Browsing by Author "Vijayan, Sujith"
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- Analyzing and Classifying Neural Dynamics from Intracranial Electroencephalography Signals in Brain-Computer Interface ApplicationsNagabushan, Naresh (Virginia Tech, 2019-06-14)Brain-Computer Interfaces (BCIs) that rely on motor imagery currently allow subjects to control quad-copters, robotic arms, and computer cursors. Recent advancements have been made possible because of breakthroughs in fields such as electrical engineering, computer science, and neuroscience. Currently, most real-time BCIs use hand-crafted feature extractors, feature selectors, and classification algorithms. In this work, we explore the different classification algorithms currently used in electroencephalographic (EEG) signal classification and assess their performance on intracranial EEG (iEEG) data. We first discuss the motor imagery task employed using iEEG signals and find features that clearly distinguish between different classes. Second, we compare the different state-of-the-art classifiers used in EEG BCIs in terms of their error rate, computational requirements, and feature interpret-ability. Next, we show the effectiveness of these classifiers in iEEG BCIs and last, show that our new classification algorithm that is designed to use spatial, spectral, and temporal information reaches performance comparable to other state-of-the-art classifiers while also allowing increased feature interpret-ability.
- Automated Rat Grimace Scale for the Assessment of PainArnold, Brendan Elliot (Virginia Tech, 2023-06-21)Pain is a complex neuro-psychosocial experience that is internal and private making it difficult to assess in both humans and animals. In research approximately 95% of animal models use rodents, with rats being among the most common for pain studies [3]. However, traditional assessments of the pain response struggle to demonstrate that the behaviors are a direct measurement of pain. The rat grimace scale (RGS) was developed based on facial action coding systems (FACS) which have known utility in non-verbal humans [6, 9]. The RGS measures facial action units of orbital tightening, ear changes, nose flattening, and whisker changes in an attempt to quantify the pain behaviors of the rat. These action units are measured on frontal images of rats with their face in clear view on a scale of 0-2, then summed together. The total score is then averaged to find a final value for RGS between 0-2. Currently, the software program Rodent Face Finder® can extract frontal face images. However, the RGS scores are still manually recorded which is a labor-intensive process, requiring hours of training. Furthermore, the scoring can be subjective, with differences existing between researchers and lab groups. The primary aim of this study is to develop an automated system that can detect action unit regions and generate a RGS score for each image. To accomplish this objective, a YOLOv5 object detector and Vision Transformers (ViT) for classification were trained on a dataset of frontal-facing images extracted using Rodent Face Finder®. Subsequently, the model was then validated using a RGS test for blast traumatic brain injury (bTBI). The validation dataset consisted of 40 control images of uninjured rats, 40 images from the bTBI study on the day of injury, and 40 images 1-month post-injury. All 120 images in the validation set were then manually graded for RGS and tested using the automated RGS system. The results indicated that the automated RGS system accurately and efficiently graded the images with minimal variation in results compared to human graders in just 1/14th of the time. This system provides a fast and reliable method to extract meaningful information of rats' internal pain state. Furthermore, the study presents an avenue for future research into real-time pain monitoring.
- Cortical circuit-based lossless neural integrator for perceptual decision-making: A computational modeling studyLee, Jung Hoon; Tsunada, Joji; Vijayan, Sujith; Cohen, Yale E. (Frontiers, 2022-11)The intrinsic uncertainty of sensory information (i.e., evidence) does not necessarily deter an observer from making a reliable decision. Indeed, uncertainty can be reduced by integrating (accumulating) incoming sensory evidence. It is widely thought that this accumulation is instantiated via recurrent rate-code neural networks. Yet, these networks do not fully explain important aspects of perceptual decision-making, such as a subject's ability to retain accumulated evidence during temporal gaps in the sensory evidence. Here, we utilized computational models to show that cortical circuits can switch flexibly between "retention" and "integration" modes during perceptual decision-making. Further, we found that, depending on how the sensory evidence was readout, we could simulate "stepping" and "ramping" activity patterns, which may be analogous to those seen in different studies of decision-making in the primate parietal cortex. This finding may reconcile these previous empirical studies because it suggests these two activity patterns emerge from the same mechanism.
- Design and Control of an Ergonomic Wearable Full-Wrist Exoskeleton for Pathological Tremor AlleviationWang, Jiamin (Virginia Tech, 2023-01-31)Activities of daily living (ADL) such as writing, eating, and object manipulation are challenging for patients suffering from pathological tremors. Pathological tremors are involuntary, rhythmic, and oscillatory movements that manifest in limbs, the head, and other body parts. Among the existing treatments, mechanical loading through wearable rehabilitation devices is popular for being non-invasive and innocuous to the human body. In particular, a few exoskeletons are developed to actively mitigate pathological tremors in the forearm. While these forearm exoskeletons can effectively suppress tremors, they still require significant improvements in ergonomics to be implemented for ADL applications. The ergonomics of the exoskeleton can be improved via design and motion control pertaining to human biomechanics, which leads to better efficiency, comfort, and safety for the user. The wrist is a complicated biomechanical joint with two coupled degrees of freedom (DOF) pivotal to human manipulation capabilities. Existing exoskeletons either do not provide tremor suppression in all wrist DOFs, or can be restrictive to the natural wrist movement. This motivates us to explore a better exoskeleton solution for wrist tremor suppression. We propose TAWE - a wearable exoskeleton that provides alleviation of pathological tremors in all wrist DOFs. The design adopts a 6-DOF rigid linkage mechanism to ensure unconstrained natural wrist movements, and wearability features without extreme tight-binding or precise positioning for convenient ADL applications. When TAWE is equipped by the user, a closed-kinematic chain is formed between the exoskeleton and the forearm. We analyze the coupled multibody dynamics of the human-exoskeleton system, which reveals a few robotic control problems - (i) The first problem is the identification of the unknown wrist kinematics within the closed kinematic chain. We realize the real-time wrist kinematic identification (WKI) based on a novel ellipsoidal joint model that describes the coupled wrist kinematics, and a sparsity-promoting Extended Kalman Filter for the efficient real-time regression; (ii) The second problem is the exoskeleton motion control for tremor suppression. We design a robust adaptive controller (IO-RAC) based on model reference adaptive control and inverse optimal robust control theories, which can identify the unknown model inertia and load, and provide stable tracking control under disturbance; (iii) The third problem is the estimation of voluntary movement from tremorous motion data for the motion planning of exoskeleton. We develop a lightweight and data-driven voluntary movement estimator (SVR-VME) based on least square support vector regression, which can estimate voluntary movements with real-time signal adaptability and significantly reduced time delay. Simulations and experiments are carried out to test the individual performance of robotic control algorithms proposed in this study, and their combined real-time performance when integrated into the full exoskeleton control system. We also manufacture the prototype of TAWE, which helps us validate the proposed solutions in tremor alleviation exoskeletons. Overall, the design of TAWE meets the expectations in its compliance with natural wrist movement and simple wearability. The exoskeleton control system can execute stably in real-time, identify unknown system kinematics and dynamics, estimate voluntary movements, and suppress tremors in the wrist. The results also indicate a few limitations in the current approaches, which require further investigations and improvements. Finally, the proposed exoskeleton control solutions are developed based on generic formulations, which can be applied to not only TAWE, but also other rehabilitation exoskeletons.
- Employing Intracranial EEG Data to Decipher Sleep Neural DynamicsKvavilashvili, Andrew Tomaz (Virginia Tech, 2023-01-24)Over the course of a typical night, sleep is comprised of multiple different stages that involve changes in brainwave patterns. Intracranial EEG (iEEG) is an invasive brain recording technique used in hospital settings in epileptic patients to determine the focus of their seizure activity. The intracranial data recorded allows one to directly observe the neural activity of deep brain structures such as the hippocampus and to detect single unit activity and local field potentials, thus providing a level of physiological detail normally available only in animal studies. In this thesis we employ intracranial data to advance our understanding of sleep neural dynamics in humans, and to this end its focus is in two areas : (1) developing a way of sleep scoring iEEG data and (2) investigating the neural dynamics of a particular waveform found during sleep, the sleep spindle, and its role in memory consolidation. Typically, iEEG recordings do not include electrooculogram or electromyogram recordings, which are normally needed for sleep scoring—especially for scoring rapid-eye movement (REM) sleep. We identified differences in alpha power between wake and REM sleep to develop a methodological way to reliably differentiate between wake and REM sleep states. We also wanted to investigate the neural dynamics involved with a particular brainwave seen during sleep, the sleep spindle, which is thought to be important for sleep-mediated memory consolidation. Historically, sleep spindles were thought to occur synchronously across the cortex, but recent findings using iEEG have identified that sleep spindles can also be local. We utilized intracranial EEG to confirm previous findings that iEEG can identify local sleep spindles. In addition to identifying local sleep spindles, we aimed to investigate the potential role that sleep spindles have on learning and memory using standard targeted memory reactivation paradigms for iii both procedural and declarative memories. We found that local sleep spindles occurred at a specific time following auditory stimulation for both procedural and declarative memories. This work has opened up the use of iEEG recordings to investigations of REM sleep dynamics and laid the groundwork for examining the role of local sleep spindles in memory consolidation.
- Extracting Feature Vectors From Event-Related fMRI Data to Enable Machine Learning AnalysisSoldate, Jeffrey S. (Virginia Tech, 2022-10-05)Linear models are the dominant means of extracting summaries of events in fMRI for feature vector based machine learning. While they are both useful and robust, they are limited by the assumptions made in modeling. In this work, we examine a number of feature extraction techniques adjacent to linear models that account for or allow wider variation. Primarily, we construct mixed effects models able to account for variation between stimuli of the same class and perform empirical tests on the resulting feature extraction – classifier system. We extend this analysis to spatial temporal models as well as summary models. We find that mixed effects models increase classifier performance at the cost of increased uncertainty in prediction estimates. In addition, these models identify similar regions of interest in separating classes. While they currently require knowledge hidden during testing, we present these results as an optimum to be reached in additional works.
- Frontal beta-theta network during REM sleepVijayan, Sujith; Lepage, Kyle Q.; Kopell, Nancy J.; Cash, Sydney S. (eLife Sciences Publications, 2017-01-25)We lack detailed knowledge about the spatio-temporal physiological signatures of REM sleep, especially in humans. By analyzing intracranial electrode data from humans, we demonstrate for the first time that there are prominent beta (15–35 Hz) and theta (4–8 Hz) oscillations in both the anterior cingulate cortex (ACC) and the DLPFC during REM sleep. We further show that these theta and beta activities in the ACC and the DLPFC, two relatively distant but reciprocally connected regions, are coherent. These findings suggest that, counter to current prevailing thought, the DLPFC is active during REM sleep and likely interacting with other areas. Since the DLPFC and the ACC are implicated in memory and emotional regulation, and the ACC has motor areas and is thought to be important for error detection, the dialogue between these two areas could play a role in the regulation of emotions and in procedural motor and emotional memory consolidation.
- Granular retrosplenial cortex layer 2/3 generates high frequency oscillation events coupled with hippocampal sharp wave-ripples and Str. LM high gammaArndt, Kaiser C. (Virginia Tech, 2024-06-11)Encoding and consolidation of memories are two processes within the hippocampus, and connected cortical networks, that recruit different circuit level dynamics to effectively process and pass information from brain region to brain region. In the hippocampal CA1 pyramidal layer local field potential (LFP), these processes take the form of theta and sharp wave ripples (SPW-Rs) for encoding and consolidation, respectively. As an animal runs through an environment, neurons become active at specific locations in the environment (place cells) increasing their firing rate, functionally representing these specific locations. These firing rate increases are organized within the local theta oscillations and sequential activation of many place cells creates a map of the environment. Once the animal stops moving and begins consummatory behaviors, such as eating, drinking, or grooming, theta activity diminishes, and large irregular activity (LIA) begins to dominate the LFP. Spontaneously, with the LIA, the place cells active during the experience are replayed during SPW-Rs in the same spatial order they were encountered in the environment. Both theta and SPW-R oscillations and their associated neuronal firing are necessary for effective place recognition as well as learning and memory. As such, interruption or termination of SPW-R events results in decreased learning performance over days. During exploration, the associated theta and sequential place cell activity is thought to encode the experience. During quiet restfulness or slow wave sleep (SWS), SPW-R events, that replay experience specific place sequences, are thought to be the signal by which systems consolidation progresses and the hippocampus guides cortical synaptic reorganization. The granular retrosplenial cortex (gRSC) is an associational area that exhibits high frequency oscillations (HFOs) during both hippocampal theta and SPW-Rs, and is potentially a period when the gRSC interprets incoming content from the hippocampus during encoding and systems consolidation. However, the precise laminar organization of synaptic currents supporting HFOs, whether the local gRSC circuitry can support HFOs without patterned input, and the precise coupling of hippocmapla oscillations to gRSC HFOs across brain states remains unknown. We aimed to answer these questions using in vivo, awake electrophysiological recordings in head-fixed mice that were trained to run for water rewards in a 1D virtual environment. We show that gRSC synaptic currents supporting HFOs, across all awake brain states, are exclusively localized to layer 2/3 (L2/3), even when events are detected within layer 5 (L5). Using focal optogenetics, both L2/3 and L5 can generate induced HFOs given a strong enough broad stimulation. Spontaneous gRSC HFOs occurring outside of SPW-Rs are highly comodulated with medial entorhinal cortex (MEC) generated high gamma in hippocampal stratum lacunosum moleculare. gRSC HFOs may serve a necessary role in communication between the hippocampus during SPW-Rs states and between the hippocampus, gRSC, and MEC during theta states to support memory consolidation and memory encoding, respectively.
- The influence and manipulation of resting-state brain networks in alcohol use disorderMyslowski, Jeremy Edward (Virginia Tech, 2024-01-25)Alcohol use disorder is common, and treatments are currently inadequate. Some of the acute effects of alcohol on the brain, such as altering the decision-making and future thinking capacities, mirror the effects of chronic alcohol use. Therefore, interventions that can address these shortcomings may be useful for reducing the negative effects of alcohol use disorder in combination with other therapies. The signature of those interventions may also be evident in the signature of large-scale, dynamic brain networks, which can show whether an intervention is effective. One such intervention is episodic future thinking, which has been shown to reduce delay discounting and orient people toward pro-social, long-term outcomes. To better understand decision making in high-risk individuals, we examined delay discounting in an adolescent population. When the decision-making faculties were challenged with difficult choices, adolescents made decisions inconsistent with their predicted preference, complemented by increased brain activity in the central executive network and salience network. Using these results and the hypothesis that the default mode network would be implicated in future thinking and intertemporal choice, we examined the neural effects of a brief behavioral intervention, episodic future thinking, that seeks to address these impairments. We showed that episodic future thinking has both acute and longer-lasting effects on consequential brain networks at rest and during delay discounting compared to a control episodic thinking condition in alcohol use disorder. Our failure to show group differences in default mode network prompted us to scrutinize it more carefully, from a position where we could measure the ability to self-regulate the network rather than its resting-state tendency. We implemented a real-time fMRI experiment to test the degree to which people along the alcohol use severity spectrum can self-regulate this network. Our results showed that default mode network suppression is impaired as alcohol use disorder severity increases. In the process, we showed that direct examination of resting-state networks with these methods will provide more information than measuring them at rest alone. We also characterized the default mode network along the real-time fMRI pipeline to show the whole-brain spatial pattern of regions associated and unassociated with the network. Our results indicate that resting-state brain networks are important markers for outcomes in alcohol use disorder and that they can be manipulated under experimental conditions, potentially to the benefit of the afflicted individual.
- Inhibition, Synapses, and Spike-Timing: Identification and disruption of pyramidal cell-interneuron interactions in SPW-Rs.Gilbert, Earl Thomas (Virginia Tech, 2024-06-25)The neural circuitry responsible for memory consists of complex components with dynamic interactions. In hippocampal area CA1, interactions between excitatory pyramidal cells and inhibitory interneurons shape ensemble activity which encodes sequential experience. An extremely diverse set of inhibitory interneurons, with variation in gene expression, synaptic targeting, state-dependent activity, and connectivity, contribute substantially to circuit activity, such as theta and sharp wave-ripple oscillations. The precise roles of each interneuron group is not well understood, though characterization of their activity reveals mechanisms underlying hippocampal circuit computation. In this dissertation, I aim to identify and disrupt interactions between pyramidal cells and local interneurons to clarify their role in shaping cell assembly activity. We characterized axo-axonic cell activity in sharp wave-ripples, and compared their control of pyramidal cell activity and ripple events to parvalbumin expressing neurons. We identified pyramidal cell-interneuron interactions during ripples, suggesting they serve as lateral inhibitors between cell assemblies. We additionally developed and implemented a novel neural device to explore the role of cannabinoid disruption of hippocampal oscillations and organization of assemblies in vivo in awake animals. We demonstrate that cannabinoid receptor type 1 within CA1 is responsible for suppression of theta and SPW-Rs. We also found that cannabinoid activation within CA1 circuitry, regardless of muted input from CA3, was sufficient to disrupt sharp wave-ripples, likely through interference of pyramidal cell-interneuron interactions. The work in this dissertation provides insight suggesting that interneuron activity must be studied at the spiking timescale to characterize their control over cell assembly activity.
- Investigation of Sleep Neural Dynamics in Intracranial EEG PatientsJain, Sparsh (Virginia Tech, 2021-06-01)Intracranial electroencephalography (iEEG) provides superior diagnostic and research benefits over non-invasive EEG in terms of spatial resolution and the level of electrophysiological detail. Post-operative Computed Tomography (CT) scans provide the precision in electrode localization required for clinical purposes; however, to use this data for basic sleep research the challenge lies in identifying the precise locations of the implanted electrodes’ recording sites in terms of neuroanatomical regions as well as reliable scoring of their sleep data without the aid of facial electrodes. While existing methods can be combined to determine their exact locations in three-dimensional space, they fail to identify the functionally relevant gray matter areas that lie closest to them, especially if the points lie in the white matter. We introduce an iterative sphere inflation algorithm in conjunction with a unified pipeline to detect the exact as well as nearest regions of interest for these recording sites. Next, for sleep scoring purposes, we establish differences observed in alpha band activity between wakefulness and rapid eye movement (REM) sleep in frontal and temporal regions of iEEG patients. Lastly, we implement an automated sleep scoring method relying on the variations in alpha and delta bands power during sleep which can be applied to large sets of iEEG data recorded without accompanying electrooculogram (EOG) and electromyogram (EMG) electrodes available across labs for use in studies pertaining to neural dynamics during sleep.
- Lateral Parabrachial Choline Acetyltransferase Neurons and the Decision to EatTatera, Walter James (Virginia Tech, 2023-06-13)Food choice is a modifiable health factor which involves neural, hormonal, and metabolic signals. The lateral parabrachial nucleus is a brain structure in the pons that integrates multiple aspects of food choice. It receives information from the homeostatic melanocortin hypothalamic system and projects to the mesolimbic dopamine reward system. The lateral parabrachial is molecularly diverse and expresses the acetylcholine synthesis enzyme: choline acetyltransferase (ChAT). In this study, we used ChAT-CRE mice to investigate the anatomical projections, the calcium dynamics, and the causal role of the LPBN ChAT neurons. Anatomical projection results were produced using CRE dependent viral vectors to express mRuby tagged synaptophysin, the highest output being the central amygdala. Calcium dynamics were measured at the amygdala using the genetically encoded calcium indicator GCaMP. The dynamics around the decision to consume food were seen to be different between fasted and sated satiety states. Activation of the circuit showed a pronounced latency to food consumption and time to finish for a single calorie of food. These data demonstrate a possible node that integrates homeostatic feeding information and relays it to higher order brain systems that modify reward value.
- On the Analysis of Mouse BehaviorMurdaugh, Laura Bethany (Virginia Tech, 2024-01-16)Accurate and high throughput methods of measuring animal behavior are critical for many branches of neuroscience, allowing for mechanistic studies and preclinical drug testing. Methodological limitations contribute to narrow investigations, which may overlook the interplay between distinct but related behaviors, like affective behaviors and executive function (EF). To prevent such oversight, researchers can perform test batteries, or multiple assessments in one study. However, test batteries often exclude cognitive behaviors due to their lengthy testing period. This dissertation first reviews current evidence related to the investigation and relation of affective, pain-like, and operant behaviors in rodent models. Then, I demonstrate the use of traditional and novel test batteries to investigate these behavioral changes in multiple mouse models. First, I investigated affective and pain-like behavior in mice lacking Nape-pld, a key enzyme that synthesizes lipid mediators which activate receptors in the endocannabinoid system. I found that these mice displayed reduced sucrose preference, but otherwise normal anxiety- and depression-like behavior, and had baseline differences in thermal nociception and inflammation response. Then, I investigated the affective, pain-like, and operant effects of chronic vapor exposure (CVE) to vehicle or nicotine (NIC). Regardless of NIC content, acute abstinence from CVE increased mechanical sensitivity and self-grooming, while chronic abstinence from NIC CVE resulted in motor stimulation. Other traditional anxiety- and depression-like behaviors were unchanged by CVE. In an operant test battery, acute abstinence from NIC CVE impaired acquisition, decreased sucrose motivation, and impaired the response to aversive rewards. Finally, I developed a protocol for the high throughput analysis of six operant tests which can be completed in as few as nineteen sessions, significantly fewer sessions than traditional operant tests. This battery investigates multiple aspects of goal-directed behavior and EF including operant acquisition, cognitive flexibility, reward devaluation, motivation via response to increased instrumental effort, cue devaluation or the extinction of learned behavior, and reacquisition. I validated several of these tests by demonstrating that lesions to specific subregions of the orbitofrontal cortex impaired cognitive flexibility and altered response to instrumental effort as observed in traditional operant tests. I then used this battery to investigate the effects of the P129T mutation, which results in a mutated version of the Fatty Acid Amide Hydrolase (FAAH) enzyme that is associated with addiction, in male and female mice. Knock-in animals displayed reduced activity in response to increasing instrumental effort, and reduced activity on the first day of an extinction test. Then, to encourage others to use this new operant battery I outlined how to efficiently collect data, shared a database for customizable analysis, and described common issues and how to solve them. This protocol has potential implications for many aspects of neuroscience including the investigation of novel therapeutics and the neural circuitry underlying behaviors. Together, the information in this dissertation demonstrates the utility of multi-faceted behavioral assays and the combination of traditional and novel approaches to collect more comprehensive behavioral data, which will allow researchers to better investigate neural circuitry underlying behaviors or the behavioral changes associated with novel therapeutics.
- The Role of CASK in Central Nervous System Function and DisorderPatel, Paras Atulkumar (Virginia Tech, 2022-05-25)Understanding how different regions of the central nervous system (CNS) are affected by genetic insults is critical to advancing the study of CNS pathologies. The cerebellum is one such region which is disproportionately hypoplastic in the majority of cases of CASK gene mutation in humans. CASK is an enigmatic multi-domain scaffolding protein which plays a vital role in organizing protein complexes at the pre-synapse through interactions with both active zone proteins and trans-synaptic adhesion molecules such as liprins-α and neurexins. Mutations in the X-linked CASK gene in humans are largely post-natally lethal in the hemizygous condition and result in microcephaly with pontine and cerebellar hypoplasia (PCH) and also optic nerve hypoplasia (ONH) in heterozygous mutations. Herein, I used various molecular and genetic strategies to uncover the role of the CASK protein in brain function and pathogenesis of cerebellar hypoplasia associated with CASK mutations/deletions. First, using the face- and construct-validated heterozygous CASK knockout (Cask+/-) murine model, I conducted bulk RNA-sequencing and proteomics experiments from whole brain lysates to uncover changes in the Cask+/- brain. RNA-sequencing revealed the majority of changes to be broadly categorized into metabolic, nuclear, synaptic, and extracellular-matrix associated transcripts. Proteomics revealed the majority of changes cluster as synaptic proteins, metabolic proteins, and ribosomal subunits. Thus, absence of CASK in half of brain cells seems to affect synaptic protein content, cell metabolism, and protein homeostasis. Extending these observations, I conducted GFP-trap immunoprecipitation followed by tandem mass spectroscopy to reveal protein complexes in which CASK participates. Commensurate with proteomic changes, CASK was found to complex with synaptic proteins, metabolic proteins, cytoskeletal elements, ribosomal subunits, and protein folding machinery. Next, in order to investigate the pathogenesis of CASK-linked cerebellar hypoplasia, I utilized a human case of early truncation wherein the 27th arginine of CASK is converted to a stop codon. Immunohistochemical analysis of this brain revealed an upregulation of glial fibrillary acidic protein, a common marker for degenerative cell death. To mechanistically test the hypothesis that cerebellar hypoplasia results from cell death rather than developmental failure, I created a murine model wherein CASK is deleted from the majority of cerebellar cells post-development using Cre recombinase driven by the Calb2 promoter. Deleting CASK from all cerebellar granule neurons post-migration indeed leads to degeneration of the cerebellum via massive depletion of granule cells while sparing Purkinje cells. Overall, the cerebellum shrinks by approximately half in cross-sectional area and degeneration is accompanied by a collapsing of the molecular layer and of Purkinje cell dendrites. In addition, cerebellar degeneration presents with a profound locomotor ataxia. In conclusion, CASK seems to be affecting brain energy homeostasis and synaptic connections via interactions with metabolic proteins, synaptic proteins, and protein homeostatic elements. Further, alterations in brain volume associated with CASK-linked disorders is the result of degenerative cell death rather than developmental failure as previously posited.
- Sensory Entrainment, Paying Attention, and Keeping Beat: General Effects and Individual DifferencesFaunce, Julia C. (Virginia Tech, 2023-06-15)Neural entrainment is a phenomenon whereby neural oscillations adjust their frequency to synchronize with the periodic vibration of external stimuli. Research suggests that neural entrainment may help explain the relationship between music education and more optimal cognitive performance later in development. This dissertation tested whether sensory entrainment caused short-term cognitive and motor performance benefits in a young adult sample, and whether entrainment or performance were impacted by stimulus parameters like modality or rhythm or individual differences in attentional ability and music training. Participants (N= 47) were asked to report the extent and type (e.g. instrumental, vocal) of music experience and severity of ADHD symptoms, and then were exposed to repetitive 1.25-Hz or arrhythmic visual or auditory stimuli with interlaced Flanker test items, while EEG was recorded. At some points in the experiment participants were additionally tasked with tapping along to the 1.25-Hz beat through both beat stimuli and gaps. Some entrainment and performance effects were congruent with findings from prior literature, while many other hypotheses regarding entrainment effects were not supported. In terms of individual differences, neither music training nor ADHD symptoms impacted entrainment, but ADHD did impact the effects of entrainment stimuli on Flanker reaction time, with higher ADHD symptoms predicting worse performance during periods of rhythmic stimulation. Lastly and surprisingly, while neither entrainment, music training, nor ADHD symptoms impacted beat-keeping performance in general, ADHD symptoms predicted better beat-keeping during stimulus gap periods. Results in general paint a complicated picture of acute entrainment effects and individual differences.
- Spatially expandable fiber-based probes as a multifunctional deep brain interfaceJiang, Shan; Patel, Dipan C.; Kim, Jongwoon; Yang, Shuo; Mills, William A. II; Zhang, Yujing; Wang, Kaiwen; Feng, Ziang; Vijayan, Sujith; Cai, Wenjun; Wang, Anbo; Guo, Yuanyuan; Kimbrough, Ian F.; Sontheimer, Harald; Jia, Xiaoting (Nature Research, 2020)Understanding the cytoarchitecture and wiring of the brain requires improved methods to record and stimulate large groups of neurons with cellular specificity. This requires miniaturized neural interfaces that integrate into brain tissue without altering its properties. Existing neural interface technologies have been shown to provide high-resolution electrophysiological recording with high signal-to-noise ratio. However, with single implantation, the physical properties of these devices limit their access to one, small brain region. To overcome this limitation, we developed a platform that provides three-dimensional coverage of brain tissue through multisite multifunctional fiber-based neural probes guided in a helical scaffold. Chronic recordings from the spatially expandable fiber probes demonstrate the ability of these fiber probes capturing brain activities with a single-unit resolution for long observation times. Furthermore, using Thy1-ChR2-YFP mice we demonstrate the application of our probes in simultaneous recording and optical/chemical modulation of brain activities across distant regions. Similarly, varying electrographic brain activities from different brain regions were detected by our customizable probes in a mouse model of epilepsy, suggesting the potential of using these probes for the investigation of brain disorders such as epilepsy. Ultimately, this technique enables three-dimensional manipulation and mapping of brain activities across distant regions in the deep brain with minimal tissue damage, which can bring new insights for deciphering complex brain functions and dynamics in the near future.
- A Time-Series Model of Phase Amplitude Cross Frequency Coupling and Comparison of Spectral Characteristics with Neural DataLepage, Kyle Q.; Vijayan, Sujith (Hindawi, 2015)Stochastic processes that exhibit cross-frequency coupling (CFC) are introduced. The ability of these processes to model observed CFC in neural recordings is investigated by comparison with published spectra. One of the proposedmodels, based onmultiplying a pulsatile function of a low-frequency oscillation (𝜃) with an unobserved and high-frequency component, yields a process with a spectrumthat is consistent with observation. Othermodels, such as those employing a biphasic pulsatile function of a low-frequency oscillation, are demonstrated to be less suitable.We introduce the full stochastic process time seriesmodel as a summation of three component weak-sense stationary (WSS) processes, namely, 𝜃, 𝛾, and 𝜂, with 𝜂 a 1/𝑓𝛼 noise process. The 𝛾 process is constructed as a product of a latent and unobserved high-frequency process 𝑥 with a function of the lagged, low-frequency oscillatory component (𝜃). After demonstrating that the model process is WSS, an appropriate method of simulation is introduced based upon the WSS property.This work may be of interest to researchers seeking to connect inhibitory and excitatory dynamics directly to observation in a model that accounts for known temporal dependence or to researchers seeking to examine what can occur in a multiplicative time-domain CFC mechanism.
- Unsupervised Multitaper Spectral Method for Identifying REM Sleep in Intracranial EEG Recordings Lacking EOG/EMG DataLepage, Kyle Q.; Jain, Sparsh; Kvavilashvili, Andrew; Witcher, Mark; Vijayan, Sujith (MDPI, 2023-08-25)A large number of human intracranial EEG (iEEG) recordings have been collected for clinical purposes, in institutions all over the world, but the vast majority of these are unaccompanied by EOG and EMG recordings which are required to separate Wake episodes from REM sleep using accepted methods. In order to make full use of this extremely valuable data, an accurate method of classifying sleep from iEEG recordings alone is required. Existing methods of sleep scoring using only iEEG recordings accurately classify all stages of sleep, with the exception that wake (W) and rapid-eye movement (REM) sleep are not well distinguished. A novel multitaper (Wake vs. REM) alpha-rhythm classifier is developed by generalizing K-means clustering for use with multitaper spectral eigencoefficients. The performance of this unsupervised method is assessed on eight subjects exhibiting normal sleep architecture in a hold-out analysis and is compared against a classical power detector. The proposed multitaper classifier correctly identifies 36±6 min of REM in one night of recorded sleep, while incorrectly labeling less than 10% of all labeled 30 s epochs for all but one subject (human rater reliability is estimated to be near 80%), and outperforms the equivalent statistical-power classical test. Hold-out analysis indicates that when using one night’s worth of data, an accurate generalization of the method on new data is likely. For the purpose of studying sleep, the introduced multitaper alpha-rhythm classifier further paves the way to making available a large quantity of otherwise unusable IEEG data.