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 …

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 …

Invariant Object Recognition In Deep Neural Networks And Humans, Haider Al-Tahan

Electronic Thesis and Dissertation Repository

Invariant object recognition, a cornerstone of human vision, enables recognizing objects despite variations in rotations, positions, and scales. To emulate human-like generalization across object transformations, computational models must perform well in this aspect. Deep neural networks (DNNs) are popular models for human ventral visual stream processing, though their alignment with human performance remains inconsistent. We examine object recognition across transformations in human adults and pretrained feedforward DNNs. DNNs are grouped in model families by architecture, visual diet, and learning goal. We focus on object rotation in depth, and observe that object recognition performance is better preserved in humans than in …

Selective Recruitment Of Cerebellum In Cognition, Ladan Shahshahani

Electronic Thesis and Dissertation Repository

Previous studies of cerebellar function in humans have shown that it is activated by a myriad of tasks ranging from motor learning and language to working memory and more. These studies have prompted a deviation from the traditional view of the cerebellum as a purely motor structure. However, the precise contribution of the cerebellum to these tasks remains ambiguous.

A prevalent assumption in fMRI studies is interpreting BOLD activation as evidence of the cerebellum's involvement in specific tasks. However, this interpretation is potentially misleading, especially considering that the BOLD signal predominantly represents cerebellar input, with output activity largely absent. Consequently, …

Visual Cortical Traveling Waves: From Spontaneous Spiking Populations To Stimulus-Evoked Models Of Short-Term Prediction, Gabriel B. Benigno

Electronic Thesis and Dissertation Repository

Thanks to recent advances in neurotechnology, waves of activity sweeping across entire cortical regions are now routinely observed. Moreover, these waves have been found to impact neural responses as well as perception, and the responses themselves are found to be structured as traveling waves. How exactly do these waves arise? Do they confer any computational advantages? These traveling waves represent an opportunity for an expanded theory of neural computation, in which their dynamic local network activity may complement the moment-to-moment variability of our sensory experience.

This thesis aims to help uncover the origin and role of traveling waves in the …

Neural Dynamics Of Visual Processes In Challenging Visibility Conditions, Saba Charmi Motlagh

Electronic Thesis and Dissertation Repository

In our daily visual experience, our brain effortlessly categorizes countless objects, enabling us to perceive and interpret the world around us. This core object recognition process is vital for our survival and adaptive behavior, allowing us to recognize objects despite variations in appearance. The incredible speed at which we accomplish this task is a testament to the efficiency of our visual system and the significance of visual processing is evident in the allocation of nearly half of the neocortex in primates to this function. Unraveling the intricacies of how the human visual system tackles this complex challenge has long been …

Neural Dynamics Of Target Processing In Attentional Blink, Mansoure Jahanian

Electronic Thesis and Dissertation Repository

The attentional blink (AB) phenomenon refers to the failure to report the second target (T2) if it appears 200-500 ms after the first target (T1) in a stream of rapidly presented images. The present study aimed to investigate the neural representations of target processing under conditions where AB does or does not occur. We recorded EEG and behavioral data while participants viewed a rapid sequence of natural object images embedded with two face targets presented at two lag conditions: lag 3 (targets were 252 ms apart) and lag 7 (targets were 588 ms apart). Consistent with AB, our behavioral results …

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 …

Temporal Dynamics Of Natural Sound Categorization, Ali Tafakkor

Electronic Thesis and Dissertation Repository

While extensive research has elucidated the brain’s processing of semantics from speech sound waves and their mapping onto the auditory cortex, the temporal dynamics of how meaningful non-speech sounds are processed remain less examined. Understanding these dynamics is key to resolving the debate between cascaded and parallel hierarchical processing models, both plausible given the anatomical evidence. This study investigates how semantic category information from environmental sounds is processed in the temporal domain, using electroencephalography (EEG) collected from 25 participants and representational similarity analysis (RSA) along with models of acoustic and semantic information. We examined information extracted by the brain from …

Machine Learning Techniques For Improved Functional Brain Parcellation, Da Zhi

Electronic Thesis and Dissertation Repository

Brain parcellation studies are fundamental for neuroscience as they serve as a bridge between anatomy and function, helping researchers interpret how functions are distributed across different brain regions. However, two substantial challenges exist in current imaging-based brain parcellation studies: large variations in the functional organization across individuals and the intrinsic spatial dependence which causes nearby brain locations to have a similar function. This thesis presents a series of projects aimed to tackle these challenges from different perspectives by using advanced machine learning techniques.

To handle the challenge of individual variability in building precise individual parcellations, Chapter 3 introduces a novel …

The Consolidation Of Memory Associations, Kyle A. Kainec

Doctoral Dissertations

Creating memories is a fundamental challenge for the human brain. To create memories, defining features of experiences must be stored distinguishably without forgetting other memories. Memory associations represent co-occurring features and defining features across experiences. Memory associations are represented as networks of information that are stored in the brain. New memory associations are encoded during experiences and can be used to update existing memory associations during offline intervals. However, the mechanisms that underlie how encoded memory associations are stored within existing networks during offline intervals remains unclear. The experiments in this dissertation address a significant theoretical gap in understanding the …

Phenotyping Regression In A Female Mouse Model For Rett Syndrome Using Computational Neuroethology Tools, Michael J. Mykins

Doctoral Dissertations

Regression is defined as loss of acquired skills over time and is a key feature of many neurodevelopmental disorders such as Rett syndrome (RTT). RTT is caused by mutations in the X-linked gene Methyl CpG-Binding Protein 2 (MECP2) and is characterized by a period of typical development with subsequent regression of previously acquired motor and speech skills in girls. In human and animal models, it is clear syndromic phenotypes are dynamic over time but phenotyping regression over time in animal models has remained elusive. Lack of established timelines to study the molecular, cellular, and behavioral features of regression in female …

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 …

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 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 …

Vi Energy-Efficient Memristor-Based Neuromorphic Computing Circuits And Systems For Radiation Detection Applications, Jorge Iván Canales Verdial

Electrical and Computer Engineering ETDs

Radionuclide spectroscopic sensor data is analyzed with minimal power consumption through the use of neuromorphic computing architectures. Memristor crossbars are harnessed as the computational substrate in this non-conventional computing platform and integrated with CMOS-based neurons to mimic the computational dynamics observed in the mammalian brain’s visual cortex. Functional prototypes using spiking sparse locally competitive approximations are presented. The architectures are evaluated for classification accuracy and energy efficiency. The proposed systems achieve a 90% true positive accuracy with a high-resolution detector and 86% with a low-resolution detector.

Artificial Dendritic Neuron: A Model Of Computation And Learning Algorithm, Zachary Hutchinson

Electronic Theses and Dissertations

Dendrites are root-like extensions from the neuron cell body and have long been thought to serve as the predominant input structures of neurons. Since the early twentieth century, neuroscience research has attempted to define the dendrite’s contribution to neural computation and signal integration. This body of experimental and modeling research strongly indicates that dendrites are not just input structures but are crucial to neural processing. Dendritic processing consists of both active and passive elements that utilize the spatial, electrical and connective properties of the dendritic tree.

This work presents a neuron model based around the structure and properties of dendrites. …

Identifying Functional Imaging Markers In Psychosis Using Fmri, Ruiqi Wang

MUSC Theses and Dissertations

Major types of psychotic disorders include schizophrenia (SCZ), bipolar disorder (BP) and schizoaffective disorder (SZA). These disorders have profound and overlapping symptoms with marked cognitive deficits, and their diagnosis relies on symptom clusters. The treatments for psychosis are usually focused on positive symptoms such as delusions and hallucinations. Although cognitive impairments underlie both positive and negative symptoms, functional brain imaging biomarkers that can reliably predict a patient's cognitive deficits are still lacking. Therefore, this project used functional MRI to explore the feasibility of using functional connectivity (FC) to predict cognitive performance.

A total of 207 subjects (BP: 79, SZ/SZA: 48, …

The Genomics Of Autism-Related Genes Il1rapl1 And Il1rapl2: Insights Into Their Cortical Distribution, Cell-Type Specificity, And Developmental Trajectories, Jacob Weaver

MUSC Theses and Dissertations

Neuropsychiatric disorders have a significant impact on modern society. These disorders affect a large percentage of the population: schizophrenia has a world-wide prevalence of 1% and autism spectrum disorders (ASD) affects 1 in 59 school-aged children in the US. There is substantial evidence that most neuropsychiatric disorders have a genetic component. Thus, with the advent of high throughput sequencing much effort has gone into identifying genetic variants associated with these disorders. The emerging picture from these studies is a complex one where hundreds of genes with small effects interact with a varied landscape of common variants to result in disease. …

Analysis Of Electrophysiological Markers And Correlated Components Of Neural Responses To Discourse Coherence, Kurt M. Masiello

Dissertations, Theses, and Capstone Projects

Constructing meaning from spoken language is invaluable for learning, social interaction, and communication. In clinical populations with developmental disorders of speech comprehension, the severity of disruption can persist and vary from limiting occupational opportunities to lower performance outcomes. Previous research has reported an event-related potential (ERP) neural positivity over right hemisphere lateral anterior sites in response to semantic and discourse processing. Although useful as a marker for clinical populations of autism spectrum disorder (ASD) and developmental language disorder (DLD), little is understood about the dynamics and neural sources of this biological marker. In addition to traditional methods of ERP analysis, …

Accelerated Forgetting In People With Epilepsy: Pathologic Memory Loss, Its Neural Basis, And Potential Therapies, Sarah Ashley Steimel Phd

Dartmouth College Ph.D Dissertations

While forgetting is vital to human functioning, delineating between normative and disordered forgetting can become incredibly complex. This thesis characterizes a pathologic form of forgetting in epilepsy, identifies a neural basis, and investigates the potential of stimulation as a therapeutic tool. Chapter 2 presents a behavioral characterization of the time course of Accelerated Long-Term Forgetting (ALF) in people with epilepsy (PWE). This chapter shows evidence of ALF on a shorter time scale than previous studies, with a differential impact on recall and recognition. Chapter 3 builds upon the work in Chapter 2 by extending ALF time points and investigating the …

Age- And Sex-Dependent Alterations In Primary Somatosensory Neuronal Calcium Network Dynamics During Locomotion, Sami L. Case

Theses and Dissertations--Pharmacology and Nutritional Sciences

Over the past 30 years, the calcium (Ca2+) hypothesis of brain aging has provided clear evidence that hippocampal neuronal Ca2+ dysregulation is a key biomarker of aging. Indeed, age-dependent Ca2+-mediated changes in intrinsic excitability, synaptic plasticity, and activity have helped identify some of the mechanisms engaged in memory and cognitive decline. However, much of this work has been done at the single-cell level, mostly in slice preparations, and in restricted structures of the brain. Recently, our lab identified age- and Ca2+-related neuronal network dysregulation in the cortex of the anesthetized animal. Still, investigations in the awake animal are needed to …

Sense And Sensitivity: Spatial Structure Of Conspecific Signals During Social Interaction, Keshav Ramachandra

Graduate Theses, Dissertations, and Problem Reports

Organisms rely on sensory systems to gather information about their environment. Localizing the source of a signal is key in guiding the behavior of the animal successfully. Localization mechanisms must cope with the challenges of representing the spatial information of weak, noisy signals. In this dissertation, I investigate the spatial dynamics of natural stimuli and explore how the electrosensory system of weakly electric fish encodes these realistic spatial signals. To do so In Chapter 2, I develop a model that examines the strength of the signal as it reaches the sensory array and simulates the responses of the receptors. The …

Spiking Neural Network That Maps From Generalized Coordinates To Cartesian Coordinates, Chloe K. Guie

Graduate Theses, Dissertations, and Problem Reports

In this thesis, I look to understand how insects compute task-level quantities by integrating range-fractionated sensory signals to create a sparse-spatial coding of Cartesian positions. I created biologically plausible 2-D and 3-D models of one species of the stick insect (Carausius morosus) leg and encoded the foot position through a spiking neural network. This model used spiking afferents from three angles of an insect leg which are integrated by one non-spiking interneuron. This model contains many dendritic compartments and one somatic compartment that encode the foot’s position relative to the body. The Functional Subnetwork Approach (FSA) was used …

Multimodal Neuron Classification Based On Morphology And Electrophysiology, Aqib Ahmad

Graduate Theses, Dissertations, and Problem Reports

Categorizing neurons into different types to understand neural circuits and ultimately brain function is a major challenge in neuroscience. While electrical properties are critical in defining a neuron, its morphology is equally important. Advancements in single-cell analysis methods have allowed neuroscientists to simultaneously capture multiple data modalities from a neuron. We propose a method to classify neurons using both morphological structure and electrophysiology. Current approaches are based on a limited analysis of morphological features. We propose to use a new graph neural network to learn representations that more comprehensively account for the complexity of the shape of neuronal structures. In …

Artificial Light At Night Disrupts Pain Behavior And Cerebrovascular Structure In Mice, Jacob Raymond Bumgarner

Graduate Theses, Dissertations, and Problem Reports

Artificial Light at Night Disrupts Pain Behavior and Cerebrovascular Structure in Mice

Jacob R. Bumgarner

Circadian rhythms are intrinsic biological processes that fluctuate in function with a period of approximately 24 hours. These rhythms are precisely synchronized to the 24- hour day of the Earth by external rhythmic signaling cues. Solar light-dark cycles are the most potent environmental signaling cue for terrestrial organisms to align internal rhythms with the external day. Proper alignment and synchrony of internal circadian rhythms with external environmental rhythms are essential for health and optimal biological function.

The modern human environment on Earth is no longer …

Computational Mechanisms Of Face Perception, Jinge Wang

Graduate Theses, Dissertations, and Problem Reports

The intertwined history of artificial intelligence and neuroscience has significantly impacted their development, with AI arising from and evolving alongside neuroscience. The remarkable performance of deep learning has inspired neuroscientists to investigate and utilize artificial neural networks as computational models to address biological issues. Studying the brain and its operational mechanisms can greatly enhance our understanding of neural networks, which has crucial implications for developing efficient AI algorithms. Many of the advanced perceptual and cognitive skills of biological systems are now possible to achieve through artificial intelligence systems, which is transforming our knowledge of brain function. Thus, the need for …

Spatial Processing Of Conspecific Signals In Weakly Electric Fish: From Sensory Image To Neural Population Coding, Oak Everette Milam

Graduate Theses, Dissertations, and Problem Reports

In this dissertation, I examine how an animal’s nervous system encodes spatially realistic conspecific signals in their environment and how the encoding mechanisms support behavioral sensitivity. I begin by modeling changes in the electrosensory signals exchanged by weakly electric fish in a social context. During this behavior, I estimate how the spatial structure of conspecific stimuli influences sensory responses at the electroreceptive periphery. I then quantify how space is represented in the hindbrain, specifically in the primary sensory area called the electrosensory lateral line lobe. I show that behavioral sensitivity is influenced by the heterogeneous properties of the pyramidal cell …