Neuroscience and Neurobiology Commons^™

The Genomics Of Champ1: Insights Into Their Cell-Type Specificity And Developmental Trajectories, Zoe Marie Van Caugherty

MUSC Theses and Dissertations

Chromosome alignment maintaining phosphoprotein 1(CHAMP1) is a gene that encodes a zinc finger protein that is involved in in the maintenance of kinetochore-microtubule attachment and regulating chromosome segregation in mitosis. (Itoh et al., 2011) CHAMP1 mutations have been shown to be major risk factors for neurodevelopmental disorders (NDDs) and autism spectrum disorder (ASD).(Asakura et al., 2021; Isidor et al., 2016; Levy et al., 2022) Although there is information on the link between CHAMP1 mutations and NDD, the role of CHAMP1 in regulating processes of human cortical development, namely, neurogenesis, proliferation, and electrophysiological properties of newly born neurons, is unknown. This …

Elucidating Neuroinflammation In Multiple Sclerosis By Network Analysis, Nora C. Welsh

Dartmouth College Ph.D Dissertations

Multiple sclerosis (MS) is a heterogeneous disease, differing on many variables, including disease course, sex, and overall activity. Key characteristics of the disease encompass demyelination, axonal damage, neuronal loss, glial cell activation, and the infiltration of peripheral immune cells. Molecular proxies of these functions are secreted proteins, including cytokines and immunoglobulins, which, in the central nervous system (CNS), can be secreted into the cerebrospinal fluid (CSF). A detailed analysis of these secreted proteins can offer insights into the evolving immunological and neurodegenerative features as the disease progresses. To understand the dynamic biological processes involved in MS, I used network analysis …

A Causal Inference Approach For Spike Train Interactions, Zach Saccomano

Dissertations, Theses, and Capstone Projects

Since the 1960s, neuroscientists have worked on the problem of estimating synaptic properties, such as connectivity and strength, from simultaneously recorded spike trains. Recent years have seen renewed interest in the problem coinciding with rapid advances in experimental technologies, including an approximate exponential increase in the number of neurons that can be recorded in parallel and perturbation techniques such as optogenetics that can be used to calibrate and validate causal hypotheses about functional connectivity. This thesis presents a mathematical examination of synaptic inference from two perspectives: (1) using in vivo data and biophysical models, we ask in what cases the …

When Brain Meets Artificial Intelligence, Lu Zhang

Computer Science and Engineering Dissertations - Archive

When we review the history of development of artificial intelligence (AI), we will find that brain science plays a pivotal role in fostering breakthroughs in AI, such as artificial neural networks (ANNs). Today, AI has made remarkable strides, particularly with the emergence of large language models (LLMs), surpassing expectations and achieving human-level performance in certain tasks. Nonetheless, an insurmountable gap remains between AI and human intelligence. It is urgent to establish a bridge between brain science and AI, promoting their mutual enhancement and collaborations. This involve establishing connections from brain science to AI (brain-inspired AI), and reversely, from AI to …

Here Come The Test Results: An Analysis Of Games In Experimental Design For Neuroscience Research, Madison Mcdougall

Scripps Senior Theses

A large part of experimental design in neuroscience revolves around the tasks that are designed for participants to complete to understand the complex system that is the brain. However, as Alan Newell pointed out in his 1973 paper, “You can’t play 20 questions with nature and win,” as designed tasks often answer a binary question that limits their contribution to understanding the brain as a system or a “genuine slab of human behavior.” Games provide an apt solution to this problem, as their complexity embodies a naturalistic task while their well-defined rules create a robust experimental system. Video games specifically …

Cerebellar Feedback Effects On Responses To Conspecific Signals Of Different Frequencies In The Electrosensory System, Elora K. Shinn

Graduate Theses, Dissertations, and Problem Reports (ETD)

Sensory systems in animals have evolved to translate physical stimuli into neural representations that, in turn, guide behavior. Feedback is critical in these processes, allowing organisms to filter redundant information and respond effectively to relevant stimuli. In humans, for example, the cerebellar flocculus provides feedback to the vestibulo-ocular reflex to stabilize eye movements during body rotation. In bats, the feedback during echolocation helps prioritize essential signals from the environment. In weakly electric fish, cerebellar feedback helps filter out redundant low-frequency modulations caused by conspecifics, enabling them to detect prey and other relevant signals. However, the influence of cerebellar feedback on …

Utilizing Ai Integrated Neuroimaging Technology To Expand Upon Machine Learning In Positron Emission Tomography Technology With The Aim Of Detecting Amyloid Beta Biomarkers Early In The Onset Of Alzheimer's., Ethan S. Terman

Undergraduate Research Posters

Early intervention in Alzheimer's is vital for treatment. The earlier a professional can detect symptoms and make a diagnosis the earlier a prognosis can be implemented. With the prevalence of data in our day-to-day world combined with Artificial intelligence (AI), utilizing both for machine learning can pave the way for more accurate and efficient detection of Alzheimer's and other neurodegenerative diseases. AI combined with Machine learning (ML) increases diagnostic efficiency and reduces human errors, making it a valuable resource for physicians and clinicians alike. With the increasing amount of data processing and image interpretation required, the ability to use AI …

A Tool To Automate Neuropathological Assessment In Huntington Disease Mouse Models, Samuel A. Moldenhauer

Honors Undergraduate Theses

As our life expectancy continues to rise, so does the prevalence of neurodegenerative diseases, such as Huntington disease (HD). Neurodegeneration leads to progressive regional brain atrophy, typically initiating prior to symptom onset. With the advancements of medical treatments designed to target neurodegeneration, researchers measure their impact on atrophy in animal models to assess their effectiveness. This is important because treatments designed to combat neuropathology are more likely to modify the disease itself, per contra to treatments designed to mask or treat symptoms. One method of brain region size quantification is magnetic resonance imaging (MRI), which while accurate, is prohibitively expensive. …

Dna Methylation-Based Epigenetic Biomarkers In Cell-Type Deconvolution And Tumor Tissue Of Origin Identification, Ze Zhang

Dartmouth College Ph.D Dissertations

DNA methylation is an epigenetic modification that regulates gene expression and is essential to establishing and preserving cellular identity. Genome-wide DNA methylation arrays provide a standardized and cost-effective approach to measuring DNA methylation. When combined with a cell-type reference library, DNA methylation measures allow the assessment of underlying cell-type proportions in heterogeneous mixtures. This approach, known as DNA methylation deconvolution or methylation cytometry, offers a standardized and cost-effective method for evaluating cell-type proportions. While this approach has succeeded in discerning cell types in various human tissues like blood, brain, tumors, skin, breast, and buccal swabs, the existing methods have major …

Cortical Representation Of Learning Social Interactions In Freely Moving Non-Human Primates, Melissa Franch

Dissertations and Theses (Open Access)

The motivation and capacity to be social is necessary for human survival. Successful learning of complex, prosocial behavior stems from the ability to perceive and respond to visual cues, such as the body language and facial expressions, from others in our environment. This dependence on visual information to guide social interaction is especially true for humans and non-human primates. Although recent studies in primate neurophysiology discovered neurons that can encode socially relevant variables, like reward and social actions, the underlying neural mechanisms of learning advanced social concepts, such as cooperation, are not well understood. Further, previous work has identified …

The Effects Of Brain Control: A 3-D Agent-Based Model For Studying Pain, Kayla Kraeuter, Carley Reith, Benedict Kolber, Rachael Miller Neilan

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Self-Supervised Pretraining And Transfer Learning On Fmri Data With Transformers, Sean Paulsen

Dartmouth College Ph.D Dissertations

Transfer learning is a machine learning technique founded on the idea that knowledge acquired by a model during “pretraining” on a source task can be transferred to the learning of a target task. Successful transfer learning can result in improved performance, faster convergence, and reduced demand for data. This technique is particularly desirable for the task of brain decoding in the domain of functional magnetic resonance imaging (fMRI), wherein even the most modern machine learning methods can struggle to decode labelled features of brain images. This challenge is due to the highly complex underlying signal, physical and neurological differences between …

Nanoscale Modeling Of Membrane Systems Under Mechanical Deformation In Traumatic Brain Injury Using Molecular Dynamics, Anh Thi Ngoc Vo

Theses and Dissertations

Neuronal membrane disruption and mechanoporation are nanoscale damage mechanisms that critically affect brain cell viability during traumatic brain injury (TBI). These nanoscale cellular impairments are elusive in experiments and necessitate in silico approaches such as molecular dynamics (MD) simulations. Implementing MD, this research aims to investigate the effects of different key factors related to membrane deformation and damage, including force field resolutions, lipid compositions, and loading conditions.

To examine the impact of force field resolution, MD deformation simulations were conducted on 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylcholine (POPC) lipid bilayer membranes, using all-atom (AA), united-atom (UA), and coarse-grained Martini (CG-M) force fields. The mechanical responses …

Voluntary Action And Conscious Intention, Jake Gavenas

Computational and Data Sciences (PhD) Dissertations

Traditional experimental methodology in neuroscience involves comparing responses to different stimuli to make inferences about the human brain. However, human volition entails making decisions and acting in a manner that is underdetermined by the external environment. Investigations of the neuronal basis of volition have led to new controversies regarding the existence of free will, and offer potential directions for medical treatments of disorders such as addiction, akinetic mutism, and locked-in syndrome. However, because volition experiments leave aspects of responses “up to” participants, neuroscientists must utilize computational techniques such as modeling, simulation, and advanced analyses to progress our understanding of human …

Unraveling The Neural Basis Of Emotions: Advancing Understanding With Ecologically Valid Paradigms And High-Resolution Intracranial Eeg, Tiankang Xie

Dartmouth College Ph.D Dissertations

Background

Emotion arises from integrating information about the external world with memories of past experiences, current homeostatic states, and future goals. They play a vital role in regulating our thoughts, feelings and behaviors, significantly impacting our mental health. Thus, it is important to understand the neurobiological mechanisms that give rise to emotions. While there has been considerable work investigating the neural basis of emotions, progress has been hampered by several methodological limitations. For example, prior work has relied on relatively simple and isolated stimuli, which often fail to effectively capture the dynamic and multifaceted nature of emotional experiences in real-life …

Destined Failure, Chengjun Pan

Masters Theses

I attempt to examine the complex structure of human communication, explaining why it is bound to fail. By reproducing experienceable phenomena, I demonstrate how they can expose communication structure and reveal the limitations of our perception and symbolization.I divide the process of communication into six stages: input, detection, symbolization, dictionary, interpretation, and output. In this thesis, I examine the flaws and challenges that arise in the first five stages. I argue that reception acts as a filter and that understanding relies on a symbolic system that is full of redundancies. Therefore, every interpretation is destined to be a deviation.

Neural Tabula Rasa: Foundations For Realistic Memories And Learning, Patrick R. Perrine

Master's Theses

Understanding how neural systems perform memorization and inductive learning tasks are of key interest in the field of computational neuroscience. Similarly, inductive learning tasks are the focus within the field of machine learning, which has seen rapid growth and innovation utilizing feedforward neural networks. However, there have also been concerns regarding the precipitous nature of such efforts, specifically in the area of deep learning. As a result, we revisit the foundation of the artificial neural network to better incorporate current knowledge of the brain from computational neuroscience. More specifically, a random graph was chosen to model a neural system. This …

Solving The Cable Equation, A Second-Order Time Dependent Pde For Non-Ideal Cables With Action Potentials In The Mammalian Brain Using Kss Methods, Nirmohi Charbe

Master's Theses

In this thesis we shall perform the comparisons of a Krylov Subspace Spectral method with Forward Euler, Backward Euler and Crank-Nicolson to solve the Cable Equation. The Cable Equation measures action potentials in axons in a mammalian brain treated as an ideal cable in the first part of the study. We shall subject this problem to the further assumption of a non-ideal cable. Assume a non-uniform cross section area along the longitudinal axis. At the present time, the effects of torsion, curvature and material capacitance are ignored. There is particular interest to generalize the application of the PDEs including and …

Neural Correlates Of Post-Traumatic Brain Injury (Tbi) Attention Deficits In Children, Meng Cao

Dissertations

Traumatic brain injury (TBI) in children is a major public health concern worldwide. Attention deficits are among the most common neurocognitive and behavioral consequences in children post-TBI which have significant negative impacts on their educational and social outcomes and compromise the quality of their lives. However, there is a paucity of evidence to guide the optimal treatment strategies of attention deficit related symptoms in children post-TBI due to the lack of understanding regarding its neurobiological substrate. Thus, it is critical to understand the neural mechanisms associated with TBI-induced attention deficits in children so that more refined and tailored strategies can …

Deep Hybrid Modeling Of Neuronal Dynamics Using Generative Adversarial Networks, Soheil Saghafi

Dissertations

Mechanistic modeling and machine learning methods are powerful techniques for approximating biological systems and making accurate predictions from data. However, when used in isolation these approaches suffer from distinct shortcomings: model and parameter uncertainty limit mechanistic modeling, whereas machine learning methods disregard the underlying biophysical mechanisms. This dissertation constructs Deep Hybrid Models that address these shortcomings by combining deep learning with mechanistic modeling. In particular, this dissertation uses Generative Adversarial Networks (GANs) to provide an inverse mapping of data to mechanistic models and identifies the distributions of mechanistic model parameters coherent to the data.

Chapter 1 provides background information on …

Gap Junctions And Synchronization Clusters In The Thalamic Reticular Nuclei, Anca R. Radulescu, Michael Anderson

Biology and Medicine Through Mathematics Conference

No abstract provided.

Computing Brain Networks With Complex Dynamics, Anca R. Radulescu

Biology and Medicine Through Mathematics Conference

No abstract provided.

Task-Driven Influences On Fixational Eye Movements, Jonathan Victor, Yen-Chu Lin, Michele Rucci

MODVIS Workshop

There is now compelling evidence that the spatiotemporal remapping carried out by fixational eye movements (FEMs) is an essential step in visual processing. Moreover, the overall Brownian-like statistics of FEMs are calibrated to map fine spatial detail into the temporal frequency range to which retinal circuitry is tuned. Here, we tested the hypothesis that the detailed spatial characteristics of FEMs can be adjusted to task demands via cognitive influences that operate even in the absence of a visual stimulus. We examined FEMs in a task that required subjects (N=6) to report which of two letters was displayed. Trials were blocked; …

Active Encoding Of Space Through Time, Michele Rucci, Jonathan D. Victor

MODVIS Workshop

No abstract provided.

Extracting Edges In Space And Time During Visual Fixations, Lynn Schmittwilken, Marianne Maertens

MODVIS Workshop

No abstract provided.

Toward A Manifold Encoding Neural Responses, Luciano Dyballa, Andra M. Rudzite, Mahmood S. Hoseini, Mishek Thapa, Michael P. Stryker, Greg D. Field, Steven W. Zucker

MODVIS Workshop

Understanding circuit properties from physiological data presents two challenges: (i) recordings do not reveal connectivity, and (ii) stimuli only exercise circuits to a limited extent. We address these challenges for the mouse visual system with a novel neural manifold obtained using unsupervised algorithms. Each point in our manifold is a neuron; nearby neurons respond similarly in time to similar parts of a stimulus ensemble. This ensemble includes drifting gratings and flows, i.e., patterns resembling what a mouse would “see” running through fields.

Regarding (i), our manifold differs from the standard practice in computational neuroscience: embedding trials in neural coordinates. Topology …

Constraining The Binding Problem Using Maps, Zhixian Han, Anne Sereno

MODVIS Workshop

We constrained the binding problem by creating maps of different attributes. We compared the performance of different models with different maps in our current study. Our preliminary results showed that the performance of the model is the highest when location maps were used. These results suggest that the optimal way to constrain the binding problem is to create location maps of different attributes.

From Image Gradients To A Perceptual Metric Space, Alan Johnston

MODVIS Workshop

How do we achieve a sense of spatial dimension from a sense of location? There are three predominant ideas about how we achieve this; spatial isomorphism, in which what we see reflects differences in distance or size in the brain; that spatial extent depends upon motor sensations or intentions related to eye movements; and that distance is computed from the degree of correlation in neural activity between adjacent locations, with distance inversely proportional to the correlation. There are problems with each of these approaches, for example, neural correlation may depend more on image structure than adjacency - consider the case …

V1 Saliency Hypothesis And Central-Peripheral Dichotomy (Cpd), Li Zhaoping Prof. Dr.

MODVIS Workshop

No abstract provided.

A Dynamical Model Of Binding In Visual Cortex During Incremental Grouping And Search, Daniel Schmid, Daniel A. Braun, Heiko Neumann

MODVIS Workshop

Binding of visual information is crucial for several perceptual tasks. To incrementally group an object, elements in a space-feature neighborhood need to be bound together starting from an attended location (Roelfsema, TICS, 2005). To perform visual search, candidate locations and cued features must be evaluated conjunctively to retrieve a target (Treisman&Gormican, Psychol Rev, 1988). Despite different requirements on binding, both tasks are solved by the same neural substrate. In a model of perceptual decision-making, we give a mechanistic explanation for how this can be achieved. The architecture consists of a visual cortex module and a higher-order thalamic module. While the …