scale (WAIS), Boston Naming Test (BNT) and Controlled Oral Word Association (FAS and CFL) (Blackmon et al., 2011). Moreover, correlations between PFC structures and set shifting have been found in certain animals such as monkeys, rats, and mice (Bissonette et al., 2013). Expanding upon these works, this study demonstrates how set shifting is associated with GWC in different PFC regions in healthy adults.
Both the set-shifting neuropsychological tests used, TMT-B and WCST-PE, require attention, working memory, visual search and executive-functioning abilities to varying extents (Fujiki et al., 2013). Higher TMT-B scores reflect difficulty in switching mental sets between sequencing numbers and letters, whereas higher WCST-PE scores reflect difficulty in relinquishing a previously established rule set that is no longer successful (Pa et al., 2010). Participants who had greater GWC in their left and right superior frontal gyri, left and right middle frontal gyri (dorsolateral PFC) and left ventrolateral PFC all displayed increased performance on either TMT-B or WCST-PE.

Left and Right Hemisphere Superior Frontal Gyri GWC Associations with TMT-B and WCST-PE Performance

Preexisting studies have tied the left superior frontal region to a different executive function, working memory (du Boisgueheneuc et al., 2006), and the right superior frontal region to self-focused reappraisal abilities (Falquez et al., 2014). The positive correlation between the participants’ GWC values of both the left and right superior frontal gyri to both TMT-B and WCST-PE scores indicate that these regions are essential to set shifting.

Left (Broca’s) and Right Ventrolateral PFC GWC Associations with TMT-B and WCST-PE Performance

The left ventrolateral PFC is part of Broca’s area, which normally associated with speech production. Studies have shown that the left ventrolateral PFC is also essential to working memory (Thothathiri, Schwartz, & Thompson-Schill, 2010). The right ventrolateral PFC, on the other hand, is associated with motor inhibition (Levy, & Wagner, 2011) but no other executive functions. This previous research is consistent with findings from the current study: the left ventrolateral PFC was correlated with both TMT-B and WCST-PE, whereas the right ventrolateral PFC was correlated with neither.

Left and Right Middle Frontal Gyri (Dorsolateral PFC) GWC Associations with TMT-B and WCST-PE Performance

The right middle frontal gyrus has been associated with reorienting attention from exogenous to endogenous attentional control (Japee, Holiday, Satyshur, Mukai, & Ungerleider, 2015), whereas the role of the left middle frontal gyrus is largely unknown. The mid frontal region in this study had split results with each hemisphere’s GWC correlating with only one of the two tests. The left middle frontal region had a significant association with WCST-PE, while the right middle frontal region had a significant association with TMT-B. The right middle frontal gyrus’ significant correlation with TMT-B might signify that the right middle frontal gyrus controls functions that TMT-B specifically tests, such as visual attention and graphomotor control. In contrast, the left middle frontal gyrus was significantly correlated with WCST-PE, which suggests that this brain region controls functions that the WCST-PE tests, such as cognitive response inhibition and generation of novel problem-solving strategies.

Left and Right Orbitofrontal Cortex (OFC) GWC Associations with TMT-B and WCST-PE Performance

The OFC is known to be essential in processing rewards and punishments (Kringelbach, 2004). The OFC contains the secondary taste cortex, secondary olfactory receptors and tertiary olfactory receptors (Rolls, 2004). No previous studies have tied the orbitofrontal cortex to set shifting, and as expected there were no correlations between the left or right OFC with either test.

PFC GWC Associations with the BNT

The Boston Naming Test was used to establish discriminant validity by demonstrating that GWC in prefrontal regions is correlated with cognitive set shifting specifically and not cognitive functioning in general. The fact that the two cognitive set shifting measures were not correlated with the BNT provides support that the BNT is an independent measure of cognitive function unrelated to cognitive set shifting. Prior studies have demonstrated that intact performance on the BNT requires temporal lobe integrity, rather than frontal lobe (Loring et al., 2008). Results from current study confirmed this as no PFC regions correlate with the BNT. It is unclear whether GWC in temporal lobe regions is correlated with BNT scores; however, this would be a valuable hypothesis to test in future studies. Most regions tested in this study have no correlation with speech production or speech comprehension except for the left superior frontal gyrus, and, as expected, GWC in these areas did not correlate with the BNT. Even the left superior frontal gyrus did not correlate with the BNT, which supports the theories that slow deterioration of Broca’s area can trigger compensatory mechanisms from surrounding areas (Plaza, Gatignol, Leroy, & Duffau, 2009). Since GWC is not an abrupt occurrence, neural plasticity is able to compensate for the deterioration of function.

Neuropsychology Test Associations (TMT-B, WCST-PE, BNT)

Since both TMT-B and WCST-PE test for set shifting, their correlation was highly significant as expected. However, the difference between the tests could be observed in the mid frontal gyrus. The two tests assessed different cognitive functions in addition to set-shifting ability. Using cards with pictures of various colors and shapes, WCST-PE tested response inhibition and novel problem solving as the participant had to inhibit a prior response pattern that was no longer successful and use trial and error to solve for the hidden rule. TMT-B, on the other hand, evaluated sequencing and visual attention when the participant had to connect dots in alphanumerical order. Both TMT-B and WCST-PE did not correlate significantly with BNT since BNT measures confrontational word retrieval whereas both TMT-B and WCST-PE focus mainly on executive functioning.
Although this study was conducted methodically with the use of the same scanner, processing steps and neuropsychological tests in each participant, the research could have been improved in several ways. One way would be to consider measures of frontal white matter integrity such as those acquired from diffusion tensor imaging. Numerous studies have shown the impact of white-matter hyperintensities and compromised fiber tracts on impaired set-shifting performance among other executive function abilities (Perry et al., 2009). This study was conducted in 61 participants, a population which could have possibly restricted the generalizability of the findings to patient populations and limited the amount of variance in the data, so further studies may benefit from a larger sample size. Also, there was a wide range of ages among the participants, which should be restricted in future studies.
The next steps would be to develop treatment options for people with executive dysfunctions. Currently, there are no specific medications that help people struggling with these problems. As cognitive set-shifting deficits may be related to certain behaviors of those diagnosed with autism spectrum and individuals diagnosed with ADHD, this research could be essential in developing treatments for these conditions. Future directions for similar research need to focus on whether white-matter integrity in the PFC subregions show a similar pattern of results in repeated trials. Moreover, this study tested not only set-shifting ability, but also other skills sets such as sequencing and working memory. Controlling for component processes to isolate set-shifting ability could have led to confounding interactions between component processes (Pa et al., 2010). In addition, instead of using a test to assess set-shifting ability along with component processes simultaneously, an alternative test, the Design Fluency (DF) test, could be used to assess solely set-shifting abilities. In the Design Fluency test, participants first connect filled dots while avoiding the empty dots and then connect the empty dots while avoiding the filled dots. The test breaks down into three different criteria: elimination of extraneous component processes, generation of original ideas and provision for the allowance of the participant to focus on set shifting. Future tests should determine whether this test is correlated with TMT-B and WCST-PE and show a similar pattern of correlations with GWC in PFC subregions.

Conclusion

In sum, this study demonstrates a positive correlation between cognitive set-shifting ability and GWC in specific parts of the PFC. This investigation tested eight different PFC regions that previous studies have marked as controls of the set-shifting abilities, or that can be useful in verifying some hypotheses which were made prior to this study. Our experimental method–using WCST-PE, TMT-B and BNT–showed that GWC in circumscribed regions of the PFC correlated with WCST-PE and TMT-B. WCST-PE correlated with GWC values in the left ventrolateral gyrus (Broca’s), the left and right superior frontal gyrus, and the left mid frontal gyrus. TMT-B correlated with the left ventrolateral gyrus (Broca’s), the left and right superior frontal gyrus, and the right mid frontal gyrus. The right ventrolateral gyrus (Broca’s) and the left and right OFC were not correlated with any of the tests. This study will provide opportunities for future research to target the specific areas of the prefrontal cortex that are inhibited in people who struggle with set shifting. As a result of these findings, potential treatments can be designed to aid individuals with disorders such as autism, Down’s syndrome and ADHD that impair set-shifting abilities.

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Therapeutic Potential of Optogenetic Treatment for Individuals with Multiple Sclerosis

doi:10.22186/jyi.33.4.77-82

Abstract | Introduction | Future Directions and Conclusion | References | PDF

Full Issue: September 2017 Special Edition

Abstract

Multiple Sclerosis (MS) is a chronic neuroautoimmune condition characterized by neurodegeneration and demyelination throughout the central nervous system. While the pathology of MS is largely unknown, its symptoms are well defined. Current MS therapies such as intravenous corticoid injection, disease modifying treatments (DMTs) and neuro-rehabilitation exist; however most are ineffective as they do not manage symptoms efficeiently, leading to many adverse side effects. Optogenetic stimulation of demyelinated regions may serve as the needed therapy to effectively treat symptoms given the advances achieved in its rapid mechanisms and accurate cell-type-specific delivery strategies. In fact, the hallmark of optogenetic technology is the fast and accurate activation of specific neurons. Current evidence supports optogenetics as a means of controlling or enhancing neural circuitry involved in specific symptoms. This is done by targeting specific cells implicated in their respective neural circuits and activating them, or activating interneurons that inhibit the target pathway. Moreover, continuous photostimulation has been found to strengthen neuronal circuitry by promoting long-term potentiation (LTP). This review analyzes several studies that utilize optogenetics to alleviate MS-related symptoms such as cognitive impairment, visual impairment, bladder/bowel dysfunction, and tremors by controlling their specific pathways. It will also assess how these studies may translate to MS patients. Possible challenges in creating such a treatment will also be discussed. Given literature on the application of optogenetic treatment in neurodegenrative models is limited, this review presents a theoretical means of creating optogenetics treatment for MS and other neurodegenerative disorders.

Introduction

Multiple Sclerosis (MS) is a chronic autoimmune disease that leads to focal and diffuse neurodegenration and myelination throughout the nervous system (Kolasinski et al., 2012; Siffrin, Vogt, Radbruch, Nitsch, & Zipp, 2010). In its most common form, relapse-remitting MS, it is characterized by high inflammation levels that lead to a continuous cycle of relapse and remission (Raffel, Wakerley, & Nicholas, 2016). These relapses, called exacerbations, may come in the form of new or worsening of old symptoms that are largely neurological such as visual impairment and imbalance that worsen over days or weeks, then recover spontaneously (Wingerchuk et al., 2014). Other common symptoms are cognitive impairment, loss of bladder control, leg weakness and sensory symptoms (Raffel et al., 2016).
Genetic and environmental factors both have a role in MS development; however, a specific link to the disease has not been found (Harbo, Gold, & Tintoré, 2013). Genetically, MS is best characterized by a mutation on the human leukocyte antigen (HLA) gene locus, which causes abnormal antigen recognition of T cells leading to attacks on myelin proteins (Raffel et al., 2016). These findings have not been conclusive, as many other genes involved in immunological roles have also been found to play a role in contributing to MS. Environmental risk factors include smoking ,sunlight exposure, and vitamin D deficiency (Raffel et al., 2016).
Currently, there are no treatments that cure MS (Ziemssen et al., 2016). Instead, treatments target symptom management to increase patients’ quality of life. These include high doses of corticosteroids such as methylprednisolone (Jongen et al., 2016), Disease modifying Treatments (DMTs) such as interferon β-1a, interferon β-1b, alemtuzumab, fingolimod and natalizumab (Carrithers et al., 2014; Gajofatto & Benedetti, 2015), and neuro-rehabilitation (Dasari, Wootla, Warrington, & Rodriguez, 2016). All of these treatment options have adverse effects or are not particularly effective in the long term (Jongen et al., 2016; Ontaneda, Fox, & Chataway, 2015; Schäcke, Döcke, & Asadullah, 2002; Ziemssen et al., 2016). There is a considerable need for new treatment options that are more effective, while reducing the adverse side-effects. A potential therapy for MS-affected individuals may be the therapeutic application of optogenetics.
Optogenetics is a novel method that utilizes photoreceptors to selectively activate neurons (Hegemann & Nagel, 2013). The genetic code of these receptors is delivered either virally or non-virally to be expressed on the cells of interest. Once expressed, light is shone directly on these cells through an optic fiber inserted into the brain or spinal cord to be activated. Advances in this technology allow the photoreceptors to be selectively expressed on specific cell types, and in turn enable the control of specific neural pathways. Although largely used in research applications (Namboodiri & Stuber, 2016), literature describing the therapeutic potential in neurodegenerative diseases is lacking. More recently, there has been a spike in literature demonstrating the therapeutic applications of optogenetics in the context of various disorders and symptoms. For example, evidence has shown optogenetic stimulation enhances cognitive function (Goshen, 2014), visual ability (Jazayeri & Remington, 2016), bladder/bowel dysfunction (Stamp et al., 2017), and tremors (Tønnesen, 2013) through neuromodulatory effects.
While many of these studies have been conducted on specific symptoms, the focus of the optogenetic treatment has not been on any neurodegenerative models that express similar symptomology. Indeed, the current literature on the topic does not present any research on the translation of optogenetic treatment into any neurogenerative models expressing similar symptomology, requiring further research in this field (Ahmad, Ashraf, & Komai, 2015; Bordia, Perez, Heiss, Zhang, & Quik, 2016; Bryson, Machado, Lieberam, & Greensmith, 2016; Jazayeri & Remington, 2016). This review paper will assess the potential use of optogenetics in the development of therapies for MS related symptoms such as cognitive impairment, visual impairment, bladder/bowel dysfunction, and tremors.

Development of Optogenetic Mechanisms

Initial studies investigating the use of photoreceptors invovled metabotropic photoreceptors of vertebrate and invertebrate eyes (Zemelman, Lee, Ng, & Miesenbö Ck, 2002). However, these systems were too complex to manipulate and the delay between light exposure and action potential firing was highly variable ranging from a few hundred milliseconds to tens of seconds (Lyon, 2013). Focus shifted to ionotropic microbial opsins as they exhibit fast, direct light-dependent ion conduction across the cell membrane (Mudiayi, Wong, & Gruber, 2015). Furthermore, microbial opsins allow reversible control of neurons on the timescale of individual action potentials, which was lacking in earlier methods (Boyden Zhang, Bamberg, Nagel, & Deisseroth, 2005). From a therapeutic perspective, however, the difference in seconds and milliseconds between the speed of activation of these cells is not significant. The rapid control of these cells does not necessarily affect the overall efficiency of the treatment. It is critical to understand that most optogenetic developments were directed as a means to enhance research tools. For example, creating faster optical controls allow remote control of individual spikes or synaptic events and enabling genetically targeted photostimulation with finer temporal resolution (Boyden et al., 2005). While the increased control speed is welcomed, it holds no major significance other than that symptoms would be halted seconds earlier. However, as it stands today, most studies use the microbial channelrhodopsin-2 (Nagel et al., 2003), warranting focus on this protein as a primary candidate for developing an optogenetic treatment.
ChR2 is isolated from the genome of the single celled algae Chlamydomonas reinhardtii (Nagel et al., 2003). ChR2 application was further developed by inserting the protein via viral vectors into mammalian hippocampal (HPC) neurons (Boyden et al., 2005). Once imbedded, high-speed optical switching photostimulates neurons, impressively responding in one to two milliseconds. Furthermore, neural activity was controlled by simply switching the optical blue light on or off (Boyden et al., 2005). This control can also be used by activating inhibitory circuitries, creating an antagonist effect on the region or function of interest. Molecularly, once the optical light is shone on the brain region of interest, photostimulation increased ion transport across the cellular membrane by either opening an ion channel or by actively pumping ions (Mudiayi et al., 2015).

Current Delivery Strategies and Therapeutic Obstacles

Non-viral delivery methods of expressing ChR2 in cells include in utero electroporation, transgenic models, chemical lipofection, and laser-assisted cellular poration (Boyden et al., 2005; Carter & de Lecea, 2011; Mohanty & Lakshminarayananan, 2015). Although beneficial for research, these methods are not viable translational strategies for human treatment. Moreover, these methods possess limitations such as not being able to specifically target cells, posing a risk to cellular components or the foreign DNA, and association with axonal pathology (Bryson et al., 2016; Mohanty & Lakshminarayananan, 2015). Due to the lack in cell specific targeting and the lack of lateralization to humans of non-viral methods, it seems viral delivery methods provide the most sensible means of creating an optogenetic treatment, especially when studied in non-human primate models.
The most common method of expressing ChR2 in a nervous system is to infect neurons with a deficient virus replication, typically an adeno-associated virus (AAV) or lentivirus (LV), containing the transgene of interest driven by a short promoter or enhancer element (Carter & de Lecea, 2011). AAVs are small viruses that efficiently transduce neurons while inducing minimal immune responses in the host brain (Blits et al., 2010). LV vectors are derived from a genus of retroviruses that cause chronic diseases characterized by long incubation periods such as the human immunodeficiency virus (HIV; Dull et al., 1998). In both methods, once ChR2 is expressed in the region of interest, illuminating the neurons with blue light at a bandwidth of 450–490 nm induces rapid depolarizing currents. However, literature has shown a difference in efficacy when used in non-human primates. One study has shown transduction with AAV yields positive functional and behavioral results, but not LV, indicating that AAV may be a more effective viral delivery method in primates compared to LV (Gerits et al., 2012). Moreover, in a recent breakthrough study, successful cell-type-specific expression of ChR2 in midbrain dopamine neurons of wild-type Rhesus macaques utilized AAVs, not LVs (Stauffer et al., 2016). A vector delivering Cre recombinase under the control of a tyrosine hydroxylase (TH) promoter fragment and a vector delivering a Cre-recombinase-dependent ChR2 were mixed and injected to attain cell-type-specific expression of ChR2 (Stauffer et al., 2016). The TH promoter can be substituted in the first vector to other neuron-subtype-specific promoters to optogenetically control other neuron types in a monkey brain. For example, in an MS patient with lesions in the spinal cord affecting γ-aminobutyric acid (GABA) neurons, the Cre-recombinase vector being develived would require a glutamate decarboxylase (GAD) promoter fragment. This is because GAD is the enzyme that catalyzes the decarboxylation of glutamate to GABA and is only found in GABAergic cells, ensuring ChR2 expression is limited these cells. Further applications of this technique can be found in treatment of various MS symptoms utilizing the specific pathways and neural circuitry they operate through.

Evidence of Therapeutic Potential for Multiple Sclerosis Related Symptoms

Photostimulation may serve as a factor in dealing with MS symptoms such as cognitive impairment (Goshen, 2014), visual impairment (Jazayeri & Remington, 2016), bladder/bowel dysfunction (Stamp et al., 2017), and tremors (Tønnesen, 2013) by controlling their specific pathways. This especially possible once coupled with stem cell therapy (Bryson et al., 2016). However, while optogenetics may treat these symptoms, this review does not intend to demonstrate the effect of photostimulation on the autoimmune function of the disorder. To our knowledge, literature documenting immunomodulation using optogenetics is lacking, and what has been published only discusses proof-of-concept and designs for future development (Tan, He, Han, & Zhou, 2017). The approaches involve optogenetic control of immune responses with novel tools that modulate lymphocyte trafficking, inflammasome activation, dendritic cell (DC) maturation, and antitumor immunity (Tan et al., 2017). Further information on the theoretical methods involving the combination of optogenetics and immunoengineering, termed optoimmunoengineering, can be found in the review conducted by Tan et al. (2017)
The most common cognitive impairment seen in MS is visual learning and memory (Chiaravalloti & Deluca, 2008). Evidence has shown the problem lies in the initial learning of the memories as memory recall in MS patients is equal to healthy individuals, indicating that long-term memory systems are relatively intact (Chiaravalloti & Deluca, 2008). The theoretical construct suggested to treat cognitive impairment in MS patients is to utilize induced pluripotent stem cells to derive oligodendrocyte progenitor cells and mature oligodendrocytes for remyelination of regions displaying degeneration that process working memory. While theroretically, stem cell transplantation should be able to resolve the issue with neuronal loss, clinical trials are showing unsuccessful results that are not entirely understood (Zhang et al., 2011). Recent studies suggest there is limited success due to the complexity involved with degrading glial scarring (Mallory, Grahn, Hachmann, Lujan, & Lee, 2015). It is hypothesized phototimulation of oligodendrocytes after differentiation would strengthen the remylination process as well as neural circuitry within that brain region through long-term potentiation (Lignani et al., 2013; Takeuchi et al., 2016). Takeuchi et al. (2016) were able to demonstrate optogenetic stimulation of the locus coeruleus (LC) enhances consolidation of everyday memory. The study electrophysiologically recorded LC firing rates in novel enivronments and stimuli and recreated this effect with photostimulation alone. Moreover, LTP was observed with repeated stimulation. This demonstration of LTP due to optogenetics is significant in providing contact for how optogenetics may affect neural circuitry with repeated stimulation. In this function and in the rest of the symptoms that will be discussed, we hypothesize that not only will optogenetics control the symptom by decreasing or inhibiting its presence, photostimulation may also lead to enhanced management of symptoms without the need for simulation in the long term.
Visual impairment is often seen in MS patients, commonly manifesting as optic neuritis, which is an acute inflammatory disorder of the optic nerve typically presenting with sudden monocular visual loss and eye pain (Garcia-Martin et al., 2017). Macaques were used to study visual information processing mechanisms in the lateral geniculate nucleus (LGN) and primary visual cortex (V1) by administering an AAV with an effective CamKII promoter into koniocellular cells (K-cells) at the LGN (Klein et al., 2016). The LGN is made up of K-cells, parvo cells and magno cells, each distinct in their circuitry, function and biochemistry, despite all passing through the V1. K-cells however were used as they are especially different form the other two cells. The vectors used were able to target K-cells, nearby CamKII-positive cells, as well as transduce distant layer 6 pyramidal cells of V1 and retinal ganglion cells (Klein et al., 2016). Measurements were conducted using average local field potential (LFP) responses across stimulation trials and current source-density (CSD) profiles were calculated for the visual flicker and optogenetic conditions to assess V1 laminar activation. Of the total population of LGN neurons recorded, the authors identified 23% as being directly affected by the optogenetic stimulation, in comparison to the ~10% observed in the literature (Klein et al., 2016). Although less than 50% of the cells were activated, the authors were able to confirm that at the neuronal circuit level, the amount of selectively recruited K-cells was sufficient to drive short-latency activity in the supra-granular layers of downstream area V1 (Klein et al., 2016).
In translating these findings for an MS treatment, a more enhanced outcome may be observed if all cells of LGN are recuited. This is especially since this study only sought to assess the ability of optogenetic stimulation in the visual cortex to understand the visual pathway, not with the intention of developing a therapy for visual impairment. No mention was made on the level of enhancement observed in the visual ability of the monkeys, warranting further studies on optogenetic stimulation of this brain region. Moreover, an optogenetic treatement for this function coupled with stem cell therapy may provide a means to alter visual impairment in MS. Similar to the function described in treating cognitive impairment, photostimulation of these pathways may treat visual impairment by strengthening and expanding the neural circuitry and remyelination processes. Interestingly, clinical trials are being conducted for a fascinating treatment option for retinitis pigmentosa (RP) using ChR2 and the concept of optogenetic stimulation (Birch, 2016).
Bladder and bowel dysfunctions are commonly seen in MS cases, causing some of the most distressing symptoms with as many as 75% MS of patients presenting the symptom (Andretta, Simeone, Ostardo, Pastorello, & Zuliani, 2014). The most frequent bladder symptoms seen are storage symptoms such as urinary frequency, urgency, and urge incontinence (Andretta et al., 2014). Voiding symptoms such as hesitancy, incomplete voiding and urinary retention are present as well, although to a lesser extent (Andretta et al., 2014).
Enteric neural cells from fetal or postnatal mouse bowels expressing ChR2 were transplanted into the distal colon of 3-4 week old wild-type mice (Stamp et al., 2017). The transplanted neural cells were able to differentiate into multiple functional types of neurons, integrating and providing functional innervation of the smooth muscle of the bowel wall (Stamp et al., 2017). In the study, optogenetics was used to to selectively stimulate graft-dervied neurons to identify that enteric neural cells isolated from the embryonic and postnatal bowels, giving rise to functional inhibitory motor neurons, excitatory motor neurons, and interneurons following transplantation into the distal colon of recipient mice (Stamp et al., 2017). While optogenetic stimulation was not the primary cause of the functional recovery, when used as a tool, photostimulation can control how these new cells operate. In a disorder that leads to a loss of function due to continuous degeneration, optogenetic stimulation can speed the recovery period as cells are continuously destroyed. Moreover, we hypothesize continuous photostimulation will lead to LTP and strengthening of these networks faster than without the stimulation. These findings are significant as they illuminate a fundamental limitation in almost all the current gold-standard nerve-targeted treatment approaches which do not specifically address isolated neural circuits and lead to undesirable side effects such as unwanted bowel movements or sexual function (Park et al., 2017).
Tremors are believed to occur in up to 75% of MS patients appearing in various forms such as postural, kinetic, proximal, distal tremors, and internal tremors (IT; Ayache et al., 2015). Dysfunction in inhibitory cerebellar efferent projections likely play a role in the generation of tremors during posture or movement in MS patients, however, additional lesions of other cerebral pathways might be involved (Ayache et al., 2015). Optogenetic treatment for tremors would function similarly to those documented in Parkinson’s disease (PD). Cell-type specific targeting of dopaminergic neurons in the substantia nigra pars compacta (SNc) has shown to be a viable means of treating denervation of striatal target areas (Stauffer et al., 2016; Tønnesen, 2013). This process is thoroughly described in the study by Stauffer et al. (2016). Applying photostimulation to the regions found to underlie tremor development and motor dysfunction in MS, such as gait, would be a feasible means of treating these symptoms, given its documented applications in similar cases (Kravitz et al., 2010).

Future Directions and Conclusion

Optogenetics has been reported to be a revolutionary technique in neurobiology research. As such, the objective of this review is to assess the potential use of optogenetics in developing therapies for MS related symptoms that include cognitive impairment, visual impairment, bladder/bowel dysfunction, and tremors. Studies for each of these symptoms have been discussed and analyzed and two primary conclusions have been found. Intially, optogenetics remains a tool to enhance treatment. For example, in the study by Stamp et al. (2017), the primary function of the study was to understand how implanted enteric cells functionally integrate into endogenous cells. Optogenetics was merely a tool to control this function to further study it. While it was not used as a treatment, this function of controlling the circuitry may be manipulated into a therapy for those with bladder dysfunction and other illnesses observed in MS.
Secondly, while this control does require manually intiating photostimulation, continuous stimulation may lead to enhancement in circuitry through neuroplastic and LTP effects  as seen in Takeuchi et al. (2016). We hypothesize the manual function of turning on photostimulation in the brain may work similarly to a pacemaker where optic fibers are connected internally in the body. Of course these concepts remain theoretical and require extensive research to validate this possibility.
Ultimately, furthering this therapeutic tool is limited by the knowledge present for specific symptoms as well as their mechanisms (Bryson et al., 2016; Jazayeri & Remington, 2016; Mohanty & Lakshminarayananan, 2015; Mudiayi et al., 2015). Just as optogenetics may be used as a therapeutic tool, it can be utilized to understand these symptoms as it is currently used as an investigative tool (Carter & de Lecea, 2011; Mohanty & Lakshminarayananan, 2015; Mudiayi et al., 2015). A first step to identifying how photostimulation can lead to therapeutic effects could ultimately be using optogenetics to understand individual symptoms by experimenting with its circuitry and inhibiting or activating symptom pathways. Once this knowledge has been attained, more specific and accurate targets can be found to control MS symptomology.

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Application of Neuroscience Principles for Evidence-based Design in Architectural Education

doi:10.22186/jyi.33.4.71-76

Abstract | Introduction | Evidence-Based DesignConclusions | References | PDF

Full Issue: September 2017 Special Edition

Abstract

We spend approximately 90% of our time within a built environment, whether it is in our homes, offices, schools, city parks, or public spaces. This bears significance, as we are equally shaped by both our genetic makeup as well as our environment, which brings into question of how we experience space, and in turn how these experiences impact our behaviour. To gain a greater understanding of these impacts, neuroscience seeks to root out the principles of biological mechanisms involved in consciousness, spatial navigation and environmental stressors. However, the use of these principles is not discussed extensively within the curriculum of undergraduate or graduate architecture programs in North America. This paper aims to highlight that such information is critical to the advancement of evidence-based design by acknowledging the role of conscious awareness within space. An observable shift in the design community in collaborative neuroscience research can be seen in multi-sensory and virtual reality labs being constructed into design firms within North America. This review stipulates that architecture students should be prepared for the changes in quantifiable research and perceptual data collection coming to their field by examining the importance of neuroscience research in perception and consciousness. Architecture students who can interpret scientific research will not only have the advantage of a greater understanding of the human condition within space, but they will also be able to evolve the standard of design.

Introduction

Architects have long sought to inspire creativity, ingenuity, worship, community and awe using the tools at their disposal. Homo faber, “Man the Maker”, crafts his environment, thereby controlling his fate. As a result of human ingenuity, we now spend over 90% of our time within a built environment crafted to suit our needs (Janda & Janda, 2017). Design is inspired by societal reform and scientific exploration expressed as an art form in itself. If architecture is an expression of creativity as a mean to reflect on the human condition, one might argue that such a reflection can also be found within neuroscientific exploration of the mind. As we come to understand the biological mechanisms of perception, consciousness and their residual impacts on mental and physical health, there is question of how our environment might in turn affect those mechanisms.
Perception of space relies upon conscious awareness: the ability to receive and comprehend exterior and interior stimuli through the use of the Global Workspace Theory. A good example of the interjection of neuroscience and architecture can be found in spatial navigation research. Scientific authors are capable of identifying floor patterns that are most and least useful to way-finding. Studies have also found that computer game and virtual reality architects may play an integral role in retaining memory and attention in elderly populations (Optale et al., 2010). Architectural students can benefit from a greater understanding of the impact of environmental stressors on biological mechanisms. Chronic stress response is one of the most pressing design problems as it may increase the risk of psychobiological disorders such as immune deficiencies, irritable bowel syndrome, depression, and anxiety (Hammen, 2015).
Neuroscience research permits an objective review of the usability and mental health impacts of space. “An informed architect could use this research as a means for evidence-based design (EBD), a concept which seeks to ratify design standards of the built environment by incorporating research from multiple disciplines into the design process.” However, of the 113 post-secondary architecture institutions in North America, only the New School of San Diego offers students a certification program, which applies neuroscience principles to evidence-based practice (“Certificate in Neuroscience for Architecture” 2017). Although EBD is currently taught in many programs geared towards renewable/sustainable/green buildings, a truly multidisciplinary approach to EBD involves neuroscientific, psychological and economic research to guide design, a method commonly used in healthcare facilities today (Ulrich, Zimring, & Zhu, 2008).
Students in architecture are entering a field which is now exploring ways to make use of neurobiological data analysis involving environmental stimuli to achieve EBD. This review stipulates that architecture students will benefit from a greater understanding of conscious perception towards health-centric design. This will also allow them to collaborate with multisensory and virtual reality labs along with other cutting edge design firms and academic institutions merging neuroscience research into design.

Neurological Influences of the Environment on Health

Consciousness is perhaps the most important existential question that has yet to be solved. It is described in a number of ways in relation to philosophical, psychological, and neuroscientific interpretation. It is broadly defined as “the mind”, the perceptual awareness of external and internal stimuli, which influence cognitive activity (Searle, 2000). Consciousness is often considered separate from other neurological mechanisms that humans share due to its qualitative and subjective nature. The majority of consciousness research has been conducted on the verbal and behavioural assessment of participants. These findings can be questionable as we all experience our environment, our personalities, and our memories differently thereby increasing the chance of error in experimental findings. However, consciousness is an entirely biological phenomenon and subjective ontology challenges, but does not prevent an objective scientific research. The breadth of this field touches upon multisensory interpretation, memory recall, attention and various cognitive mechanisms; however, this section will focus on the conscious effects of our environment on way-finding and stress.
Through the use of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), neuroscientists attempt to identify the neuronal pathways, which produce the conscious experience. The Global Workspace Theory (GWT) is widely accepted in the scientific community to be representative of conscious and unconscious processes (Prakash et al., 2008). It is similar to the concept of working memory in that GWT proposes experience to be momentary and subjective. Multisensory stimuli (conscious) are initially interpreted by various cognitive processes (unconscious), which is referred to as the “receiving process”. This information can then be used to produce a movement, emotion, or behaviour. GWT lends itself quite readily to computational modeling and can distinguish the brain regions impacted by competition of sensory modalities (e.g., a video and its audio being out of sync).

Way-Finding and Spatial Awareness

There are a multitude of research initiatives involving the brain and behaviour within the environment. Way-finding will be used as a practical example to showcase the power of design as a psychobiological influencer on behaviour and health.Way-finding is the neurophysiological experience of self while navigating the environment. This biological mechanism allows us to locate ourselves within space by taking in information from visual and auditory cues while simultaneously utilizing stored spatial memories. The environmental cues paired with spatial memory then allow for a decision to be made via limb movement and body axis direction (Macagno, 2014). Way-finding is a particularly relevant tool in design as spatial alignment efficiency can either aid navigation or cause confusion and unease within the built environment.
Way-finding can be categorized by the activation of neuronal pathways that create a cognitive mapping system. Nobel Prize laureate John O’Keefe and Lynn Nadel researched hippocampal cell signalling in rats. They found that specific groups of neurons, termed “place cells”, fired when rats either tasked with location recall or object recall (O’Keefe & Nadel, 1978). The authors stipulated that place representations within the hippocampus were activated together depending on the physical and perceived distances between places. Later on these findings formed the basis of the theory of “grid cells”, place-modulated cells in the presubiculum and hippocampus which fire in a crystal-like fashion in conjunction with head-direction neurons (Boccara et al., 2010). The discovery of hippocampal involvement of place cells and grid cells has offered a fascinating insight on the way we understand geometric boundaries, spatial memory and directional movement. These findings may be especially significant to architects when considering the effects of floor misalignment on the perception of our environment. Designers may also take advantage of research involving the deterioration of the aging hippocampus which impacts spatial memory in the geriatric population.
One of the ways that a building is considered a success is in the functional ease of navigation. There is a positive correlation between the perceived figural complexity of space and how the actual space reflects that perception (Weisman, 1981). If misaligned, the spatial structure of the built environment can be known to cause cognitive dissonance with way-finding. A study performed in 2004 by Werner and Schindler studied this effect via the use of a computer program simulating various aligned, misaligned, connected and disconnected floor plans (Figure 1).

NP Figure 1

Figure 1. 16 floor plans, the top two rows are connected (at 0° and 45°), the two rows at the bottom are disconnected floor plans (at 0° and 45°) (Werner & Schindler, 2004). Findings suggest that aligned floorplans reduce effort to establish spatial navigation.

Fifty-six participants familiarized themselves with a digital interface and an assigned floor plan. They were then instructed to find five target objects within the span of five minutes (Werner & Schindler, 2004). The authors found that misalignment to a central reference frame reduced the speed of accuracy in finding objects by 25% as compared to aligned floor plans. When participants were asked to point in the direction they believed an object might be according to a specific floor plan their pointing error was on average greater for misaligned than aligned floor plans (Werner & Schindler, 2004). These findings represent an exciting example of how neuroscience can interject itself into architectural education. The orientation of a floor plan can directly impact the usability and positive experience within space, this information can help disseminate patterns of design that are more efficient, thereby reducing spatial dissonance.
Finding our position within space is directly impacted by our ability to recall the layout and landmarks of our environment, and this ability deteriorates significantly with age. From approximately 50 years onwards, MRI scans reveal a decline in white matter volume, reaching up to 26% in reduction at 90 years old (Gunning-Dixon, Brickman, Cheng, & Alexopoulos, 2009). This cell loss is detrimental to hippocampal functioning, the principle region responsible for spatial memory in regards to navigation and orientation. However, research suggests that enriched environments have positive neurological benefits for the geriatric community. Researchers took a novel approach of using virtual reality (VR) to determine the possible effects on cognition and memory (Optale et al., 2010). The participants, a median age of 80 years old, were placed in either a VR memory training experimental group or control group for 6 months. The VR training involved simulated visuo-auditory environments and focused on way-finding scenes. The authors found that participants treated with VR memory training had better long term memory and cognitive functioning than the control group which they theorized may have been caused by a boost in attention capabilities. Way-finding and memory training using virtual reality environments designed using perception based learning programs can be used to reinvigorate spatial memory recall.
How we design our built environment influences the ways that we behave within that environment. There should be an educational focus on tested principles in way-finding to promote evidence-based design. Using research, we can disseminate the floor plans that generate the greatest positive response to navigation within space. When considering aging populations architects can assist virtual reality programmers in creating enriched environments that can improve spatial memory.

Impact of Environmental Stressors on Health

The stressors within our environment can shape our health. This is particularly true of how chronic stress can cause immune deficiencies and increase susceptibility to psychobiological disorders. The hypothalamic-pituitary-adrenal (HPA) axis is the pathway which mediates stress response. As a survival tactic, HPA axis activation causes physiological changes such as disruption of the digestive system, vasodilation and the release of adrenaline. The HPA axis secretes glucocorticoid cortisol, an anti-inflammatory response hormone, to suppress inflammatory cytokine production. This can be accomplished by inhibiting pro-inflammatory gene promoters, blocking in cell cascades effects, and antagonizing protein-protein interactions which mediate cytokine production (Slavich & Irwin, 2014). Under chronic stress circumstances we can observe a glucocorticoid resistance, as immune cells become less sensitive to anti-inflammatory mechanisms, causing an inability to properly regulate rising cytokine levels (Schleimer, 1993). Inflammation also causes an up regulation of enteroendocrine cells which produce serotonin, permeating the sympathetic response of the HPA axis (Spiller et al., 2000). Therefore, chronic stress can cause immune response irregularities, which can in turn increase susceptibility to common viral attacks and functional disorders, and diminish our capacity to mitigate future stress responses.
The immune system is the body’s first line of defense against viral, pathogenic and bacterial infections. This is accomplished by first detecting the agent, and then sending neural and endocrine signals to the brain. These signals up regulate the creation of inflammatory response cytokines IL-6, IL-1, and TNF-α to the affected area in an effort to contain the infection (Slavich & Irwin, 2014). Genetic predispositions towards inflammation and stress response inhibition combined with the impact of environmental stressors have been connected to an increased risk to functional disorders such as irritable bowel syndrome (IBS) (Drossman, Camilleri, Mayer, & Whitehead, 2002). It is estimated that approximately 10-25% of the population is affected at some point but only 30% of those suffering from IBS are likely to seek out treatment (Drossman et al., 2002). Overexposure of proinflammatory cytokines and cortisol may also increase susceptibility to heightened states of anxiety and depression. Furthermore, deregulation of the HPA axis can impact synaptic plasticity as well as dopaminergic and serotonin output within the striatal areas, the amygdala, and the hippocampus (Hammen, 2015).
Furthermore, maladaptive stress response can be transmitted through transgenerational epigenetic modification. In rodent and primate models, we can observe prevalence in genetic expression instigated by stress response across multiple generations (Franklin et al., 2010). Studies have shown that unpredictable home environments in rats can reduce 5HT1A receptor expression in the dorsal raphe nucleus in descendants (Franklin et al., 2010). This change in receptor expression is akin to the pathogenesis seen in antisocial behaviours and personality disorders (Gudsnuk & Champagne, 2012). Vulnerability to stress induced psychobiological disorders is perhaps the most urgent health contingency that neuroscience research can address in the greater development of evidence-based design architecture.

Evidence-Based Design

Evidence-based design (EBD) involves the use of clinical research in the design concept of the built environment to improve health, productivity, and economic outcomes. It is a relatively new approach as it prioritizes objective and quantifiable results. EBD utilizes mounting research from neuroscience, environmental psychology, architecture, and behavioural economics to produce a framework of desired outcomes from our buildings. This section will review the process of EBD in addition to the architecture curriculums integrating of neuroscience and architecture. Furthermore, there will be an observation of the benefit of EBD in the design of health care facilities.

EBD and Neuroscience

There are a multitude of comparisons in the way science and design curriculums measure feasibility of findings and outcomes. Scientific concepts must be grounded within specific methods intrinsic to their validity. Publication of findings is critical to the advancement of research as it allows for objective review. The architectural approach differs as the interpretation of design is often subjective when considering the cultural and artistic ramifications of a structural landmark. Design is often led by trend or form, a novel build is often praised for its avant-garde design and emphasis on function can be perceived as a detriment to creativity. However, neuroscience research involving perceptual stimuli and its impacts on behaviour and health can be used to improve the current practice. As in scientific exploration, architecture may see its greatest advancements once design research is integral from building conception to measured impact and publication of findings.
A truly progressive curriculum expands onto scientific dialogue, which seeks to validate how to best enhance the human experience and eliminate the designs that are not beneficial. Students must understand the components which are conducive to those human experiences in order to conduct an evidence-based practice of responsible design. One post-secondary curriculum which stands out amongst others in terms of neuroscience and architecture integration is the New School of Architecture in San Diego. This is the first educational institution in the world to offer a certificate program in Neuroscience for Architecture (“Certificate in Neuroscience for Architecture” 2017). The courses focus on four areas that involve evidence based design practice. Students learn about environmental psychology, which is the quantifiable relationship between environment and behaviour. There is also an overview of the neurological components responsible for sensory and cognitive responses, which permit human experience within space. Students have access to neuroscience seminars focus on how to best improve health care facilities, educational, spiritual, and corporate environments using neuroscience principles. The integration of these concepts is further solidified with studio time geared towards applying these principles towards the built environment.
This program was developed by fellow of AIA and founder of the Academy of Neuroscience for Architecture (ANFA) John Eberhard, along with Dr. Eve Edelstein, PhD in Neuroscience and MA in Architecture. Both have extensive backgrounds in research and practical application of neuroscience within the built environment. Eberhard is the author of such behavioural neuroscience and architecture books such as “Inquiry by Design”(with John Zeisel, 2006), “Architecture and the Brain” (2007), and “Brain Landscape” (2008). His involvement in promoting neuroscience based EBD led to the creation of ANFA, a nexus of both fields in collaboration and research. Dr. Edelstein is the world’s first PhD Neuroscientist with a master in Architecture. She has contributed to over 43 scientific papers involving the impact of the environment on the body and brain. As a faculty member of the New School, Dr. Edelstein is educating architecture students on the concept of the built environment as a psychobiological influencer on behaviour and health. She is also the founder of Innovative Design Science, which is a design firm that specializes in implementing neuroscience research, virtual reality mock-ups, on-site design studies, as well as pre- and post-occupancy evaluations of the build. Students in San Diego are privileged to take part in a new approach towards architectural education, as they will come to understand the benefits of neuroscience and EBD in creating better buildings for its occupants.

The Role of EBD in Healthcare

EBD is most commonly used in the design of healthcare facilities. This may be due to the higher risks associated with hospitals which demand informed design to minimize loss. The concept of EBD first emerged in the 1960s as American and British health care providers measured the impact of spatial alignment and way-finding of floor layouts on staff productivity (Clipson & Johnson, 1987). Today, its method has been widely adopted by health care providers across North America. Notably, the US military Health System has constructed over 70 hospitals totalling $6 billion dollars in construction (Ulrich, Zimring, & Zhu, 2008). EBD in healthcare focuses primarily on four components: mental health improvement for patients and staff, patient recovery, staff productivity, and the use of evidence-based metrics.
The Center for Health and Design has provided a universal list of guidelines to perform EBD in healthcare facilities. The first step involves a literature review of neurological, psychological, architectural and economic research in relation to the problems the project is attempting to solve. Financial operations also need to be considered in association to multi-year investment returns and cost-effectiveness of design options. SWOT analysis is used as a decision-making tool in the placement of technical and safety healthcare features. Furthermore, the design team is heavily involved with patients and staff in regards to surveys, simulations, and pre- and post-occupancy evaluations. The goal of this method is to acquire as much information as possible in regards to healing environments to guide the construction of the facility.
The impact of EBD in healthcare is that of a measureable improvement in health outcomes, which leads to a reduced chance of infection and medical error, thereby reducing the length and cost of a patient’s stay. Researchers performed a meta-analysis of healthcare facility layouts and patient recovery time (Ulrich et al., 2008). The findings suggested that single patient rooms reduced the chance of infection, allowed for better communication with staff, and length of stay (Ulrich et al., 2008). The Agency for Healthcare Research and Quality (AHRQ) is currently leading the way in EBD by lobbying for the health and economic research in hospital design. The center advocated the use of EBD as a means to reduce avoidable incidences by using single patient room layouts, acuity-adaptable rooms and accessible nursing stations (Shoemaker & Kazley, 2010). The AHRQ also mentioned that too often there are no clear, measurable or expected outcomes for large design projects. Although EBD demonstrates these features readily, it is not widely adopted throughout the field of design (Shoemaker & Kazley, 2010). These concepts are beneficial to more than just the healthcare community as the use of EBD can transfer over to all building types and occupancy groups to improve living standards.
An understanding of scientific analysis impacts the ability for designers to implement the important research of perceptual awareness, behavioral interaction and consciousness into the conception of various structures. Educating architecture students in neuroscience allows for an EBD approach to be implemented in all areas of design. If students are more experienced with research and foster a greater awareness of the use of measurable impacts on health, they will be equipped with the knowledge to push design standards forward.

Conclusion

The future of architectural design will depend upon the advancement of evidence-based design and the inclusion neuroscientific research regarding the human experience within space. Studies involving consciousness and the Global Workplace Theory can be used to teach design students about neural correlates which permit conscious awareness (Mallgrave, 2010). A fitting example of neuroscience in architecture is found in the research involving spatial navigation and way-finding. The alignment of floor plans and their feasibility can be monitored and designed to permit the greatest ease in locating one’s self within a build environment. The aging population may also be presented with virtual reality experiences designed by computer game architects to improve memory and attention capacity (Optale et al., 2010). Neuroscience research may also be used to study the effect of environmental stressors on mental health. Students, receiving an overview of chronic HPA axis activation and its role in psychobiological disorders such as IBS, depression, anxiety and transgenerational modifications, have a responsibility to minimize the impact of stress in our daily lives through responsible design. Should students be more aware of the influence of design on the health mechanisms that allow conscious interpretation to take place, they would be more capable to participate in evidence-based design practices.
There is only one architectural program in North America which offers a Certificate in Neuroscience for Architecture; it is offered at the New School of San Diego. Students come into contact with the benefits of EBD with courses on environment and behavior, an overview of the conscious response within space, neuroscience seminars, as well as studio time dedicated to the merger of both disciplines (“Certificate in Neuroscience for Architecture” 2017). EBD is currently taught in many Architectural programs, but the course work only relates to the use of energy efficiency research to increase sustainability rather than neurobiological research to enhance perceptual experience. However, the application of neuroscience principles in EBD is widely accepted in one area of architecture today, healthcare facilities. The multi-disciplinary approach involves extensive background research, patient and staff health, and economic feasibility by implementing design standards that will reduce the length and cost of a patient’s stay. The methods used in the design of healthcare facilities and their measurable outcomes can be applied to any building type. Designers working specifically within EBD using neuroscience research demonstrate that the methods can be taught in architectural programs to promote responsible design.
The limitations of the present research involve the subjective nature of the conscious experience. The greatest challenge to the application of neuroscience as a tool in EBD involves its acceptance within the architectural community. Neuroscience and EBD are generally found within healthcare design as the planning, financial, and life risk implications are extensive. The design process is much greater and more time consuming than other builds on average. However, students stand to benefit from scientific incorporation within design, if only to have a better understanding of the impact of their work.
The future directions of the merger of these two fields involve the use of interactive labs funded by governmental agencies and architecture firms in collaboration with academic institutions. Public access to design research will improve social welfare by eliminating the design standards that are not conducive to occupant health and wellbeing. Architecture firms will need to look at the impact of their builds and become accountable for their health impacts. This may be accomplished by performing post-hoc analyses, animal and human lab research, retrieving foot traffic sensor data, satisfaction surveys and virtual reality prototyping. Before, we could look at architecture as a balance between form and function, which mostly based on what we feel rather than what we can prove as the science was not present. Allowing design to go on without accountable measures of perceptual adaptation when they are now becoming available through research negates advancement within the field and students should be ready for the changes to come in their profession.

References

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Chronic Traumatic Encephalopathy: Connecting Mechanisms to Diagnosis and Treatment

doi:10.22186/jyi.33.4.83-86

Abstract | Introduction |Discussion | Conclusions | References | PDF

Full Issue: September 2017 Special Edition

Abstract

Chronic traumatic encephalopathy is a progressive neurodegenerative disease that has been linked to the incidence of repetitive mild traumatic brain injuries. As chronic traumatic encephalopathy has no formal diagnosis or treatment, current research is striving to better understand its neuropathology in order to develop effective diagnostic and treatment strategies. This review will outline recent research findings in the understanding of the neuropathological mechanisms of chronic traumatic encephalopathy, and connect these findings to advancements in the diagnosis and treatment of the disease. With the emergence of more sophisticated technology, neuroimaging techniques have shown promise as prospective diagnostic tools. Functional neuroimaging techniques that allow for the observation of task-related brain activity such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) imaging have provided significant insight into the progression of chronic traumatic encephalopathy. Additionally, a branch of magnetic resonance imaging (MRI) called diffusion tensor imaging (DTI) is currently being used to assess white matter integrity, which is often compromised in cases of repetitive mild traumatic brain injury and may be indicative of an increased risk for developing chronic traumatic encephalopathy and other neurodegenerative diseases. Several forms of pharmacotherapy, including lithium treatment and monoacylglycerol antagonists, have been suggested to target the common neuropathological markers of chronic traumatic encephalopathy. Recent research suggests that a combination of pharmacotherapy and cognitive therapy may effectively reduce symptoms and improve the quality of life in individuals with chronic traumatic encephalopathy.

Introduction

Chronic traumatic encephalopathy (CTE) is a neurodegenerative disease that is commonly observed in professional athletes, military veterans, and other individuals who have been subjected to repetitive brain injuries. Approximately 42 million people worldwide suffer from brain injury every year, which increases their risk of developing chronic traumatic encephalopathy later in life (Gardner & Yaffe, 2015).
The main symptoms associated with the disease are profound memory loss, motor deterioration, unexplained aggression, depression, and suicidality. These cognitive and behavioral symptoms are also accompanied by biological changes in the brain. Similar to Alzheimer’s disease, CTE is primarily characterized by an accumulation of tangles of protein, although the distribution of these tangles throughout the brain is unique to each disease (Walker & Tesco, 2013).
Significant attention was directed towards chronic traumatic encephalopathy when Dr. Bennet Omalu discovered the disease in a brain autopsy of former National Football League athlete Mike Webster, whose cognitive abilities had drastically declined following his retirement. Numerous indicators of significant brain deterioration were observed in Webster’s autopsy, which was suggested to be accountable for his cognitive dysfunction in his later years (Omalu et al., 2005). Since this initial autopsy, 96% of professional athletes who have been examined for CTE by autopsy have been tested positive for the disease. Although CTE appears to be most prevalent among American football athletes, it is not restricted to this group of individuals. It is suggested that any individual who has been subjected to extensive brain injury throughout their life, including victims of abuse, can develop CTE (Baugh et al., 2012).
Although many great strides have been made in the progression of research on CTE, there is still much that remains unclear about the disease. Currently, there is no formal diagnosis that can be made while the individual is still alive. A post-mortem diagnosis can be performed by an autopsy, which allows for the identification of neuropathological markers of the disease. These markers include the presence of TAR DNA-binding protein 43 (TDP-43), a diffuse spread of hyperphosphorylated tau protein, and enlarged ventricles (Gavett, Stern, & McKee, 2011). Furthermore, there are no established treatment or rehabilitation protocols for individuals who are suspected to have the disease. The goal of current research on chronic traumatic encephalopathy is to investigate prospective solutions to these gaps in knowledge, and this review will discuss recent findings in this area.

Diagnosing Chronic Traumatic Encephalopathy

Although there have been numerous suggested guidelines for diagnosing CTE in vivo based on neuropsychological observations and life history, there are currently no widely accepted pre-mortem diagnostic criteria for the disease. The emergence of more sophisticated technology is creating opportunities for advancing in vivo diagnostic methods, particularly with regards to advancements in neuroimaging techniques. These advancements enable more detailed evaluations of the features of CTE, which are greatly beneficial for further understanding the neuropathological correlation of the disease and how they are unique from other neurodegenerative diseases.
Traditional structural neuroimaging techniques such as computed tomography (CT) and MRI that are often used in clinical assessments to evaluate gross anatomical changes are unable to effectively detect many of the pathological features of the disease that can only be observed at the cellular level, such as the aforementioned TDP-43 and hyperphosphorylated tau protein. However, DTI has become a popular tool for assessing brain injury as it is specialized for detecting abnormalities in brain white matter, which are common in cases of CTE as a result of the mechanical stress on axons following injury (Sundman, Doraiswamy, & Morey, 2015). Assessing white matter integrity can provide insight into the severity of brain damage, and may be a useful tool in identifying individuals at risk of developing CTE.
In addition to DTI, functional neuroimaging techniques have demonstrated great promise in establishing connections between the neuropathology and symptomatology of CTE. Functional neuroimaging uses various techniques to assess brain activity, typically during the performance of a specific task. Positron emission tomography, or a PET scan, uses radioisotopes to measure the amount of glucose being taken up by regions of the brain, which is indicative of activity level. Barrio et al. (2015) used PET scans to investigate the differences in brain activity between retired football players who exhibited symptomatology associated with CTE, confirmedly diagnosed Alzheimer’s patients, and normal controls. The study employed the radioisotope [F-18]FDDNP because it has a high affinity for insoluble protein aggregates, which are trademark features of both CTE and Alzheimer’s disease. The neuroimaging results revealed significantly different signaling patterns across all three conditions. In the group of retired football players suspected to be tested positive for CTE, tau protein aggregates were observed to congregate in subcortical areas and limbic structures, namely in the amygdala. In contrast, the results of the Alzheimer’s group indicated tau protein aggregates predominantly in medial temporal regions, with minimal involvement of subcortical structures. These findings are highly valuable as they not only demonstrate that neuroimaging techniques can potentially be used in the detection of chronic traumatic encephalopathy in vivo, but also to distinguish the neuropathology of CTE from other neurodegenerative disorders.
Another functional neuroimaging technique that has shown promise in identifying features of CTE is fMRI, which measures the levels of oxygen concentration in regions of the brain in response to a certain task or external stimuli. Brain regions with increased oxygen concentrations are suggested to respond with increased activity in comparison to regions with lower oxygen concentrations. Ford, Giovanello, and Guskiewicz’s experiment (2013) was the first to use fMRI to assess differences in brain activity during memory tasks in football players who had been subjected to multiple concussions. The study compared two groups: football players who had experienced more than three concussions during their careers in sports and football players who had experienced less than three concussions. The results indicated no significant differences in performance on memory tasks between the two groups, although notable differences in neural activity were observed in the fMRI results. The low-frequency concussion group displayed more neural activity during relational memory tasks in the parahippocampal gyrus and the inferior parietal cortex. Previous research has suggested that these regions are associated with relational memory tasks in typical individuals. In contrast, the high-frequency concussion group recruited more neural activity from regions of the prefrontal cortex for the same relational memory tasks. The authors hypothesized that this may be due to the fact that the brain regions that are typically active during relational memory tasks were damaged in the high-frequency concussion group, thus explaining the lack of activity in these regions. Further investigation into the long-term effects of this differential neural activity during memory tasks is warranted, as this study did not observe any significant differences in functionality. These findings suggest that the restructuring of neural connections are likely to occur following repetitive brain injury, and support the hypothesis that there are discrepancies in the neurobiology of repetitive versus acute brain injury. This may have implications for the in vivo imaging of neural functioning in individuals who have been subjected to repetitive brain injury and who are suspected to test positive for CTE.

Prospective Treatment Methods for Chronic Traumatic Encephalopathy

Just as there is a lack of pre-mortem diagnostic criteria for CTE, the same is true for treatment methods. The prospective treatment methods for CTE are predominantly preventative in nature by aiming to target and alleviate the adverse neurobiological outcomes of brain injury before they can become pathological and manifest as neurodegeneration. The investigation of pharmacological agents as candidates for the treatment of concussive brain injury has been a popular area of research, and there are several studies that have begun to investigate the use of pharmacological agents to directly target the neuropathology of chronic traumatic encephalopathy itself, rather than the preliminary features of brain injury.
Zhang and colleagues observed that pharmacologically inhibiting the enzyme monoacylglycerol lipase, which plays an important role in degrading endocannabinoid neurotransmitters, significantly reduced the release of pro-inflammatory cytokines and suppressed the phosphorylation of tau protein in a mouse model with repetitive brain injury (Zhang, Teng, Song, Hu, & Chen, 2015). Endocannabinoid neurotransmitters such as 2-arachidonoylglycerol are known to have anti-inflammatory properties, and these findings suggest implications of the endocannabinoid system in the management and reversal of CTE-like neuropathology. Additional research has demonstrated that administering the pharmacological agent JZL184, another monoacylglycerol lipase inhibitor, to rats which had undergone experimental mild traumatic brain injury significantly reduced neuroinflammation, glutamate excitotoxicity, and behavioural impairments associated with brain injury (Mayeux, Katz, Edwards, Middleton, & Molina, 2017). These animal studies provide evidence for the neuroprotective role of the endocannabinoid system in cases of repetitive brain injury. Despite these promising findings, these types of drugs have yet to progress into human clinical trials as enhancing neurotransmission in the endocannabinoid system may have adverse effects including cognitive and sensorimotor impairments (Di Marzo, 2008).
Another experiment using a mouse model of traumatic brain injury revealed that administering lithium significantly reduced tau neuropathology in the thalamus and improved performance on spatial learning tasks (Yu, Zhang, & Chuang, 2012). Although this study chiefly focused on the ability of lithium to reduce the presence of beta-amyloid plaques in the brain, a feature that is more consistent with Alzheimer’s disease, the fact that an attenuation of tau neuropathology was also observed suggests that these findings may also be relevant to CTE. Additional research on using lithium as a treatment for brain injury and CTE is warranted as lithium is known to have psychotropic effects, including producing symptoms of dysphoria and cognitive slowing (Moncrieff, Cohen, & Porter, 2013). Considering that cognitive disturbances and depression, which are often accompanied by dysphoria, are symptoms of brain injury and CTE, these effects of lithium may pose obstacles for its usage as a treatment method. These experiments are notable as they indicate that neurodegenerative markers of CTE may have the capacity to be reversed pharmacologically.

Discussion

There have been many relevant research findings concerning the underlying mechanisms of CTE and implications for diagnosis and treatment. Functional neuroimaging techniques have drastically altered the way that the components of the disease can be investigated, and research has demonstrated a potential role for certain pharmacological agents in the treatment of the disease. In particular, several researchers are focusing on agents that enhance the anti-inflammatory properties of the endocannabinoid system.
Several limitations exist in terms of investigating prospective treatment methods for CTE. For one, the lack of a standardized pre-mortem diagnostic criteria for the disease makes it difficult to formulate a suitable treatment. Furthermore, there are obstacles with translating the existing pharmacological treatment research to human populations due to the known side effects of the proposed drugs. The aforementioned findings are promising, however there is still much that is largely unknown. There are some established hypotheses that aim to explain how brain injury can predispose an individual to developing CTE, although the specific mechanisms by which this occurs remain unclear. Moreover, there is currently no pre-mortem diagnostic protocol for the disease, despite the fact that there is substantial evidence suggesting that in vivo diagnoses are possible. Future research on the relationship between repetitive brain injury and disease progression is warranted, in addition to further investigation into the formation of a standardized diagnostic criteria and effective treatment methods for the disease.

References

Barrio, J. R., Small, G. W., Wong, K.-P., Huang, S.-C., Liu, J., Merrill, D. A., … Kepe, V. (2015). In vivo characterization of chronic traumatic encephalopathy using [F-18]FDDNP PET brain imaging. Proceedings of the National Academy of Sciences of the United States of America, 112(16), E2039-47. doi:10.1073/pnas.1409952112
Baugh, C. M., Stamm, J. M., Riley, D. O., Gavett, B. E., Shenton, M. E., Lin, A., … Stern, R. A. (2012). Chronic traumatic encephalopathy: neurodegeneration following repetitive concussive and subconcussive brain trauma. Brain Imaging and Behavior, 6(2), 244–254. doi:10.1007/s11682-012-9164-5
Di Marzo, V. (2008). Targeting the endocannabinoid system: to enhance or reduce? Nature Reviews Drug Discovery, 7(5), 438–455. doi:10.1038/nrd2553
Ford, J. H., Giovanello, K. S., & Guskiewicz, K. M. (2013). Episodic memory in former professional football players with a history of concussion: an event-related functional neuroimaging study. Journal of Neurotrauma, 30(20), 1683–701. doi:10.1089/neu.2012.2535
Gardner, R. C., & Yaffe, K. (2015). Epidemiology of mild traumatic brain injury and neurodegenerative disease. Molecular and Cellular Neurosciences, 66, 75–80. doi:10.1016/j.mcn.2015.03.001
Gavett, B. E., Stern, R. A., & McKee, A. C. (2011). Chronic traumatic encephalopathy: a potential late effect of sport-related concussive and subconcussive head trauma. Clinics in Sports Medicine, 30(1), 179–88. doi:10.1016/j.csm.2010.09.007
Mayeux, J., Katz, P., Edwards, S., Middleton, J. W., & Molina, P. E. (2017). Inhibition of Endocannabinoid Degradation Improves Outcomes from Mild Traumatic Brain Injury: A Mechanistic Role for Synaptic Hyperexcitability. Journal of Neurotrauma, 34(2), 436–443. doi:10.1089/neu.2016.4452
Moncrieff, J., Cohen, D., & Porter, S. (2013). The psychoactive effects of psychiatric medication: the elephant in the room. Journal of Psychoactive Drugs, 45(5), 409–15. doi:10.1080/02791072.2013.845328
Omalu, B. I., DeKosky, S. T., Minster, R. L., Kamboh, M. I., Hamilton, R. L., & Wecht, C. H. (2005). Chronic traumatic encephalopathy in a National Football League player. Neurosurgery, 57(1), 128-34-34. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/15987548
Sundman, M., Doraiswamy, P. M., & Morey, R. A. (2015). Neuroimaging assessment of early and late neurobiological sequelae of traumatic brain injury: implications for CTE. Frontiers in Neuroscience, 9, 334. doi:10.3389/fnins.2015.00334
Walker, K. R., & Tesco, G. (2013). Molecular mechanisms of cognitive dysfunction following traumatic brain injury. Frontiers in Aging Neuroscience, 5, 29. doi:10.3389/fnagi.2013.00029
Yu, F., Zhang, Y., & Chuang, D.-M. (2012). Lithium reduces BACE1 overexpression, β amyloid accumulation, and spatial learning deficits in mice with traumatic brain injury. Journal of Neurotrauma, 29(13), 2342–51. doi:10.1089/neu.2012.2449
Zhang, J., Teng, Z., Song, Y., Hu, M., & Chen, C. (2015). Inhibition of monoacylglycerol lipase prevents chronic traumatic encephalopathy-like neuropathology in a mouse model of repetitive mild closed head injury. Journal of Cerebral Blood Flow and Metabolism, 35(3), 443–53. doi:10.1038/jcbfm.2014.216

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Non-Hermitian Wave Mechanics: An Unorthodox Way into Embedded Systems

doi:10.22186/jyi.33.4.87-90

Abstract | Introduction | Summary and Outlook| References | PDF

Full Issue: September 2017 Special Edition

Abstract

This review outlines an unconventional but timely formulation of quantum dynamics of systems in contact with an environment. This alternative approach to traditional quantum mechanics is generic and is currently gaining attention in a number of fields as, for example, quantum scattering and transport, optical waveguides, devices embedded in an environment, oscillatory classical systems, RLC circuits and other open systems with loss and gain. Here we briefly outline this formulation in which the condition of space-time reflection (PT-symmetry) plays a central role. If PT-symmetry is broken upon parametric change, real energy levels generally turn complex. At the onset of such a symmetry breaking levels coalesce at “Exceptional Points” (EP).

Introduction

In 1926, Erwin Schrödinger formulated his famous non-relativistic equation for matter waves. In this form quantum mechanics (QM) has since then remained a never-ending success. It expands the classical Newtonian mechanics for particle orbitals into the world of quantum matter as atoms, molecules, solid matter, micro- and nano-scale devices, etc., in which particles acquire wave properties. For this reason it is also referred to, particularly in the early years of the new theory, as wave mechanics (WM) with reference to common wave phenomena present in acoustics, electromagnetism, vibrational structures as membranes and drums, hydrodynamics and more. The predictive power of QM is, as well known, overwhelming.
In short, traditional QM as above rests solidly on a number of postulates as (Schiff, 1968):

(a) A physical system is represented by a wave function Φ(r,t) which holds all information of a system;
(b) Physical observables, as for example momentum p, are represented by Hermitian operators meaning that associated eigenvalues are real numbers and equal possible outcomes of measurements;
(c) The operator representing energy, the sum of kinetic energy T and potential energy V, is the usual Hamiltonian

NHWM eq 1 (1)

where m is the mass of a particle which moves under the influence of a real potential V(r) (ℏ is the reduced Planck constant h/2π). When V(r) does not depend on time t the eigenvalues En of the Hermitian Hamiltonian H are the energy levels of system.
(d) The time evolution of the wave function is given by the time-dependent Schrödinger equation

NHWM eq 2 (2)

For the case above one then has

NHWM eq 3 (3)

where ψn(r) is the n:th stationary solution Hψn=Enψn with real eigenvalue En.
In this review we will introduce an extension (PT-symmetry) to the well known Hermitian QM and describe its implications on QM as well as analogous classical systems. After reviewing the background and current state of the field we discuss some open problems and suggest further studies with the goal to inspire new and clever ideas. Today we lack an experimentally realized PT-symmetric QM system, but with new efforts and ideas that will surely change soon.

A new Paradigm: Non-Hermitian QM and Parity-Time (PT) Symmetry

Measurements in QM return the eigenvalues of observables; for example, a measurement of a particle’s energy yields an eigenvalue of the Hamiltonian. The important assumption of Hermitian operators guarantees that eigenvalues are real and that QM is consistent with measurements. However, more lately it has been argued that the requirement of Hermiticity may be too taxing. Can the energy levels be real also for a Hamiltonian that is complex, i.e., a non-Hermitian one? Under certain circumstances, the answer is yes. Bender and Boettcher (1998) showed how this happens when a system is symmetric under the combined PT operations of parity, or mirror symmetry, (P) and time-reversal (T). These symmetry operations translate to p→-p,r→-r for parity and p→-p,r→r,i→-i for time reversal. Enforcing this symmetry implies for the potential to satisfy V(r)=V*(-r) and thus there is a balanced flow, i.e., gain versus loss is harmonized (Bender, 2005, 2007; Weigert, 2004).
To get an understanding of the role of the complex potential V(r)= VRe(r)+ iVIm(r) consider the simple case of a pair of nearby even and odd states that are localized, for example, to the interior of a closed cavity (Figure 1). Let the solutions for the “unperturbed” case VIm(r)=0 be E1 and E2. Under a parametric change such that VIm(r)≠0 the two levels will interact according to the 2×2  matrix equation

NHWM eq 4,5

(4)

(5)

where Vint is the interaction matrix element between the initial states 1 and 2, i.e., Vint= <1│VIm|2>=<2│VIm|1>; c1 and c2 are the mixing coefficients for the two states. The eigenvalues of the mixed states are

NHWM eq 6 (6)

The modified eigenvalues are evidently real as long as energy gap between states 1 an 2 is larger than |2Vint|. There is a balance between gain and loss. However, as the gap becomes equal to abs(2Vint) on further parametric increase a profound change takes place. The eigenvalues coalesce into a common value referred to as an exceptional point (EP); beyond this point the eigenvalues become complex. Rewriting Eq. (6) as

NHWM eq 7 (7)

The time-dependent solutions in Eq. (2) are now

NHWM eq8 (8)

Beyond the exceptional point there may thus be either exponential decay or growth of the states.  The outline above is a rather elementary one but points to the existence of EPs into which states, may coalesce on parametric change. If we consider the exponentially decaying states, which would apply to fermions because of the Pauli principle that forbids double occupancy, one should thus have the possibility of switching a state on and off by playing with Vint.
In the next section we will discuss the specific example of a quantum in contact with an environment. There will be a number states and for this reason one will have to use more refined methods than above to solve the Schrödinger equation, in this case numerical methods based on finite differences. As we will find the occurrence of EPs is a more complicated story than above, they may come and go with the gain/loss parameter Vint.

A Two-Dimensional Quantum Dot in Contact with an Environment

There is a rich variety of quantum dots fabricated from different materials for different purposes. They may be three- or two-dimensional objects embedded in solid materials, colloidal nanocrystals, etc., with intriguing physics and vast applications. A common feature is, as already the name indicates, that states are confined within a dot are quantized because of its smallness, typically in the nanometer regime. Research, basic and applied, remains very dynamic and there is a rich literature with many good monographs, see for example (Klimov 2010) and more.
Here we will focus on a particular kind of quantum dots that may be created in layered semi-conductor hetero-structures like Ga1-xAlxAs/GaAs. Because of a mismatch between the band-gaps of the two materials and modulation doping with donor atoms there will be an effectively two-dimensional electron gas that resides at the interface. A smart step is to add metallic top layer/gate which makes it possible to vary the density of electrons, even to deplete it. Another smart step is to use lithography to shape the electron gas into small structures like one-dimensional wires, dots of various geometries, combinations of such objects into networks, etc., as for example described by Ferry, Goodnick, & Bird (2009).
Here we present a schematic model of a circular two-dimensional quantum dot embedded in a hetero-structure (Figure 1). The dot contains a number of electrons, usually small, that may be varied via the top gate. There are also pairs of ports that serve as emitters and collectors. In Figure 1A, for example, we may let the left port L be purely imaginary with VL=iVIm and VR=-iVIm for the other port R. Evidently there will be a current flowing through the dot. Related configurations have been elaborated for an electron/microwave billiard (Berggren et al., 2010) and, most recently, for interacting Bose-Einstein condensates (Schwartz et al., 2017).

Figure 1. Schematic picture of two-dimensional circular dots. (A) shows the case with two opposite ports with complex potentials VL(x) = VR*(-x). The interior potential is real and may be set equal to zero. The potential in the exterior region may be set to infinity, i.e., wave functions are confined to the circular area and ports. The vertical line is the line of reflection. The two ports serve as source and drain. Because of PT-symmetry, gain and loss can balance each other. (B) shows a dot with several ports with the possibility of combining the corresponding potentials according to the different symmetry lines and PT invariance. The flow of particles between the ports may thus be monitored by flexible pairings of the potentials in the different sections, i.e., the system will act a bit like a switchboard. While retaining PT-symmetry, the imaginary part of the potential may be chosen differently for the pairs giving rise to a more complex two-dimensional landscape of EPs. Obviously we may also consider more ports than just four.

Figure 1. Schematic picture of two-dimensional circular dots. (A) shows the case with two opposite ports with complex potentials VL(x) = VR*(-x). The interior potential is real and may be set equal to zero. The potential in the exterior region may be set to infinity, i.e., wave functions are confined to the circular area and ports. The vertical line is the line of reflection. The two ports serve as source and drain. Because of PT-symmetry, gain and loss can balance each other. (B) shows a dot with several ports with the possibility of combining the corresponding potentials according to the different symmetry lines and PT invariance. The flow of particles between the ports may thus be monitored by flexible pairings of the potentials in the different sections, i.e., the system will act a bit like a switchboard. While retaining PT-symmetry, the imaginary part of the potential may be chosen differently for the pairs giving rise to a more complex two-dimensional landscape of EPs. Obviously we may also consider more ports than just four.

As shown in Figure 2, the pair of levels may change under the parametric change, and we recover the EP discussed in the previous section. In addition we find, however, that there is another EP on further increase of the interaction, i.e., the state with real eigenvalues is restored. The calculations are more cumbersome than the analytic analysis above; a convenient approach is to turn to numerical finite difference methods described previously (Tellander & Berggren, 2017). Indeed, this method allows for a greater number of states, than just two as was discussed above. With a larger number of states one can expect more EPs to appear in the spectrum. However, the EPs only seem to appear over a finite range of VIm (Tellander & Berggren, 2017) which means that the spectrum can, as in Figure 2, be divided into three regions:the left region where VIm is less than the critical values and all eigenvalues are real, the finite critical region where many EPs exist and the rightmost part of the spectrum where most of the eigenvalues are again real. This crossover between different dynamical regimes is called a dynamical crossover and is of great importance for experimental studies of non-Hermitian QM. In the region of many EPs, the transmission through the system should be enhanced and the states that remain complex in the right region of the spectra are believed to be associated to superradiant modes (a collection of emitters, such as atoms, that radiates strongly due to coherence) studied in atomic physics. Whether superradiance really can be viewed as a dynamical crossover is an unanswered question (Rotter and Bird, 2015).
A system with more gates (Figure 1B) allows for a more direct measurement of EPs and has the possibility to settle the long-lived discussion in the field about the geometric phase obtained by a state when an EP is encircled in the parameter space. This phase is geometric in the sense that it is independent of the path that encircles the EP; compare with Cauchy’s theorem for complex curve integrals or the classical experiment using Foucault’s pendulum to prove that the earth rotates around its own axis. The system in Figure 1B can have one independent imaginary potential for each pair of leads and the parameter space is therefore two-dimensional. This system could therefore be transported around an EP and the phase change of the wave function could be extracted. Similar experiments in analogous systems such as microwave (Dembowski, 2001) and exciton-polariton (Gao, 2015) billiards have been preformed but a pure quantum experiment is still in the future.

Summary and Outlook

Above we have outlined in a schematic way how quantum states and currents in a biased PT-symmetric cavity in contact with surrounding reservoirs may be emulated by means of complex potentials for source and drain. This is, for example, of considerable computational convenience when modelling transport in real devices at small source-drain bias. This idea is already found to work well for the analogue case of two-dimensional microwave billiards (Berggren et al., 2010). There is still, however, a challenge to design and implement real semiconductor devices with the above characteristics.
The physics associated with PT-symmetry is common for a number of wave phenomena and there is a rich and rapidly expanding literature. This includes, for example, electromagnetic systems, in particular in the fields of optics and photonics for which many new possibilities have opened up. Complex potentials in terms of complex refractive indices enter here in a natural way. Thus possible systems to study are co-axial waveguides, microwave billiards and more. In classical mechanics the same kind of behavior may be realized by means of a driven and a damped pendulum coupled to each other. Also in electronics when two RLC−circuits are inductively coupled, one with amplification and one with attenuation, a PT-symmetric system is obtained with EPs that can be studied in details. This shows that PT-symmetry phenomena are ubiquitous in quantum as well as electrical systems. For recent updates and reviews see (Christodoulides et al., 2017; Konotop et al., 2016; Rotter & Bird, 2015) which shows that the present field is an expanding one within fundamental science and technology. Most recently it has also been shown how the formalism for non-Hermitian quantum physics with gain and loss may be used to analyse a very different kind of system, namely photosynthesis (Eleuch & Rotter, 2017).
Finally, it is exciting to find that there is a much older field of physics with its very own traditions and literature that relates to vibrations in string instruments like violins, cellos and pianos (Gough, 1981; Weinreich, 1977, 1979). One thus talks about wolf-notes which are unfortunate facts of life for, for example, cellists who may have to struggle with and tame “wolf cellos.” Wolf notes refer to unwanted interactions of different modes and how these coalesce into damped degenerate states at certain frequencies.  The similarity with EPs that appear in non-Hermitian quantum systems as described above for a quantum dot and illustrated in Figure 2 is obvious. We therefore wish to name such features “quantum wolves.”

References

Bender, C. M., & Boettcher, S. (1998). Real Spectra in Non- Hermitian Hamiltonians Having PT-Symmetry. Physical Review Letters, 80(24), 5243-5246. doi:10.1103/PhysRevLett.80.5243
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Bender, C. M. (2007). Making Sense of Non-Hermitian Hamiltonians. Reports on Progress in Physics, 70, 947-1018. doi:10.1088/0034-4885/70/6/R03
Berggren, K.-F., Yakimenko. I. I., & Hakanen, J. (2010).  Modeling of open quantum dots and wave billiards using imaginary potentials for the source and the sink. New Journal of Physics, 12, 073005-19. doi:10.1088/1367-2630/12/7/073005
Christodoulides, D., El-Ganainy, R., Peschel, U., & Rotter, S. (2017). Focus on Parity-Time Symmetry in Optics and Photonics, New Journal of Physics (A series of selected articles commencing 2014).
Dembowski, C., Gräf, H.-D., Harney, H., Heine, A., Heiss, W., Rehfeld, H., & Richter, A. (2001). Experimental Observation of the Topological Structure of Exceptional Points. Physical Review Letters, 86(5), 787-790. doi:10.1103/PhysRevLett.86.787
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Gao, T., Estrecho, E., Bliokh, K. Y., Liew, T. C. H., Fraser, M. D., Brodbeck, S.,… Ostrovskaya E. A. (2015). Observation of non-Hermitian degeneracies in a chaotic exciton-polariton billiard. Nature 526(7574), 554-558. doi:10.1038/nature15522
Gough, C. E. (1981). The theory of string resonances on musical instruments. Acustica 49, 124-141.
Klimov, V. I. (2010). Nanocrystal Quantum Dots. CRC Press, 2nd edition. CRC Press: ISBN 9781420079265
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Rotter, I., & Bird, J. P (2015). A review of progress in the physics of open quantum systems: theory and experiment. Reports on Progress in Physics, 78, 114001-37. doi:10.1088/0034-4885/78/11/114001
Tellander, F., & Berggren, K.-F. (2017). Spectra, current flow and wave function morphology in a model PT −symmetric quantum dot with external interactions. Physical Review A, 94(4), 042115-12. doi:10.1103/PhysRevA.95.042115
Schiff, L. I. (1968). Quantum mechanics. International Series in pure and applied physics (New York) ISBN : 0070856435 or other basic textbooks on QM.
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Weigert, S. (2004). The physical interpretation of PT -invariant potentials, Czechoslovak Journal of Physics, 54, 1139-11142. doi:10.1023/B:CJOP.0000044016.95629.a7
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Strain Specific: Microbial Strains Involved in Gut-Brain Signaling

doi:10.22186/jyi.33.3.49-54

Abstract | Introduction | Methods | Results | Discussion | Conclusions |Acknowledgements | 
References | PDF

Abstract

Exploration into the microbial role within behavior and neurologic regulation has been an area of growing interest and research. While in-vitro and in-vivo experimentation has suggested that commensal microbiota play a role within behavioral and neurologic functioning, little distinction has been made about the specific microbes inducing change. In order to understand and potentially utilize this complex gut-microbe-brain connection, it is imperative to distinguish between which microbes are inducing behavioral and or neurologic effects, and which biologic mechanisms are mediating said effects. To enhance the current understanding of neurologically influential microbes, this review will analyze eight microbial strains belonging to the genus types Lactobacillus and Bifidobacterium, and note the similarities and dissimilarities pertaining to modulation of inflammatory response, intestinal permeability, neurochemical concentrations, and interaction with the vagus nerve expressed amongst included microbial strains. Analysis of the selected microbes demonstrated significant distinction regarding neurochemical, inflammatory, and immunological effect amongst microbial strains belonging to the genus types Lactobacillus and Bifidobacterium. Interestingly, despite the expressed biologic variation, behavioral influence was largely uniform amongst the included microbial strains and expressed almost exclusively through a reduction in typified anxious and depressive behavior.

Introduction

Despite the myriad of pills and procedures aiming to treat psychiatric abnormalities and conditions, there is still much to be discovered about the brain. Fortunately, exploration into the effect upon commensal microbiota within behavioral and neurologic regulation has created a path in which to further decode and explore this enigmatic terrain. Recent in vitro and in vivo experimentation has demonstrated microbial influence within complex emotive states such as depression, chronic stress, anxiety, and psychiatric disorder (Bailey et al., 2011; Bercik et al., 2010; Maes, Kubera, Leunis, & Berk, 2012; Rook & Lowry, 2008). While this field is emerging and many mechanistic factors facilitating the microbial influence within gut-brain axis regulation have yet to be identified, the work done thus far suggest a future in which the brain can be indirectly targeted for therapeutic benefit through manipulation of commensal microbiota (Clarke et al., 2014; Cryan & Dinan, 2012). At a time when neuropsychiatric disorders are the leading cause of disability in the U.S (USBDC, 2013), the importance of this research cannot be overstated.
Although the blueprint outlining the microbial role within gut-brain axis regulation is far from maturation, the current understanding is that certain microbial strains are able to induce measurable neurologic and behavioral effect through the modulation of inflammatory response, neurochemical concentration, barrier-integrity, and interaction with the vagus nerve.  In order to understand, and potentially utilize these microbial capabilities, it is essential to distinguish between which microbes are inducing behavioral and or neurologic effect, and which pathway each microbe is using to do so. The complexity of this task resides in the significant microbial distinction expressed not only on a species level, but amongst microbial strains (Greenblum, Rogan, & Borenstein, 2015).
Among the more than 7,000 microbial strains (Ley, Peterson, & Gordon, 2006), majority have not demonstrated direct behavioral or neurologic effect. The collection of strains that have been shown to induce measurable neurologic and behavioral manipulation belong to one of the three genus types Lactobacillus, Bifidobacterium, and Bacteroides (Mayer, Knight, Mazmanian, Cryan, & Tillisch, 2014).  Amongst these genus types, a plethora of biologic and behavioral variances have been expressed on both a species and strain level. While variation across differing genus and species type is expected, differences expressed between microbial strains belonging to the same genus and species type is a surprising find in light of the genomic similarity expressed between them. Further investigation into these microbial variances can explain questions such as why, despite genomic similarities, only certain microbial strains are able to induce behavioral and neurologic effect. Why the microbial strains that do induce measurable effect, do so in a variety of ways that are often dissimilar from other strains belonging to the same genus and species type. And whether or not these strain mediated effects can be utilized for therapeutic benefit within neurologic and psychiatric disorders.

Lactobacillus rhamnosus JB-1.

L. rhamnosus JB-1 induces effect within gut-brain signaling through modulation of GABA receptors and inflammatory response. In an experimental study by Bravo et al. (2001), chronic treatment of L. rhamnosus JB-1 in a healthy mice model significantly increased GABA-B receptor concentrations in the cingulate and pre-limbic cortical regions, increased the concentration of GABA-A receptors in the hippocampus, decreased GABA-B receptor concentrations in the hippocampus, amygdala, and locus coerulus, and decreased GABA-A receptors concentrations in the pre-frontal cortex and amygdala. The influence over GABA receptors is supported to have psychiatric impact that is demonstrated through work by Cryan and Kaupmann (2005), whom found a reduced concentration of GABA-B receptors in the frontal cortices of a depression induced mouse model, and Jacobson-Pick, Elkobi, Vander, Rosenblum, and Richter-Levin (2008) whom found an increase of GABA-A receptors in the amygdala of a chronically stressed mice model.
Inflammatory affect was demonstrated in a study by Forsythe and Bienstock (2004), which found that L. rhamnosus JB-1 reduced intestinal inflammation through an upregulation of nerve growth factor, and inhibition of IL-8 synthesis. The neurologic impact of these noted effects is supported in work done by Angelucci, Aloe, Vasquez, and Mathé (2000), showing an altered concentration of brain derived neurotropic factor and nerve growth factor in a depression induced mice model. As well as in work done by Janelidze et al. (2015), showing an association between low serum levels of IL-8 and anxiety in suicidal patients.
L. rhamnosus JB-1 demonstrated behavioral impact in the abovementioned study by Bravo et al. (2001) through a significant decrease in typified anxious and depressive behavior in mice chronically administered with L. rhamnosus JB-1. In addition to modulation of GABA receptors, this effect was deemed to be in relation to interaction with the vagus nerve, for behavioral effect was not observed in vagotamized mice (Bravo et al., 2001). Due to the noted associating between nerve growth factor and depression, the increased concentration of nerve growth factor by L. rhamnosus JB-1 could also play a role within the noted decrease of typified anxious and depressive behavior.

Lactobacillus rhamnosus GG.

L. rhamnosus GG induced neurologic influence primarily through modulation of inflammatory processes. In an experimental study by Turner (2009), children with atopic dermatitis administered with L. rhamnosus GG resulted in significant reductions of serum levels of IL-10, which has been shown to reduce intestinal inflammation through the suppression of regulatory T cells (Park et al., 2005). The behavioral impact of this effect is suggested in a previous study by Dhabhar et al. (2009), which observed a decreased concentration of IL-10 levels in adults with major depression syndrome. Additional inflammatory effect was observed in an experimental study by Pessi et al. (2001), which noted a decreased concentration of regulatory T cells in casein degraded by L. rhamnosus GG. While no direct associations between regulatory T cell concentration and behavioral and or neurologic impact was observed, an indirect link pertaining to decreased inflammation by regulatory T cells (Park et al., 2005) which has been correlated with depression and anxiety (Bercik et al., 2010; Maes, Kubera, Leunis, & Berk, 1999; Smith & Rudolph, 1991;) supports the psychiatric influence of this inflammatory effect.
Behavioral impact was observed in a study by Kantak, Bobrow, and Nyby (2013), which observed an attenuation of obsessive compulsive disorder (OCD) typified behaviors in a house mice model administered with L. rhamnosus GG, that worked as well as the positive study control, fluoxetine, which is common medication used in OCD treatment (Bandelow et al., 2012). The noted behavioral affect was presumed to be in relation to a decrease in the anxiety often preceding or accompanying OCD, and not from a direct modulation of the neurologic mediators associated with OCD typified behavior (Kantak et al., 2013). In reference to the abovementioned effects upon inflammation, it is plausible to infer that the noted behavioral modulation could be mediated, at least partially, through inflammatory modulation which has been previously associated with anxiety (Maes et al., 1999; Smith & Rudolph, 1991).

Lactobacillus helveticus NS8.

L. helveticus NS8 induces behavioral and neurologic impact through modulation of neurotransmitters and inflammatory response. Research conducted by Liang et al. (2015) found that when administered in a pathogen free rat model, L. helveticus NS8 restored hippocampal concentrations of serotonin and norepinephrine. The psychological significance of this effect can be noted in previous work by Stanton and Sarvey (1985), which found that hippocampal depletion of norepinephrine reduces the frequency and magnitude of long term potentiation, which is a synaptic mechanism associated with cognitive functions pertaining to learning and memory (Eichenbaum & Otto, 1992), as well as attention and arousal (Shors & Matzel, 1997). Hippocampal effect was also noted in a study by Martinowich et al (2007), which observed an increase of hippocampal brain derived neurotrophic factor mRNA, which has been noted to play a role within depression and psychiatric disorders such as schizophrenia and bi-polar disorder (Martinowich et al., 2007; Palomino et al., 2006). L. helveticus NS8 was also shown to induce inflammatory effect through the attenuation of LPS induced inflammation which was mediated through an increased synthesis of IL-10 (Rong et al., 2015).
L. helveticus NS8 induced behavioral effect in an experimental study by Luo et al. (2014), which observed an attenuation of anxiety and improved cognition in a hyperammonemia induced rat model administered with L. helveticus NS8. These results were supported in a proceeding study by Liang et al. (2015), which observed a significant reduction of chronic stress induced anxiety, depression, and cognitive dysfunction in mice orally administered with L. helveticus NS8. All abovementioned induced effects including the synthesis of serotonin and norepinephrine (Liang et al., 2015), increased concentration of hippocampal brain derived neurotrophic factor mRNA (Martinowich, Manji, and Lu, 2007), and increased synthesis of IL-10 (Rong et al., 2015), appear to be plausible, and likely, mechanisms mediating the observed behavioral impact by L. helveticus NS8.

Lactobacillus helveticus R0052.

L. helveticus R0052 induced cerebral effect through mechanisms pertaining to increased barrier integrity and amelioration of stress induced irregularities in hypothalamic-pituitary-adrenal axis (HPA-axis) and automatic nervous system (ANS) functioning. Influence upon barrier integrity was supported in a study by Johnson-Henry, Hagen, Gordonpour, Tompkins, and Sherman (2007), which observed that L. helveticus R0052 prevented against pathogenic infiltration of Escherichia coli 0157:H7 by binding to epithelial cells and blocking pathogenic entry. This impact suggests that L. helveticus R0052 may play a beneficial immunologic role through the enhancement of intestinal barrier security and protection against pathogenic infection. This is a neurologically relevant action, for previous studies have shown a connection between pathogenic infection and behavior (Quinn et al., 1984) as well as psychological stress and barrier integrity (Ait-Belgnaoui, Bradesi, Fioramonti, Theodorou, & Bueno, 2005). HPA-axis modulation was supported in a study by (Ait-Belgnaoui et al., 2014), which observed a reversal of stress-induced HPA-axis and ANS irregularities, which was mediated through a decreased plasmatic concentration of corticosterone, adrenaline, and noradrenaline in a stress-induced mice model administered with a combination of L. helveticus R0052 and Bifidobacterium longum R0175. The modulation of the HPA-axis presents possible psychological relevance, for the HPA-axis has domain over coordinating all bodily stress responses (Tsigos & Chrousos, 2002).
L. helveticus R0052 induced behavioral effect in a study by Ohland et al. (2013), which observed an amelioration of diet-induced anxiety and memory deficits in mice administered with L. helveticus R0052. Interestingly, this effect appeared to be diet-dependent, for behavioral effect was only noted when mice were followed a high fat “western” diet, but not when following a low fat “chow” diet. These results were supported in another study by Gilbert, Arseneault-Bréard, and Flores Monaco (2013), which noted a diet-dependent decrease in typified depressive behavior in a post-myocardial infarction induced mice model administered with a combination of L. helveticus R0052 and Bifidobacterium longum R0175. Although future experimentation is required to be able to accurately ascertain the likely factors mediating the noted behavioral influence, it is reasonable to conclude that in addition to dietary intake, the abovementioned influence over HPA-axis regulation (Ait-Belgnaoui et al., 2014) could be a contributing factor to the noted behavioral modulation.

Lactobacillus johnsonii N6.2

L. johnsonii N6.2 induced neurologic effect through modulation of neurotransmitters concentrations, gut-barrier integrity, and oxidative stress concentrations. In an experimental study by Valladares et al. (2013), L. johnsonii N6.2 increased ileum and peripheral serotonin concentrations, decreased peripheral kynurenine concentrations, and decreased tryptophan activity when administered to a “BioBreeding” rat model. The significance of this alteration within behavior and neurologic functioning can be seen in previous work showing an association between low levels of serotonin and depression (Lucki et al., 1998), as well as an association between tryptophan depletion and depression and panic disorder symptom relapse (Bell et al., 2001). Further microbial influence was shown in a study by Valladares et al. (2010), which observed that when administered to a diabetic induced rat model, L. johnsonii N6.2 induced effect within barrier integrity by increasing concentrations of tight junction protein claudin and decreasing host oxidative stress response.  In addition to the previously noted association between barrier integrity and psychological stress (Ait-Belgnaoui et al., 2005), the behavioral significance of the modulation of oxidative stress response is supported in a study by Gorrindo, Lane, Lee, McLaughlin, and Levitt (2013), which found a correlation between increased oxidative stress response levels and increased severity of autism spectrum disorder associated behavioral abnormalities pertaining to language impairment, OCD typified behavior, and aversion to social communication. While no studies have tested to see whether or not L. johnsonii N6.2 induces effect within behavior, the noted induced effects pertaining to modulation of neurotransmitters and effect upon oxidative stress levels warrants future research to assess the degree of possible behavioral modulation.

Bifidobacterium longum, subspecies longum JCM1217

Bifidobacterium longum subsp. longum JCM1217 induced effect within gut-brain axis regulation through immunological modulation. In a study by Fukuda et al. (2011), pathogenic infection of Escherichia coli 0157:H7 was prevented in mice administered with B. longum, subspecies longum JCM1217 through production of acetate. Fukuda et al. (2011) proposed that increased acetate production protected against pathogen infection by attaching to epithelial cells and preventing the pathogen from translocating from the gut lumen to host blood supply. In addition to the increased barrier integrity, which has been previously noted to have an association with psychological stress (Ait-Belgnaoui et al., 2005), the induced production of acetate could have possible behavioral ramifications, for experimentation by MacFabe et al. (2007) observed that autism spectrum disorder behavioral abnormalities were induced in a rat model when short chain fatty acid levels were manually increased. More research should be conducted on B. longum, subspecies longum JCM1217 to assess whether or not the noted immunological effect induces behavioral or neurologic modulation.

Bifidobacterium longum NCC3001.

In an experimental study by Bercik et al. (2010), Bifidobacterium longum NCC3001 ameliorated colitis associated behavioral alterations and brain derived neurotrophic factor depletions, which has been previously associated with stress and depression (Martinowich et al., 2007; Angelucci et al., 2000). However, when tested in a follow-up study by Bercik et al. (2011), colitis associated alterations brain derived neurotrophic factor was not observed, which demonstrates that this was likely not a factor mediating the noted behavioral change. Additional neurologic effect was demonstrated in a study Khoshdel et al. (2013), which observed that B. longum NCC3001 significantly reduced the excitability of enteric neurons when applied to ileal segments of adult mice. This effect upon enteric neurons was supported in a study by Bercik et al. (2011), and presumed to be a possible method in which the central nervous system is signaled through activation of vagal pathways within the enteric nervous system.
Behavioral effect was noted in the abovementioned studies (Bercik et al., 2010; Bercik et al., 2011) through the attenuation of colitis associated typified anxious behavior.  Both studies also observed in the abovementioned study by Bercik et al. (2011) through an attenuation of typified anxious behavior and an increase in typified exploratory behavior.  Interaction with the vagus nerve appeared unclear, for behavioral effect was found independent of the vagus nerve in the first study by Bercik et al. (2010) and dependent of the vagus nerve in the follow-up study by Bercik et al. (2011). While the noted effect regarding decreased excitability of the vagus nerve supports interaction with the vagus nerve within behavioral modulation (Bercik et al., 2010; Khoshdel et al., 2013), more research is required to adequately determine the degree of vagal involvement.

Bifidobacterium infantis 35624.

Bifidobacterium infantis 35624 induced both behavioral and neurologic effect through modulation of neurotransmitters, inflammatory response, and intestinal permeability.  Research by Desbonnet, Garrett, Clarke, Bienenstock, and Dinan (2008) observed that B. infantis 35624 reduced frontal cortex serotonin concentrations, increased plasmatic concentrations of noradrenaline and tryptophan, and inhibited the production of IL-10. The significance of these mediated effects are supported in previous work by (Amat et al., 2005; Bland et al., 2003) showing a connection between prefrontal cortex serotonin concentration and the modulation of anxiety, in work by Vijayakumar and Meti (1999) showing a connection between noradrenaline and depression, in work by Myint et al. (2007) showing a connection between decreased plasmatic tryptophan concentrations and depression, and work by (Dhabhar et al., 2009) showing a connection between IL-10 and major depressive syndrome.
Behavioral influence was demonstrated in the abovementioned study by (Desbonnet et al., 2008) which noted an amelioration of typified depressive behavior in rats administered with B. infantis 35624 that worked as effectively as the positive study control Citalopram supporting the effectiveness of B. infantis 35624 as a behavioral regulator.

DISCUSSION

The variation of induced effects expressed by the 8 included microbial strains demonstrate that even amongst strains belonging to the same genus type, generalizations pertaining to induced effects should be avoided. For example, while four out of the five included Lactobacillus strains induced influence over a collection of 9 neurotransmitters, modulation over the same neurotransmitter was only expressed one time in the modulation of serotonin by both L. helveticus NS8 and L. johnsonii N6.2 (Liang et al., 2015; Valladares et al., 2013). Interestingly, in some instances microbial strains belonging to the same genus type induced oppositional effects. For example, while both L. rhamnosus GG and L. helveticus NS8 impacted the concentration of anti-inflammatory cytokine IL-10, L. rhamnosus GG decreased IL-10 concentrations (Turner., 2009) while L. helveticus NS8 increased them (Rong et al., 2015). Oppositional impact was also expressed amongst strains belonging to differing genus types, such as L. johnsonii N6.2, which increased peripheral serotonin concentrations (Valladares et al., 2013), and B. infantis, which reduced peripheral serotonin concentrations (Desbonnet et al., 2008).
Despite the diversity of microbially induced effects, behavioral modulation was largely uniform amongst the included strains. Six out of the eight strains demonstrated behavioral influence through the attenuation of of typified anxious and or depressive behavior. The apparent dichotomy expressed by the divergent mediated effects and uniform behavioral influence demonstrates the complexity of assessing the microbe-brain connection. As with many organically occurring events, no one factor determines whether or not an effect will take place. It is a consortium of many factors, some yet to be identified, and some yet to be understood.
While the noted behavioral modulation is promising and undoubtedly warrants further investigation, it is essential to remember the vast differences between human and animal physiologies and the methodology in which behavioral influence is assessed in animal models. The clinical testing done thus far has suggested promising results. For example, a study examining the effect of a multispecies probiotic supplement upon depressive indicators in healthy individuals found a significant decrease in a cognitive reactivity to sad mood amongst those given the probiotic supplement when compared to participants supplied with the placebo (Steenbergen, Sellaro, Hemert, Bosch, & Colzato, 2015). This effect was attributed to a decrease in frequency of negative and aggressive thoughts which further supports the potential therapeutic benefit of varying microbial strains (Steenbergen et al., 2015). It will be interesting to see whether or not this effect is viable in participants who have pre-existent conditions such as depression or acute anxiety as well as other psychiatric disorders.

Acknowledgements

This review was completed as an exterior project for a graduate level course facilitated by Dr. Colette LaSalle, at San Jose State University.

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Brain Tumor Segmentation Using Morphological Processing and the Discrete Wavelet Transform

doi:10.22186/jyi.33.3.55-62

Abstract | Introduction | Methods | Results | Discussion | Conclusions |Acknowledgements | 
References | PDF

Abstract

Medical imaging is key for the successful diagnosis and treatment of brain tumors, but the initial detection of tumors is, by nature, difficult. Image segmentation, a technique often used to aid detection, is highly dependent on the resolution of the segmented image. Many common morphological segmentation methods often suffer from a lack of resolution which hinders tumor detection. Thus, in this paper, two tumor segmentation techniques are developed and compared using MATLAB – one based on morphological processing, and a second which combines the discrete wavelet transform with morphological processing. Both proposed approaches begin with skull stripping via binary erosion, followed by image contrast enhancement and histogram thresholding. In the wavelet-based technique, the key step is to perform a fourth level discrete wavelet decomposition followed by manipulations of the wavelet. The resulting image is then morphologically opened, contrast enhanced, and gray thresholded. Both approaches were successfully tested on several magnetic resonance images, and it was shown that the wavelet transform method generally produces higher resolution segmented images. Additionally, it was found that the choice of wavelet basis function used plays a key role in the resolution of segmentation, with the Symlet 20 wavelet basis able to segment out almost 18% more pixels on average from an MR image than the Haar wavelet basis. These results can serve as a useful future reference as they provide convincing evidence of the necessity for careful choice of wavelet basis and suggest a basis that seems well suited for this application.

Introduction

Within Magnetic Resonance (MR) image processing, one major problem is the segmentation of brain tumors. Segmentation is the process of partitioning an image into several distinct sections to simplify the image or to focus on a region for further study (Kaur, G., & Rani, 2016). In the last several decades, there has been a push for automating the process of segmentation of pathological regions in the brain in MR scans. Current research on brain tumor segmentation uses a wide variety of methods which can be grouped as “intelligent” or “non-intelligent.” “Intelligent” techniques include machine learning methods such as neural-networks and support vector machine. The focus of this investigation, however, was on non-intelligent techniques, which include local and global thresholding, histogram operations, and morphology (Kaur & Banga, 2013).
Each of these methods has certain deficiencies. For example, global thresholding is not localized enough to produce segmentations with the desired resolution, but local thresholding is often not robust enough because of variations in how tumors present on MR images. Statistical models and morphological processing can also struggle to differentiate tissues in complex cases (Kaus et al., 1999). Wavelet-based techniques have become more popular in recent years because of the robustness they provide, and many hybrid wavelet methods are present in the literature (e.g., Sawakare & Chaudhari, 2014). When the correct method is found for a specific application, image segmentation can be a highly effective and important process because it allows clinicians to better understand the nature of a tumor and to plan more effective treatments (Xuan & Liao, 2007). Non-intelligent segmentation techniques are particularly important because they are often used for refining and improving intelligent segmentation methods (Kaur & Banga, 2013). In this research, a non-intelligent segmentation method, different from but inspired by those that are currently in the literature (Saini & Singh, 2015), was created based on morphology. Additionally, enhancements to this technique were developed using the discrete wavelet transform. These improvements were tested using several different wavelet basis functions. The major result of this investigation shows the key role that the choice of wavelet basis function plays in the resolution of the segmented image, a notion generally not discussed in the literature.

Morphology

The first of the two main mathematical concepts underlying this research is morphological image processing. Morphology consists of several operations that can be performed on a given image, represented by an image matrix, using other matrices called structuring elements (SE’s) in order to alter the image in desirable ways. Morphological processing is driven by operations performed by the SE’s on the image matrix, which use ones and zeroes to perform geometrical transformations based on the distributions of said ones and zeroes. The morphological operation used for segmentation in this investigation is called morphological opening. Morphological opening is made up of the successive operations of morphological erosion and dilation, which are both performed using the same SE.
Morphological erosion is defined in terms of set notation as follows. Given image A and structuring element B, both sets in Euclidean N-space, then the erosion of A by B, denoted A A B, “is the set of all elements x for which (x+b) ϵ A for every b ϵ B” (Sternberg, Haralick, & Zhuang, 1987):

BT equation 1 (1)

Essentially, erosion shrinks the geometric features within an image based on the distribution of ones and zeroes within the SE. Morphological dilation, denoted A A B (where A and B are the same as above), is defined as (Sternberg et al., 1987):

BT eq 2 (2)

Essentially, dilation inflates the geometric features within an image based on the nature of the SE.
The combination of these operations in succession, denoted  A o B, is called morphological opening, and it is defined as (Sternberg et al., 1987):

BT eq 3 (3)

Discrete Wavelet Transform

The second concept central to this investigation is the discrete wavelet transform. Traditionally, Fourier processing is used in most signal and image processing applications. Fourier bases are frequency localized however, meaning that small changes in space produce great changes in frequency and vice versa. As a result, Fourier based processing methods work very well for analyzing periodic signals, but abrupt changes are not easily detected through Fourier transform based analysis (Ingale, 2014). Due to these limitations, the decision was made to use the wavelet transform in support of morphological segmentation instead of the Fourier transform. The most important property of wavelets, the localization of their basis functions, sharply contrasts with the nature of Fourier basis functions; wavelet basis functions provide a degree of localization in both the space and frequency domains, meaning that small changes in space create small changes in frequency and vice versa.
Wavelet basis functions consist of a father wavelet (scaling function) notated φ(x), and a mother wavelet function, the first level of which is notated ψ(x). These mother wavelet functions can be scaled and shifted so that they cover the entire x-axis. These shifted, scaled functions are used to decompose a signal into its component parts. This decomposition, which allows for a more in-depth analysis of a particular region of the signal, is known as the discrete wavelet transform (DWT).
The DWT in one dimension is used to decompose and analyze signals such as an electrocardiogram or audio signal (a signal which can be represented by a single row or column vector). However, an image is not a one-dimensional signal like an EKG, but can instead be thought of as a two-dimensional signal (represented by a matrix instead of a column or row vector). Thus, for the computation of the DWT of an image, the first step is to extend the wavelet basis function to two dimensions, which is done using both the father wavelet φ(x) and the mother wavelet ψ(x). The wavelet function is extended to two dimensions in the following manner (Tolba, Mostafa, Gharib, & Megeed, 2001):

 Screen Shot 2017-07-29 at 7.46.16 PM

(4)

 

 

(5)

 

 

(6)

 

 

(7)

where the superscripts denote horizontal (h), vertical (v), and diagonal (d) basis functions. These new wavelet functions are used to define high and low pass decomposition and reconstruction filters (Lo-D and Hi-D in the above figure represent said decomposition filters), which are used to compute the DWT of the image by the convolution algorithm pictured in Figure 1 (“DWT2,” n.d.). This decomposition creates sub-images, the “CA,” “CD-horizontal,” “CD-vertical,” and “CD-diagonal,” in Figure 1. These sub-images each contain unique data about the original image; the approximation image, “CA,” contains a smaller, lower resolution version of the original image, and the detail (“CD”) images contain information about the vertical, horizontal, and diagonal aspects of the original image. Manipulation of these sub-images allows for effective analysis of important data, such as a tumor region, and has proved to be the powerful analysis tool that enhances morphological segmentation in this investigation.

Figure 1. Algorithm for DWT decomposition computation.

Figure 1. Algorithm for DWT decomposition computation.

Methods

Step 1: Skull Stripping

For both segmentation methods, the first step is to remove the skull from the original MR image. This is an important step because in many MR images, the skull appears as one of the brightest regions of the image, and is sharply contrasted with other regions of the brain, such as grey matter. In the types of MR images used in this investigation, tumors also appear as bright regions, and thus to limit false positive segmentations from occurring, the skull must be removed from the image. To do this, the original Red, Green, and Blue (RGB) or Grayscale image was converted to a binary image. Next, the pixels that corresponded to the skull were eroded away (i.e. removed from the outside in, until the skull was eliminated). Then, the smaller, eroded binary image was used as a mask and placed over top of the original image, so that only the pixels in the original image that correspond to ones in the eroded binary image are kept as they are in the original image, and all other pixels (those of the skull) are set to 0 (eliminated or set to black). The result is a skull stripped version of the original MR image. This is shown in Figure 2 (Clark et al., 2013).

Figure 2. The results of performing skull stripping. A. Original brain MR image obtained from The Cancer Imaging Archive. B. The results of skull stripping the image a via the processes described in Methods: Step 1.

Figure 2. The results of performing skull stripping. A. Original brain MR image obtained from The Cancer Imaging Archive. B. The results of skull stripping the image a via the processes described in Methods: Step 1.

Step 2: Contrast Enhancement and Thresholding

The next step in the proposed segmentation method is to perform contrast enhancement and thresholding based on Otsu’s thresholding method (Otsu, 1979). These operations are performed as preliminary, global segmentation steps which help to illuminate the tumor region. The contrast enhancement method used is based on the shape of a specified curve. All the MR images used in this investigation are weighted such that pathological regions appear as brighter than other regions of the brain. Thus, to illuminate the tumor region and fade other regions, the general shape of the curve used made light regions lighter and dark regions darker. The results of contrast enhancement are shown in Figure 3.

Figure 3. The results of performing contrast enhancement on the skull-stripped image shown in Figure 2B.

Figure 3. The results of performing contrast enhancement on the skull-stripped image shown in Figure 2B.

After contrast enhancement is performed, the next step is to perform thresholding by Otsu’s method. The goal of thresholding is to decide which pixels in the image correspond to the foreground and which correspond to the background. Once the background and foreground pixels have been determined based on the distribution of pixel values within the image, the image is converted to binary, sending the background pixels to a value of 0 and the foreground pixels to a value of 1. The skull stripped image is then masked with this thresholded binary image to produce the final image for this step in the segmentation (Figure 4).

Figure 4. The results of performing Otsu thresholding on the image shown in Figure 3.

Figure 4. The results of performing Otsu thresholding on the image shown in Figure 3.

Step 3: The DWT

For the technique involving the discrete wavelet transform, the next step is the wavelet step (for the technique not involving the DWT, the next step is morphological opening, which will be discussed in Step 4). The first aspect of this step is the choice of wavelet basis function. Since different basis functions provide different degrees of localization in the space and frequency domains, they also allow for different degrees of resolution in segmentation. The four main bases tested in this investigation are shown in Figure 5.

Figure 5. Wavelet Basis Functions Tested. A-D from left to right, top to bottom. A. Haar Basis. B. Daubechies 2 Basis (‘db2’). C. Symlet 4 Basis (‘sym4’). D. Symlet 20 Basis (‘sym20’).

Figure 5. Wavelet Basis Functions Tested. A-D from left to right, top to bottom. A. Haar Basis. B. Daubechies 2 Basis (‘db2’). C. Symlet 4 Basis (‘sym4’). D. Symlet 20 Basis (‘sym20’).

After much testing, it was found that the best wavelet basis for this application was the Symlet 20 wavelet, pictured in Figure 6 with the corresponding high and low pass filters. Comparisons between bases will be examined in the results section.

Figure 6. Father and mother wavelet functions, as well as the high and low pass decomposition and reconstruction filters, for the Symlet 20 wavelet.

Figure 6. Father and mother wavelet functions, as well as the high and low pass decomposition and reconstruction filters, for the Symlet 20 wavelet.

Once the wavelet basis is chosen, the DWT can be performed on the image. The computation of the DWT in MATLAB decomposes the image into four outputs: the approximation image and the three detail images (“CA,” “CD-horizontal,” “CD-vertical,” and “CD-diagonal). Once the first DWT is computed, a second level DWT is computed using the first level approximation image as the input, and the small magnitude coefficients of the approximation matrix are set to 0. This pattern is repeated two more times for a total of four operations of the DWT.
Then, the detail sub-images are set to 0. The first reconstruction produces an image which is then used to reconstruct the second image and so on until the final image is the result of four reconstructive processes using the inverse DWT (IDWT) (four operations of the IDWT are needed for reconstruction to “undo” the four operations of the DWT used for deconstruction). The resultant image is then contrast enhanced and thresholded, for a second time, via the methods used in Step 2 above. The results of these operations are used as a mask which is overlaid on the original image (Figure 7).

Figure 7. The results of performing wavelet decomposition and reconstruction on the image pictured in Figure 4.

Figure 7. The results of performing wavelet decomposition and reconstruction on the image pictured in Figure 4.

Step 4: Morphological Opening

In this step, the two approaches converge again. For the hybrid, wavelet-morphology based approach, the next step is to morphologically open the results of the wavelet processing step (Figure 7) to remove all regions that do not correspond to the tumor. For the morphological (non-wavelet) approach, the step is to morphologically open the skull-stripped, thresholded image (Figure 4). After much testing, the most suitable structuring element for this application was found to be a “disk” (i.e. a circle of 1’s with 0’s outside of the circle in the SE matrix). The radius of the SE used in this research was 14 pixels.
The results of morphologically opening the wavelet reconstructed image with the above structuring element are shown below in Figure 8a, and the results of morphologically opening the skull-stripped, thresholded image without the wavelet step are pictured in Figure 8b.

Figure 8. A. The results of morphologically opening the image shown in Figure 7. B. The results of morphologically opening the image shown in Figure 4.

Figure 8. A. The results of morphologically opening the image shown in Figure 7. B. The results of morphologically opening the image shown in Figure 4.

Step 5: Final Contrast Enhancement and Thresholding

The final step is to perform another contrast enhancement and thresholding on the morphologically opened images created in the previous step. This makes it so that the only object left in the image after this step is the segmented tumor region (Figure 9). This region is shown in red (Figure 10) overlaid on the original MR image for qualitative comparison.

Figure 9. A. The results of contrast enhancing and thresholding the image shown in Figure 8A. B. The results of contrast enhancing and thresholding the image shown in Figure 8B.

Figure 9. A. The results of contrast enhancing and thresholding the image shown in Figure 8A. B. The results of contrast enhancing and thresholding the image shown in Figure 8B.

Results

This section will present two sets of results:  a comparison of the two segmentation methods developed in this paper (i.e. segmentation with and without the DWT) and a comparison of the resolutions produced when using the different wavelet bases which shown in Figure 5.
The comparisons made in this section show the difference in the ability of each technique to segment out data. They indicate the percent difference in how much data was segmented out of the image by each technique (they are shown in the form of percent difference instead of actual pixel numbers because the size of each tumor varies greatly from image to image, making raw pixel number comparisons meaningless).

Comparison Between Segmentation Methods

Morphological segmentation was successfully used as a baseline for tumor segmentation in this paper. With the addition of the wavelet transform step, the resolution of segmentation (i.e. the number of correct pixels that were partitioned from the image) fluctuated based on the choice of wavelet basis. The following tables shows comparisons between morphological segmentation and hybrid wavelet-morphological segmentation for each image tested and each of the four wavelet bases shown in Figure 5 above.

Comparison Between Wavelet Bases

From the comparisons made in Table 1, it is clear that certain wavelet bases perform better than others. Since the Symlet 20 basis consistently outperformed the other three in comparison to morphological processing, the following section serves to compare the resolution of the Symlet 20 basis with the Symlet 4, Daubechies 2, and Haar bases. The following table illustrates how the Symlet 20 wavelet basis compared with each of the wavelet bases used above for each of the images tested.

Table 1. Comparisons Between Techniques. The percent difference in resolution represents the number of pixels that were segmented using the given wavelet basis for the DWT compared with the number of pixels.

Table 1. Comparisons Between Techniques. The percent difference in resolution represents the number of pixels that were segmented using the given wavelet basis for the DWT compared with the number of pixels.

Discussion

As the data shows, the hybrid wavelet-morphological technique performs consistently better segmentation than just morphological segmentation when using the Symlet 4 and Symlet 20 wavelet bases, but is inconsistent when using the Daubechies 2 and Haar bases. Table 1 indicates that the Symlet 20 basis is the best suited basis tested, as the worst resolution that it produced was still about thirteen percent better than the morphological approach. Additionally, the Symlet 20 basis consistently outperformed each other wavelet basis, with an average of seventeen percent more data segmented when compared with the Haar basis and two and a half percent better resolution when compared with the Symlet 4 basis. This underscores the importance of the choice of wavelet basis function for tumor segmentation, a result not previously reported in the literature. Thus, with the right choice of wavelet basis, using a combination of wavelet processing and morphological techniques instead of just morphological processing can help to eliminate data loss in tumor segmentation. On average, the Symlet 20 wavelet basis could segment out about 20 percent more data than morphological processing alone.
Even though it is clear that the Symlet 20 basis is best suited for this technique, it is still uncertain as to exactly why this is the case. One thought, at least when it comes to comparing the Haar and Symlet 20 bases, is that the Haar basis is more ‘rigid’ and is less able to detect smaller changes in pixel characteristics than the Symlet 20 basis. This is an unproven idea, meaning that one main area of future work is to develop a more concrete theory as to why some wavelet bases perform so much better than others. An additional area of future work is to improve on the skull stripping method used in this technique. This skull stripping method is somewhat “primitive” and not highly adaptable, meaning that for tumors near the skull region, significant data loss is a distinct possibility. As a result, another highly important area of future work would be to develop a more robust, wavelet-based skull stripping method.

Conclusion

Brain tumor segmentation is an inherently difficult problem because of the widely varying nature of pixel distributions of pathological tissues in MR images. The segmentation methods and results presented in this paper are just two of many methods that have been developed over the past several decades with the goal of automating and improving brain tumor segmentation. The results underscore the effectiveness of the wavelet transform, as well as present novel findings about the impact of basis function choice on the resolution of the segmented image. Although many techniques exist that use the wavelet transform in some way, none that combine morphology and the DWT in the way presented in this paper have been reported in the literature. Additionally, there have never been clear results reported on the impact of the wavelet basis function choice on the results of segmentation. The results of the investigation in this paper can therefore serve as a useful guide for the development of future segmentation techniques involving the wavelet transform because they provide convincing evidence that the choice of wavelet basis can be vital to the resolution of a segmented image, as well as point to a basis with useful properties for this application.

Acknowledgments

The author would like to thank his mentors, Dr. Marcus Fries, Dr. Pierre-Richard Cornely, and Dr. Jill Macko for their invaluable ideas and support.

References

Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moorse, S., Phillips, S., Maffit, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, 26(6), 1045-1057. doi:10.1007/s10278-013-9622-7.  DWT2. (n.d.). Retrieved from https://www.mathworks.com/help/wavelet/ref/dwt2.html. x
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of Signal Processing, Image Processing and Pattern Recognition, 7(4), 345-362. doi:10.14257/ijsip.2014.7.4.33
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Kaur, M., & Banga, V. K. (2013). Thresholding and Level Set Based Brain Tumor Detection Using Bounding Box as Seed. International Journal of Engineering Research & Technology, 2(4). Retrieved from https://www.ijert.org.
Kaus, M. R., Warfield, S. K., Nabavi, A., Chatzidakis, E., Black, P. M., Jolesz, F. A., & Kikinis, R. (1999). Segmentation of Meningiomas and Low Grade Gliomas in MRI. In Medical Image Computing and Computer-Assisted Intervention – MICCAI’99: Second International Conference, Cambridge, UK, September 19-22, 1999 (Vol. 1679, Lecture Notes in Computer Science, pp. 1-10). Springer Berlin Heidelberg.
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Saini, P., & Singh, M. (2015). Brain Tumor Detection in Medical Imaging Using MATLAB. International Research Journal of Engineering and Technology, 2, 2nd ser., 191-196. Retrieved from https://www.irjet.net.
Sawakare, S., & Chaudhari, D. (2014). Classification of Brain Tumor Using Discrete Wavelet Transform, Principal Component Analysis and Probabilistic Neural Network. International Journal for Research in Emerging Science and Technology, 1, 6th ser., 13-19. Retrieved from http://ijrest.net/.
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Tolba, M. F., Mostafa, M. G., Gharib, T. F., & Megeed, M. A. (2001). Medical Image Segmentation Using a Wavelet-Based Multiresolution EM Algorithm. IEEE International Conference on Industrial Electronics, Technology & Automation, IETA’2001, Cairo, 19th-21st Dec., 2001. Retrieved from http://www.academia.edu/25106125/Medical_Image_Segmentation_Using_a_Wavelet-Based_Multiresolution_EM_Algorithm.
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Prevalence and Outcomes of Electrolytes Deficiency in Children under Five with Diarrhea in Mwanza, Tanzania

doi:10.22186/jyi.33.2.46-48

Abstract | Introduction | Methods | Results | Discussion | Conclusions |Acknowledgements | 
References | PDF

Abstract

Dehydration from diarrhea leads to a loss of vital electrolytes in the body. The prevalence of electrolytes deficiency and its outcomes due to diarrhea among children under five in Mwanza, Tanzania was not clearly known, thus this study was performed to determine this statistic. A cohort study was conducted among 66 children less than five years old suffering from diarrhea attended and admitted to health centers in Mwanza, Tanzania. Vein puncture was performed to obtain peripheral blood, processed, and analyzed for two major electrolytes, Potassium and Sodium. This study was conducted because the loss of vital electrolytes (sodium and potassium) from diarrhea can be fatal if poorly treated. The median age of study participants was 1 year, ranged from 0.6 to 3 years. The prevalence of electrolytes deficiency in the cohort was determined to be 54.5%. Sodium deficiency (Hyponatremia) was the most prevalent (37.9%). After medication and oral rehydration therapy, all of the diarrheagenic children recovered, and those with electrolytes deficiency had their electrolytes balanced. Proper medication with oral rehydration therapy ensures complete recovery from diarrhea and electrolytes balance.

Introduction

Diarrhea is the passage of three or more loose or liquid stools per day; it may be a result of eating contaminated food and water or from food poisoning and is a common symptom of gastrointestinal infections caused by a wide range of pathogens, including bacteria, viruses and protozoa (Black et al., 1980; Kothari VR, & Thakur NA, 2014). Dehydration can be identified by experiencing dizziness, thirst, fatigue, infrequent urination and dark colored urine, nausea and headaches can leave the body without the electrolytes necessary for survival (Hirschhorn, 1980; Mackenzie, Barnes, & Shann, 1989). The electrolytes found in the body are potassium, calcium, sodium, magnesium, bicarbonate and chloride for cells functioning and signaling. According to WHO estimates; diarrheal disease is the leading cause of under-five mortality and is responsible for killing around 760,000 children every year (Kothari VR, & Thakur NA, 2014).
Epidemiological studies have shown that in developing countries, there are an estimated 1.3 billion episodes and 3.2 million deaths  of those under age five each year due to diarrhea (Rahman, 2014). Overall, these children experience an average of 3.3 episodes of diarrhea per year, but in some areas, primarily in developing countries, the average exceeds 9 episodes per year (Rahman, 2014). Where episodes are frequent, children may spend more than 15% of their days with diarrhea and about 80% of deaths that occur in the first two years of life are due to the condition. In developing countries 50% of pediatric hospitalizations  are due to acute diarrhea (Rahman, 2014).
A study carried out  by the BP Koirala Institute of Health Sciences in Dharan, Nepal that examined acid, base, and electrolyte disturbance in diarrhea showed 56% sodium  deficiency (hyponatremia), 46% potassium deficiency (hypokalemia) and 26% combined (hyponatremia and hypokalemia) (Shah, Das, Kumar, Singh, & Bhandari, 2007). The same study reported five children out of 57 had died due to electrolytes loss from diarrhea (Shah et al., 2007).
Statistical information about the prevalence of electrolyte deficiency and the outcomes among children under five years old with diarrhea that attended or was admitted to healthcare centers in Mwanza, Tanzania was not clearly known. We hypothesized that, vital electrolytes (sodium and potassium) are lost together with water due to excessive diarrhea among children under five years old. The outcome of vital electrolytes lost can be fatal, so this study was designed to guide management of children under five years old with diarrhea for early and complete recovery.

Materials and Methods

A cohort study was conducted between July and August 2016. All children less than five years old suffering from diarrhea who was admitted at the pediatric wards in Mwanza Healthcare centers and whose parents or guardians gave their consent to take part in this study were used in the study. A serial sampling method was used to determine eligibility of these study participants. About 2.5 to 5ml of two blood samples were collected from each participant and placed in a plain vacutainer tubes whereby the serum was extracted for electrolytes (sodium and potassium) analysis. Blood sample A was collected on the first day of participant admission or visit and sample B, as a follow up to sample A, was collected three days after administering oral rehydration solution (ORS) or antibiotics to all participants with electrolyte(s) [sodium and/or potassium] deficiency results.
Laboratory Procedure
Extracted sera were analyzed within two hours after specimen collection for sodium and potassium following the internal standard operating procedures, and as per reagents manufacturer guidelines for the SP Twin Electrolytes Test Kit (ARKRAY Healthcare Pvt. Ltd, India) in the Corolimeter manual analyzer (CL 157 Colorimeter).

Results

Demographic and Clinical Characteristics of Participants

During this study, a total of 66 children were enrolled. Out of those, 53.0% (35/66) were females. The median age of study participant was 1 (IQR: 0.6-3) year (Table 1).

Table 1. Demographic and clinical information of study participants.

Table 1. Demographic and clinical information of study participants.

Electrolyte Deficiency Results

The overall prevalence of electrolytes deficiency in diarrheagenic under-five children was 54.5% (36/66). Hyponatremia, hypokalemia and both (hyponatremia and hypokalemia) electrolytes deficiency were observed in 37.9% (25/66), 16.7% (11/66) and 15.2% (10/66) of the cohort respectively.

Factors Associated with Electrolytes Deficiency

Factors found to be connected with electrolytes deficiency among diarrheagenic under-five children in the bivariate analysis were 1) Present of symptoms like fever, vomiting and dehydration, 2) Duration of diarrhea, and 3) Diarrhea treatment. Children with symptoms such as fever, vomiting and dehydration showed electrolyte deficiency; 20/58 (34.5%) had hyponatremia, 10/58 (17.2%) had hypokalemia, and 8/58 (13.8%) had both (p = .707) (Table 2).
Considering the duration of diarrhea, most of children showed electrolyte deficiency during the early three up to seven days of diarrhea; 13/36 (36.1%) had hyponatremia, 6/36 (16.7%) had hypokalemia, and 8/36 (22.2%) had both hypokalemia and hyponatremia (p = .651).
Following diarrhea treatment with ORS, antibiotics or both, for those who did not received any treatment; 12/34 (35.3%) had hyponatremia, 4/34 (11.8%) had hypokalemia and 9/34 (26.5%) had both hypokalemia and hyponatremia (p = .031) (Table 2).

Table 2. Factors associated with electrolytes deficiency.

Table 2. Factors associated with electrolytes deficiency.

Electrolyte Deficiency Outcomes

Out of 66 diarrheagenic children, 40 recovered completely, 18 were still suffering from diarrhea and 8 were lost prior to follow-up (discharged or did not attend next visit) with no record of death. Among the cohort, 32 received management with antibiotics, ORS or both, whereby 62.5% (20/32) recovered completely from diarrhea (p = .0069). All diarrheagenic children who had recovered after treatment, 20/20 (100%) had balanced electrolytes from day three of follow up (Table 3).

Table 3. Outcome of electrolytes deficiency from day three after diarrhea treatment with ORS/antibiotics.

Table 3. Outcome of electrolytes deficiency from day three after diarrhea treatment with ORS/antibiotics.

Discussion

In this study, the prevalence of electrolytes deficiency among under-five diarrheagenic children was 54.5%, with the most prevalent depleted electrolyte being sodium (hyponatremia), 37.9% followed by potassium depletion (hypokalemia), 16.7%. This is comparable to another study done in Dharan, Nepal which found that the most prevalent depleted electrolyte among children with diarrhea was sodium (hyponatremia), 56% (Shah et al., 2007). Another study done in Nigeria in 2015 on serum electrolyte profiles in children admitted with dehydration due to diarrhea showed that hyponatremia and hypokalemia ranked first and second by 60.5% and 44.3% respectively (Onyiriuka, & Iheagwara, 2015). Sodium and potassium are the major lost electrolytes in diarrhea because they form intracellular and extracellular fluids respectively at the sodium-potassium pump (Skou, 1989).
The current study found that, electrolytes deficiency among diarrheagenic children to be associated with clinical symptoms like fever, duration of diarrhea and treatment type as previously reported (Bahl et al., 2002; Donowitz, Kokke, & Saidi, 1995; Thapar, & Sanderson, 2004; Weiner, & Epstein, 1970). The more episodes of diarrhea a child experiences, the greater amount of water and electrolytes are lost (Thapar, & Sanderson, 2004). Lack of treatment therapy to replace water and electrolytes results  in a high level of deficiency (Thapar, & Sanderson, 2004).
This study found that most of the children experienced electrolyte deficiency after suffering from diarrhea during the early three to seven days. This may be because during the early days they had not yet received treatment; thus, the results showed electrolytes had decreased. However, as the days went on they underwent treatment and their levels began to rise as ORS replaced the lost electrolytes. Diarrhea treatment either with ORS or antibiotics ensures recovery and a rise in electrolyte  levels (Hirschhorn, 1980). But for those children who received antibiotic treatment and ORS, still in diarrhea may be the exact aetiological cause of diarrhea was not bacteria (Black et al., 1980; Hirschhorn, 1980).
Most patients with electrolyte deficiency recovered completely after receiving treatment; those who had not received any treatment but recovered may have been affected by a bacterial toxin, in which case the diarrhea usually stops itself after some time (self-limiting) (Challapalli, Tess, Cunningham, Chopra, & Houston, 1988).

Conclusion

The current study found high prevalence of electrolytes deficiency, 54.5% among children under five years old with diarrhea. Electrolytes deficiency was connected with fever, vomiting and dehydration. The use of ORS as part of diarrhea management to replace water and the lost electrolytes is recommended. This study was unable to determine the aetiological causative agent of diarrhea among children under five years old. Further studies should attempt to determine the aetiological causative agent of diarrhea cases.

References

Bahl, R., Bhandari, N., Saksena, M., Strand, T., Kumar, G. T., Bhan, M. K., &
Sommerfelt, H. (2002). Efficacy of zinc-fortified oral rehydration solution in 6-to 35-month-old children with acute diarrhea. The Journal of Pediatrics, 141(5), 677-682.
Black, R. E., Merson, M., Rahman, A. M., Yunus, M., Alim, A. A., Huq, I., Curlin,
G. (1980). A two-year study of bacterial, viral, and parasitic agents associated with diarrhea in rural Bangladesh. Journal of Infectious Diseases, 142(5), 660-664.
Challapalli, M., Tess, B. R., Cunningham, D. G., Chopra, A. K., & Houston, C. W.
(1988). Aeromonas-associated diarrhea in children. The Pediatric Infectious Disease Journal, 7(10), 693-697.
Donowitz, M., Kokke, F. T., & Saidi, R. (1995). Evaluation of patients with chronic
 diarrhea. New England Journal of Medicine, 332(11), 725-729.
Hirschhorn, N. (1980). The treatment of acute diarrhea in children. An historical
and physiological perspective. The American Journal of Clinical Nutrition, 33(3), 637-663.
Hsiao, A. L., & Baker, M. D. (2005). Fever in the new millennium: a review of
recent studies of markers of serious bacterial infection in febrile children. Current Opinion in Pediatrics, 17(1), 56-61.
Mackenzie, A., Barnes, G., & Shann, F. (1989). Clinical signs of dehydration in
 children. The Lancet, 334(8663), 605-607.
Onyiriuka, A., & Iheagwara, E. (2015). Serum electrolyte profiles of under-five
Nigerian children admitted for severe dehydration due to acute diarrhea. Nigerian Journal of Health Sciences, 15(1), 14.
Rahman, H. (2014). Molecular characterization of Necrotoxigenic Escherichia Coli
NTEC of man and animals.
Shah, G., Das, B., Kumar, S., Singh, M., & Bhandari, G. (2007). Acid base and
electrolyte disturbance in diarrhoea.
Skou, J. C. (1989). Sodium-potassium pump Membrane transport (pp. 155-185):
Springer.
Thapar, N., & Sanderson, I. R. (2004). Diarrhoea in children: an interface between
 developing and developed countries. The Lancet, 363(9409), 641-653.
VR, Kothari., & NA, Thakur. (2014). A Cross Sectional Study of Risk Factors for
Development of Dehydration in Children under 5 Years Having Acute Watery Diarrhea.
Weiner, M., & Epstein, F. (1970). Signs and symptoms of electrolyte disorders. The
 Yale Journal of bBology and Medicine, 43(2), 76.

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Varying Sugars and Sugar Concentrations Influence In Vitro Pollen Germination and Pollen Tube Growth of Cassia alata L.

doi:10.22186/jyi.33.1.42-45

Abstract | Introduction | Methods | Results | Discussion | Conclusions |Acknowledgements | 
References | PDF

Abstract

This study investigates the effects of varying sugars and sugar concentrations on the in vitro germination and tube growth of pollens of Cassia alata L., a known Philippine ornamental and medicinal plant. This aims to add information on the pollination fertilization mechanism of the plant for its possible extensive cultivation. Using a pollination germination medium with different sugar concentrations (2.5, 5.0, 7.5 and 10.0%), pollen germination and pollen tube growth is highly influenced by all sucrose concentrations and by certain glucose (2.5%) and lactose (2.5 and 7.5%) concentrations. Maltose and fructose, on the other hand, are determined to be inhibitory sugars for pollen germination.

Introduction

The total count of pollen grains on a stigma usually surpasses the number required to fertilize all ovules; thus, the process of pollen growth in the carpel is highly competitive (Okusaka & Hiratsuka, 2009). In higher plants, the elongation of pollen tube is extremely fast making the pollen tube the plant cell with the fastest growth rate. Accordingly, this swift growth of pollen tubes is essential for male reproductive success (Okusaka, & Hiratsuka, 2009) and for the subsequent plant development.
Pollen development and tube growth (due to its high growth rate) are high energy-requiring processes (Selinski, & Scheibe, 2014). Carbohydrates act as energy source during the two processes (Okusaka, & Hiratsuka, 2009). The storage compounds and sugars stored in mature pollen can adequately sustain survival of pollen and germination; however, the rapid pollen tube elongation requires secretions of carbohydrates (exogenous sugars) from the stylar canal to proceed (Reinders, 2016). Exogenous sugars also provide and maintain suitable osmotic environment not only for germination of pollen but also for sustained pollen tube growth (Baloch & Lakho, 2001).
Most of the studies conducted on C. alata L. are on its therapeutic properties. Leaves of C. alata L. contain anthraquinone derivatives which exhibit antimicrobial, antitumor, antioxidant, cytotoxic and hypoglycemic activities (Alalor, Igwilo, & Jeroh, 2012). Crude extracts of the plant are being used to treat various skin diseases (Balinado, & Chan, 2017) and are effective against Staphylococcus aureus and Bacillus subtilis (Alalor, Igwilo, & Jeroh, 2012). Also, C. alata L. based soap was proven effective against opportunistic yeasts (Esimone, 2007).
Preliminary investigation of the developmental morpho-anatomy of the male gametophyte of C. alata L. was already conducted (Tolentino, 2011), but limited information is known regarding its sugar metabolism and investigating this will immensely contribute to the extensive cultivation of the plant taking into consideration its medicinal properties. This study, therefore, would add light to the developmental biology of C. alata particularly to its pollen germination and pollen tube growth.
The study specifically aims to determine the effect of varying sugars and sugar concentrations on the in vitro pollen germination and tube growth of C. alata by calculating the germination percentage and measuring the pollen tube length after exposure to different sugars. In numerous studies on in vitro pollen germination of different plant species, sucrose exhibited strong stimulatory effects (Baloch, & Lakho, 2001; Patel, 2017; Zhang, & Croes, 1982), together with glucose and lactose (Ismail, 2014); thus, may also promote pollen germination in C. alata. Maltose and fructose, on the other hand, were reported to have varied effects on pollen germination of various plant species (Ismail, 2014; Okusaka, & Hiratsuka, 2009; Nakamura, & Suzuki, 1985).  

Methods

Pollen Collection

Cassia alata L. flowers at anthesis were collected randomly from Cavite State University, Indang, Cavite during daytime. Flowers were immediately transported to the Department of Biological Sciences of the same institution for the conduct of the experiment. Pollen grains were collected by carefully tapping and brushing the anthers of each flower on a clean petri dish.

Preparation of Pollen Germination Medium

A Brewbaker and Kwack medium was used as pollen germination medium. It was composed of 100mg 1-1 boric acid, 200mg 1-1 magnesium sulfate, 100mg 1-1potassium nitrate, 300-mg 1-1 calcium nitrate, 1% agar and sugars (Jayaprakash, & Sarla, 2000). Five sugars were utilized, namely; fructose, glucose, lactose, maltose and sucrose. For each sugar, four different concentrations were prepared: 2.5%, 5.0%, 7.5%, and 10.0%. A medium with no sugar added was used as negative control. The resulting medium was finally autoclaved to maintain sterility.

Preparation of a Humid Chamber and Germination Slides

A filter paper was placed in each petri dish before pouring distilled water sufficient enough to obtain a moist environment for the pollen. A glass slide with several (two to three) drops of hot liquid pollen germination medium at the center was then placed in each petri dish. This allowed the agar to completely cool and harden. With the aid of a nylon brush, pollen grains were transferred onto the solidified agar medium. Resulting petri dishes were then incubated in the dark for a total of three hours. This was performed in triplicate.

Observation of Pollen Germination and Pollen Tube Growth

Observation for signs of pollen germination and pollen tube growth was done by microscopy thrice at one-hour interval. A single field of view per replicate that contained at least 30 solitary pollen grains was observed and photographed. A pollen grain was considered germinated when its tube length doubled the diameter of the pollen grain.  The total number of pollens that germinated was determined and percent germination was calculated using the following formula.

VS equation 1 (1)

Pollen tube lengths were then measured (in μm) with the aid of ImageJ free software using the images obtained from microscopy.

Statistical Treatment

Descriptive statistics, such as means and percentages, were utilized in determining pollen germination percentage and mean pollen tube lengths. One-way Analysis of Variance (ANOVA) was used to determine the significant differences in pollen tube growth among sugar concentrations.

Results

Examination of Pollen Germination

Humid chambers containing germination slides with pollen grains were incubated for a total of three hours. The total number of germinated pollen grains per sugar concentration was obtained as shown in Figure 1. As presented, pollen grains only germinated in media containing glucose (i.e. 2.50%) and lactose (i.e. 2.50% and 7.50%) and all concentrations of sucrose (with 100% germination in 5.00-10.00% sucrose concentrations). On the other hand, germination was not observed in solitary pollen grains exposed to fructose and maltose.

Figure 1. Mean number of germinated pollen grains per sugar concentration after 3 h of incubation.

Figure 1. Mean number of germinated pollen grains per sugar concentration after 3 h of incubation.

As shown in Table 1 and Figure 2, pollen tube growth of C. alata L. was only observed in 2.5- and 7.5-% lactose concentrations. Increase in pollen tube length under 7.5-% concentration was found to be directly proportional to increasing time of incubation and was significantly different from other concentrations.

Table 1. Mean pollen tube lengths (in μm) in response to increasing lactose concentrations.

Table 1. Mean pollen tube lengths (in μm) in response to increasing lactose concentrations.

 

Figure 2. Graph showing mean pollen tube lengths (in μm) in response to increasing lactose concentrations.

Figure 2. Graph showing mean pollen tube lengths (in μm) in response to increasing lactose concentrations.

In addition, as presented in Table 2 and Figure 3, C. alata pollens only responded to 2.5-% glucose concentration. Pollen tube lengths increased as incubation time also lengthened. No pollen tube length was observed in glucose concentrations higher than 2.5%.

Table 2. Mean pollen tube lengths (in μm) in response to increasing glucose concentrations.

Table 2. Mean pollen tube lengths (in μm) in response to increasing glucose concentrations.

 

Figure 3. Mean pollen tube lengths (in μm) in response to increasing glucose concentrations.

Figure 3. Mean pollen tube lengths (in μm) in response to increasing glucose concentrations.

Varying sucrose concentrations differently influenced pollen tube growth, results were statistically significant (Table 3, Figure 4). In all concentrations, an increase in pollen tube length was observed in response to increasing time of incubation. A representative photograph of pollen tube growth on germination medium with sucrose is shown in Figure 5.

Table 3. Mean pollen tube lengths (in μm) in response to increasing sucrose concentrations.

Table 3. Mean pollen tube lengths (in μm) in response to increasing sucrose concentrations.

 

Figure 4. Mean pollen tube lengths (in μm) in response to increasing sucrose concentrations.

Figure 4. Mean pollen tube lengths (in μm) in response to increasing sucrose concentrations.

 

Figure 5. Pollen germination on sucrose.

Figure 5. Pollen germination on sucrose.

Discussion

Varying sugars and sugar concentrations differently influenced pollen germination and pollen tube growth of C. alata L. Pollens successfully germinated in the sugar sucrose and acted more effectively than glucose and lactose; while fructose and maltose strongly inhibited germination on agar medium.
The observation that glucose permitted pollen tube growth could be explained by the fact that glucose is natural pollen constituent, together with other sugars, such as arabinose and galactose (Loo & Hwan, 1944). This sugar acts as an essential signaling molecule that controls plant growth and development and gene expression (Zhou et al., 1998). In addition, the effect of lactose in this study was similarly reported by Bishop (2009) and Ismail (2014). Most significant pollen tube growth on lactose compared to other sugars was also observed by Takao et al. (2006). Bishop (2009) even suggested that a higher concentration of lactose could be used as substitute for the normally used sucrose. The positive influence of sucrose to pollen germination and growth, on the other hand, could be attributed to the condition it provides to pollen that is similar to the condition of the stigmatic tissue of a flower; this stigma that secretes a fluid substance to rehydrate the pollen (Zhang, & Croes, 1982). Sucrose is the most common sugar form found in the translocation stream and is transported to other non-photosynthetic plant tissues, such as flowers, for direct metabolic use (Hopkins, & Huner, 2009).The growth of pollen tube on sugar-free medium, in addition, could be attributed to the use of endogenous carbohydrates of the pollen without the influence—be it stimulatory or inhibitory—of other sugars present in the medium.
Similarly to the results obtained by Nakamura and Suzuki (1985), maltose strongly inhibited pollen tube growth in Camella japonica. Okusaka and Hiratsuka (2009), in addition, reported that fructose causes pollen inhibition. It was suggested that the pollen on fructose medium predominantly uses other sugars (e.g. sucrose and glucose) as respiration substrates and cannot maintain the constant level of these sugars.
This study reveals that different sugars have a considerable influence on pollen germination and pollen tube growth in C. alata L. Pollen tube growth is influenced by glucose, lactose and sucrose sugars; the latter being the most effective. Maltose and fructose were, on the other hand, found inhibitory of germination. This study therefore adds information on the developmental biology of pollens of C. alata L., a known ornamental and medicinal plant in the Philippines, which can further be used for its extensive cultivation in the country.

Acknowledgement

The researchers would like to acknowledge with deep and warm gratitude the Department of Biological Sciences, College of Arts and Sciences, Cavite State University for the laboratory materials and equipment used in the study.

References

Okusaka, K., & Hiratsuka, S. (2009). Fructose inhibits pear pollen germination on agar medium without loss of viability. Scientia Horticulturae, 122(1):51-55. doi:10.1016/j.scienta.2009.03.024
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