Computational Neuroscience Commons^™

Microtubule Polarity Flaws As A Treatable Driver Of Neurodegeneration, Bridie D. Eckel, Roy Cruz Jr., Erin M. Craig, Peter W. Baas

All Faculty Scholarship for the College of the Sciences

Microtubule disruption is a common downstream mechanism leading to axonal degeneration in a number of neurological diseases. To date, most studies on this topic have focused on the loss of microtubule mass from the axon, as well as changes in the stability properties of the microtubules and/or their tubulin composition. Here we posit corruption of the normal pattern of microtubule polarity orientation as an underappreciated and yet treatable contributor to axonal degeneration. We include computational modeling to fortify the rigor of our considerations. Our simulations demonstrate that even a small deviation from the usual polarity pattern of axonal microtubules is …

Social Virtual Reality: Neurodivergence And Inclusivity In The Metaverse, James Hutson

Faculty Scholarship

Whereas traditional teaching environments encourage lively and engaged interaction and reward extrovert qualities, introverts, and others with symptoms that make social engagement difficult, such as autism spectrum disorder (ASD), are often disadvantaged. This population is often more engaged in quieter, low-key learning environments and often does not speak up and answer questions in traditional lecture-style classes. These individuals are often passed over in school and later in their careers for not speaking up and are assumed to not be as competent as their gregarious and outgoing colleagues. With the rise of the metaverse and democratization of virtual reality (VR) technology, …

Neuromatch Academy: A 3-Week, Online Summer School In Computational Neuroscience, Bernard Marius 'T Hart, Titipat Achakulvisut, Ayoade Adeyemi, Athena Akrami, Bradly Alicea, Alicia Alonso-Andres, Diego Alzate-Correa, Arash Ash, Jesus J. Ballesteros, Aishwarya Balwani, Eleanor Batty, Ulrik Beierholm, Ari S. Benjamin, Upinder Bhalla, Gunnar Blohm, Joachim C. H. Blohm, Kathryn Bonnen, Marco Brigham, Bingni W. Brunton, John Butler, Brandon Caie, N Alex Cayco Gajic, Sharbatanu Chatterjee, Spyridon Chavlis, Ruidong Chen, You Cheng, H. M. Chow, Raymond Chua, Yunwei Dai, Isaac David, Eric E. J. Dewitt, Julien Denis, Alish Dipani, Arianna Dorschel, Jan Drugowitsch, Kshitij Dwivedi, Sean Escola, Haoxue Fan, Roozbeh Farhoodi, Yicheng Fei, Pierre-Étienne Fiquet, Lorenzo Fontolan, Jeremy Forest, Yuki Fujishima, Byron V. Galbraith, Mario Galdamez, Richard Gao, Julijana Gjorgjieva, Alexander Gonzalez, Qinglong Gu, Yueqi Guo, Ziyi Guo, Pankaj K. Gupta, Busra Tugce Gurbuz, Caroline Haimerl, Jordan B. Harrod, Alexandre Hyafil, Martin Irani, Daniel Jacobson, Michelle Johnson, Ilenna Simone Jones, Gili Karni, Robert E. Kass, Hyosub Edward Kim, Andreas M. Kist, Randal Koene, Konrad Kording, Matthew R. Krause, Arvind Kumar, Norma K. Kühn, Ray Lc, Matthew L. Laporte, Junseok Lee, Songting Li, Sikun Lin, Yang Lin, Shuze Liu, Tony Liu, Jesse A. Livezey, Linlin Lu, Jakob H. Macke, Kelly Mahaffy, A. Lucas Martins, Nicolás Martorell, Manolo Martínez, Marcelo G. Mattar, Jorge Aurelio Menendez, Kenneth D. Miller, Patrick J. Mineault, Nosratullah Mohammadi, Yalda Mohsenzadeh, Elenor Morgenroth, Taha Morshedzadeh, Alice Claudia Mosberger, Madhuvanthi Muliya, Marieke Mur, John D. Murray, Yashas Nd, Richard Naud, Prakriti Nayak, Anushka Oak, Itzel Olivos Castillo, Seyedmehdi Orouji, Jorge Otero-Millan, Marius Pachitariu, Biraj Pandey, Renato Paredes, Jesse Parent, Il Memming Park, Megan A. K. Peters, Xaq Pitkow, Panayiota Poirazi, Haroon Popal, Sandhya Prabhakaran, Tian Qiu, Srinidhi Ragunathan, Raul Rodriguez-Cruces, David Rolnick, Ashish Kumar Sahoo, Saeed Salehinajafabadi, Cristina Savin, Shreya Saxena, Paul Schrater, Karen Schroeder, Alice C. Schwarze, Madineh Sedigh-Sarvestani, K. Yuvaraj Sekhar, Reza Shadmehr, Maryam M. Shanechi, Siddhant Sharma, Eric Shea-Brown, Krishna V. Shenoy, Carolina L. Shimabukuro, Sergey Shuvaev, Man Ching Alison Sin, Maurice Smith, Nicholas A. Steinmetz, Karolina Stosio, Elizabeth Straley, Gabrielle Strandquist, Carsen Stringer, Rimjhim Tomar, Ngoc Tran, Sofia Triantafillou, Lawrence Udeigwe, Davide Valeriani, Vincent Valton, Maryam Vaziri-Pashkam, Peter Vincent, Gal Vishne, Pascal Wallisch, Peiyuan Wang, Claire Ward, Michael Waskom, Kunlin Wei, Anqi Wu, Zhengwei Wu, Brad Wyble, Lei Zhang, Daniel Zysman, Federico D’Oleire Uquillas, Tara Van Viegen

Articles

No abstract provided.

Virtual Reality (Vr)-Based Environmental Enrichment In Older Adults With Mild Cognitive Impairment (Mci) And Mild Dementia, Waleed Riaz, Zain Yar Khan, Ali Jawaid, Suleman Shahid

Medical College Documents

Background: Despite an alarming rise in the global prevalence of dementia, the available modalities for improving cognition and mental wellbeing of dementia patients remain limited. Environmental enrichment is an experimental paradigm that has shown promising anti-depressive and memory-enhancing effects in pre-clinical studies. However, its clinical utility has remained limited due to the lack of effective implementation strategies.
Objective: The primary objective of this study was to evaluate the usability (tolerability and interactivity) of a long-term virtual reality (VR)- based environmental enrichment training program in older adults with mild cognitive impairment (MCI) and mild dementia. A secondary objective was to assess …

Performance Of Openbci Eeg Binary Intent Classification With Laryngeal Imagery, Nathan George, Samuel Kuhn

Regis University Faculty Publications (comprehensive list)

One of the greatest goals of neuroscience in recent decades has been to rehabilitate individuals who no longer have a functional relationship between their mind and their body. Although neuroscience has produced technologies which allow the brains of paralyzed patients to accomplish tasks such as spell words or control a motorized wheelchair, these technologies utilize parts of the brain which may not be optimal for simultaneous use. For example, if you needed to look at flashing lights to spell words for communication, it would be difficult to simultaneously look at where you are moving. To improve upon this issue, this …

The Neurological Asymmetry Of Self-Face Recognition, Aleksandra Janowska, Brianna Balugas, Matthew Pardillo, Victoria Mistretta, Katherine Chavarria, Janet Brenya, Taylor Shelansky, Vanessa Martinez, Kitty Pagano, Nathira Ahmad, Samantha Zorns, Abigail Straus, Sarah Sierra, Julian Keenan

Department of Biology Faculty Scholarship and Creative Works

While the desire to uncover the neural correlates of consciousness has taken numerous directions, self-face recognition has been a constant in attempts to isolate aspects of self-awareness. The neuroimaging revolution of the 1990s brought about systematic attempts to isolate the underlying neural basis of self-face recognition. These studies, including some of the first fMRI (functional magnetic resonance imaging) examinations, revealed a right-hemisphere bias for self-face recognition in a diverse set of regions including the insula, the dorsal frontal lobe, the temporal parietal junction, and the medial temporal cortex. In this systematic review, we provide confirmation of these data (which are …

Understanding Individual Differences Within Large-Scale Brain Networks Across Cognitive Contexts, Katherine L. Bottenhorn

FIU Electronic Theses and Dissertations

Historically, human neuroimaging has studied brain regions “activated” during behavior and how they differ between groups of people. This approach has improved our understanding of healthy and disordered brain function, but has two key shortcomings. First, focusing on brain activation restricts how we understand the brain, ignoring vital, behind-the-scenes processing. In the past decade, the focus has shifted to communication between brain regions, or connectivity, revealing networks that exhibit subtle, consistent differences across behaviors and diagnoses. Without activation-focused research’s constraints, connectivity-focused neuroimaging research more comprehensively assesses brain function. Second, focusing on group differences ignores substantial within-group heterogeneity and often imposes …

Sites Of Circadian Clock Neuron Plasticity Mediate Sensory Integration And Entrainment, Maria P. Fernand, Hannah L. Pettibone, Joseph T. Bogart, Casey J. Roell, Charles E. Davey, Ausra Pranevicius, Khang V. Huynh, Sara M. Lennox, Boyan Kostadinov, Orie T. Shafer

Publications and Research

Networks of circadian timekeeping in the brain display marked daily changes in neuronal morphology. In Drosophila melanogaster, the striking daily structural remodeling of the dorsal medial termini of the small ventral lateral neurons has long been hypothesized to mediate endogenous circadian timekeeping. To test this model, we have specifically abrogated these sites of daily neuronal remodeling through the reprogramming of neural development and assessed the effects on circadian timekeeping and clock outputs. Remarkably, the loss of these sites has no measurable effects on endogenous circadian timekeeping or on any of the major output functions of the small ventral lateral neurons. …

Nmda Receptors Enhance The Fidelity Of Synaptic Integration, Chenguang Li, Allan Gulledge

Dartmouth Scholarship

Excitatory synaptic transmission in many neurons is mediated by two coexpressed ionotropic glutamate receptor subtypes, AMPA and NMDA receptors, that differ in kinetics, ion selectivity, and voltage-sensitivity. AMPA receptors have fast kinetics and are voltage-insensitive, while NMDA receptors have slower kinetics and increased conductance at depolarized membrane potentials. Here, we report that the voltage dependency and kinetics of NMDA receptors act synergistically to stabilize synaptic integration of EPSPs across spatial and volt- age domains. Simulations of synaptic integration in simplified and morphologically realistic dendritic trees re- vealed that the combined presence of AMPA and NMDA conductances reduce the variability of …

Stratifying Ischaemic Stroke Patients Across 3 Treatment Windows Using T2 Relaxation Times, Ordinal Regression And Cumulative Probabilities, Bryony Mcgarry, Elizabeth Hunter, Robin Damian, Michael Knight, Philip Clatworthy, George Harston, Keith Muir, Risto Kauppinen, John Kelleher

Conference papers

Unknown onset time is a common contraindication for anti-thrombolytic treatment of ischaemic stroke. T2 relaxation-based signal changes within the lesion can identify patients within or beyond the 4.5-hour intravenous thrombolysis treatment-window. However, now that intra-arterial thrombolysis is recommended between 4.5 and 6 hours from symptom onset and mechanical thrombectomy is considered safe between 6 and 24 hours, there are three treatment-windows to consider. Here we show a cumulative ordinal regression model, incorporating the T2 relaxation time, predicts the probabilities of a patient being within one of the three treatment-windows and is more accurate than signal intensity changes from T2 weighted …

Axonal Blockage With Microscopic Magnetic Stimulation, Hui Ye

Biology: Faculty Publications and Other Works

Numerous neurological dysfunctions are characterized by undesirable nerve activity. By providing reversible nerve blockage, electric stimulation with an implanted electrode holds promise in the treatment of these conditions. However, there are several limitations to its application, including poor bio-compatibility and decreased efficacy during chronic implantation. A magnetic coil of miniature size can mitigate some of these problems, by coating it with biocompatible material for chronic implantation. However, it is unknown if miniature coils could be effective in axonal blockage and, if so, what the underlying mechanisms are. Here we demonstrate that a submillimeter magnetic coil can reversibly block action potentials …

3d Architectural Analysis Of Neurons, Astrocytes, Vasculature & Nuclei In The Motor And Somatosensory Murine Cortical Columns, Jared Leichner

FIU Electronic Theses and Dissertations

Characterization of the complex cortical structure of the brain at a cellular level is a fundamental goal of neuroscience which can provide a better understanding of both normal function as well as disease state progression. Many challenges exist however when carrying out this form of analysis. Immunofluorescent staining is a key technique for revealing 3-dimensional structure, but subsequent fluorescence microscopy is limited by the quantity of simultaneous targets that can be labeled and intrinsic lateral and isotropic axial point-spread function (PSF) blurring during the imaging process in a spectral and depth-dependent manner. Even after successful staining, imaging and optical deconvolution, …

Circuits With Broken Fibration Symmetries Perform Core Logic Computations In Biological Networks, Ian Leifer, Flaviano Morone, Saulo D. S. Reis, José S. Andrade Jr., Mariano Sigman, Hernán A. Makse

Publications and Research

We show that logic computational circuits in gene regulatory networks arise from a fibration symmetry breaking in the network structure. From this idea we implement a constructive procedure that reveals a hierarchy of genetic circuits, ubiquitous across species, that are surprising analogues to the emblematic circuits of solid-state electronics: starting from the transistor and progressing to ring oscillators, current-mirror circuits to toggle switches and flip-flops. These canonical variants serve fundamental operations of synchronization and clocks (in their symmetric states) and memory storage (in their broken symmetry states). These conclusions introduce a theoretically principled strategy to search for computational building blocks …

Using Machine Learning To Conduct A Detailed Behavioral Analysis In An Appetitive Social Learning Task, Thomas Shao

Honors Scholar Theses

Learning by watching others, or observational learning, is important for social development and survival. However, not much is known about the brain mechanisms underlying this type of learning. Since the 1960s, observational learning has been widely studied in humans, but developing and analyzing experiments for animals has been challenging. Here, I explore observational learning using a novel paradigm while performing an analysis that involves tracking the rats using an active learning paradigm called DeepLabCut. In this novel paradigm, customized operant conditioning chambers are used for the rats to observe and learn from another animal repeatedly on multiple trials each day. …

Timing Is Everything: Temporal Dynamics Of Brain Activity Using The Human Connectome Project, Francesca Lofaro

Summer Research

Most neuroimaging studies produce snapshots of brain activity. The goal of this project is to examine the temporal dynamics of how these areas interact through time, using fear as a case study to assess how regions involved in fear interact. Working with Matlab computer code, I sort through the large fMRI dataset known as the Human Connectome Project to extract neuroimaging data from patients with different NIH Toolbox Fear-Somatic survey scores to assess the temporal dynamics between brain regions. The results will allow an understanding beyond which areas are involved, and instead will provide a picture of how these areas …

Review: Do The Different Sensory Areas Within The Cat Anterior Ectosylvian Sulcal Cortex Collectively Represent A Network Multisensory Hub?, M. Alex Meredith, Mark T. Wallace, H. Ruth Clemo

Anatomy and Neurobiology Publications

Current theory supports that the numerous functional areas of the cerebral cortex are organized and function as a network. Using connectional databases and computational approaches, the cerebral network has been demonstrated to exhibit a hierarchical structure composed of areas, clusters and, ultimately, hubs. Hubs are highly connected, higher-order regions that also facilitate communication between different sensory modalities. One region computationally identified network hub is the visual area of the Anterior Ectosylvian Sulcal cortex (AESc) of the cat. The Anterior Ectosylvian Visual area (AEV) is but one component of the AESc that also includes the auditory (Field of the Anterior Ectosylvian …

The Rhetoric Of Science Education And Technology, Iwasan D. Kejawa

School of Computing: Faculty Publications

Nearly thousands of science experiments are performed both on humans and animals every year in the United States (Gregory, 1999). Does Science enormously play a role in the well-beings of individual in the society? Research has found that science education is through motivation and satisfying the needs of humans. The scientific world is part of an elongated human development. This can be substantiated with the use and evolution of TECHNOLOGY and SCIENCE (Minton, 2004). Education of the entities that comprise the need to achieve the goal of TECHNOLOGY and SCIENCE which are important issues of today. Research has shown that …

Analytical Modeling Of A Communication Channel Based On Subthreshold Stimulation Of Neurobiological Networks, Alireza Khodaei

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

The emergence of wearable and implantable machines manufactured artificially or synthesized biologically opens up a new horizon for patient-centered health services such as medical treatment, health monitoring, and rehabilitation with minimized costs and maximized popularity when provided remotely via the Internet. In particular, a swarm of machines at the scale of a single cell down to the nanoscale can be deployed in the body by the non-invasive or minimally invasive operation (e.g., swallowing and injection respectively) to perform various tasks. However, an individual machine is only able to perform basic tasks so it needs to exchange data with the others …

Pattern Discovery In Brain Imaging Genetics Via Scca Modeling With A Generic Non-Convex Penalty, Lei Du, Kefei Liu, Xiaohui Yao, Jingwen Yan, Shannon L. Risacher, Junwei Han, Lei Guo, Andrew J. Saykin, Li Shen, Michael W. Weiner, Paul Aisen, Ronald Petersen, Clifford R. Jack, William Jagust, John Q. Trojanowki, Arthur W. Toga, Laurel Beckett, Robert C. Green, John Morris, Leslie M. Shaw, Zaven Khachaturian, Greg Sorensen, Maria Carrillo, Lew Kuller, Marc Raichle, Steven Paul, Peter Davies, Howard Fillit, Franz Hefti, David Holtzman, Charles D. Smith, Gregory Jicha, Peter A. Hardy, Partha Sinha, Elizabeth Oates, Gary Conrad

Neurology Faculty Publications

Brain imaging genetics intends to uncover associations between genetic markers and neuroimaging quantitative traits. Sparse canonical correlation analysis (SCCA) can discover bi-multivariate associations and select relevant features, and is becoming popular in imaging genetic studies. The L1-norm function is not only convex, but also singular at the origin, which is a necessary condition for sparsity. Thus most SCCA methods impose ℓ₁-norm onto the individual feature or the structure level of features to pursuit corresponding sparsity. However, the ℓ₁-norm penalty over-penalizes large coefficients and may incurs estimation bias. A number of non-convex penalties are proposed to reduce …

Rules And Mechanisms For Efficient Two-Stage Learning In Neural Circuits, Tiberiu Teşileanu, Bence Ölveczky, Vijay Balasubramanian

Publications and Research

Trial-and-error learning requires evaluating variable actions and reinforcing successful variants. In songbirds, vocal exploration is induced by LMAN, the output of a basal ganglia-related circuit that also contributes a corrective bias to the vocal output. This bias is gradually consolidated in RA, a motor cortex analogue downstream of LMAN. We develop a new model of such two-stage learning. Using stochastic gradient descent, we derive how the activity in ‘tutor’ circuits (e.g., LMAN) should match plasticity mechanisms in ‘student’ circuits (e.g., RA) to achieve efficient learning. We further describe a reinforcement learning framework through which the tutor can build its teaching …

The Brain Imaging Data Structure, A Format For Organizing And Describing Outputs Of Neuroimaging Experiments, Krzysztof Gorgolewski, Tibor Auer, Vince Calhoun, R Cameron Craddock, Samir Das, Eugene Duff, Guillaume Flandin, Tristan Glatard, Yaroslav Halchenko

Dartmouth Scholarship

The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this …

“My Logic Is Undeniable”: Replicating The Brain For Ideal Artificial Intelligence, Samuel C. Adams

Senior Honors Theses

Alan Turing asked if machines can think, but intelligence is more than logic and reason. I ask if a machine can feel pain or joy, have visions and dreams, or paint a masterpiece. The human brain sets the bar high, and despite our progress, artificial intelligence has a long way to go. Studying neurology from a software engineer’s perspective reveals numerous uncanny similarities between the functionality of the brain and that of a computer. If the brain is a biological computer, then it is the embodiment of artificial intelligence beyond anything we have yet achieved, and its architecture is advanced …

Hpcnmf: A High-Performance Toolbox For Non-Negative Matrix Factorization, Karthik Devarajan, Guoli Wang

COBRA Preprint Series

Non-negative matrix factorization (NMF) is a widely used machine learning algorithm for dimension reduction of large-scale data. It has found successful applications in a variety of fields such as computational biology, neuroscience, natural language processing, information retrieval, image processing and speech recognition. In bioinformatics, for example, it has been used to extract patterns and profiles from genomic and text-mining data as well as in protein sequence and structure analysis. While the scientific performance of NMF is very promising in dealing with high dimensional data sets and complex data structures, its computational cost is high and sometimes could be critical for …

Neuron Morphology Influences Axon Initial Segment Plasticity, Allan T. Gulledge, Jaime J. Bravo

Dartmouth Scholarship

In most vertebrate neurons, action potentials are initiated in the axon initial segment (AIS), a specialized region of the axon containing a high density of voltage-gated sodium and potassium channels. It has recently been proposed that neurons use plasticity of AIS length and/or location to regulate their intrinsic excitability. Here we quantify the impact of neuron morphology on AIS plasticity using computational models of simplified and realistic somatodendritic morphologies. In small neurons (e.g., dentate granule neurons), excitability was highest when the AIS was of intermediate length and located adjacent to the soma. Conversely, neurons having larger dendritic trees (e.g., pyramidal …

Analysis Of Neuronal Sequences Using Pairwise Biases, Zachary Roth

Department of Mathematics: Dissertations, Theses, and Student Research

Sequences of neuronal activation have long been implicated in a variety of brain functions. In particular, these sequences have been tied to memory formation and spatial navigation in the hippocampus, a region of mammalian brains. Traditionally, neuronal sequences have been interpreted as noisy manifestations of neuronal templates (i.e., orderings), ignoring much richer structure contained in the sequences. This paper introduces a new tool for understanding neuronal sequences: the bias matrix. The bias matrix captures the probabilistic tendency of each neuron to fire before or after each other neuron. Despite considering only pairs of neurons, the bias matrix captures the best …

The New York Head—A Precise Standardized Volume Conductor Model For Eeg Source Localization And Tes Targeting, Yu Huang, Lucas C. Parra, Stefan Haufe

Publications and Research

In source localization of electroencephalograpic (EEG) signals, as well as in targeted transcranial electric current stimulation (tES), a volume conductor model is required to describe the flow of electric currents in the head. Boundary element models (BEM) can be readily computed to representmajor tissue compartments, but cannot encode detailed anatomical information within compartments. Finite element models (FEM) can capture more tissue types and intricate anatomical structures, but with the higher precision also comes the need for semiautomated segmentation, and a higher computational cost. In either case, adjusting to the individual human anatomy requires costlymagnetic resonance imaging (MRI), and thus head …

Pathological Effects Of Repeated Concussive Tbi In Mouse Models: Periventricular Damage And Ventriculomegaly, Richard H. Wolferz Jr.

Honors Scholar Theses

Repeated concussive traumatic brain injury (rcTBI) is the most prominent form of head injury affecting the brain, with an estimated 1.7 million Americans affected each year (Kuhn 2012). Neurologists have been concerned about the danger of repeated head impacts since the 1920’s, but researchers have only begun to understand the long-term effects of rcTBI (McKee 2009). Although symptoms can be as mild as dizziness, current research suggests that multiple concussions can lead to a progressive degenerative brain disease known as chronic traumatic encephalopathy (CTE) (Luo 2008, McKee 2009, Kane 2013). Research on the brain is just beginning to scratch the …

Spike Field Coherence (Sfc) For Ripples In Rat Hippocampus, Pranav Singla

Honors Scholar Theses

The aim of this project was to determine coherence between two types of neural recordings which can be obtained from the rat hippocampus: spikes and local field potentials. Extracellular recording makes it possible to determine spiking activity from individual neurons in the vicinity of the recording electrode. Local field potential recording gives a combined activity of many neurons (thousands) at once to determine an overall picture of the coordination of the cells in real time. Here we examine the relationship between these two signals, focusing on place cells which spike at their maximal rate only at certain positions in physical …

Design, Programming, And User-Experience, Kaila G. Manca

Honors Scholar Theses

This thesis is a culmination of my individualized major in Human-Computer Interaction. As such, it showcases my knowledge of design, computer engineering, user-experience research, and puts into practice my background in psychology, com- munications, and neuroscience.

I provided full-service design and development for a web application to be used by the Digital Media and Design Department and their students.This process involved several iterations of user-experience research, testing, concepting, branding and strategy, ideation, and design. It lead to two products.

The first product is full-scale development and optimization of the web appli- cation.The web application adheres to best practices. It was …

Modeling Neurovascular Coupling From Clustered Parameter Sets For Multimodal Eeg-Nirs, M. Tanveer Talukdar, H. Robert Frost, Solomon G. G. Diamond

Dartmouth Scholarship

Despite significant improvements in neuroimaging technologies and analysis methods, the fundamental relationship between local changes in cerebral hemodynamics and the underlying neural activity remains largely unknown. In this study, a data driven approach is proposed for modeling this neurovascular coupling relationship from simultaneously acquired electroencephalographic (EEG) and near-infrared spectroscopic (NIRS) data. The approach uses gamma transfer functions to map EEG spectral envelopes that reflect time-varying power variations in neural rhythms to hemodynamics measured with NIRS during median nerve stimulation. The approach is evaluated first with simulated EEG-NIRS data and then by applying the method to experimental EEG-NIRS data measured from …