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Articles 1 - 9 of 9
Full-Text Articles in Computational Neuroscience
Elucidating Neuroinflammation In Multiple Sclerosis By Network Analysis, Nora C. Welsh
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 …
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 …
Self-Supervised Pretraining And Transfer Learning On Fmri Data With Transformers, Sean Paulsen
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 …
Accelerated Forgetting In People With Epilepsy: Pathologic Memory Loss, Its Neural Basis, And Potential Therapies, Sarah Ashley Steimel Phd
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 …
Nmda Receptors Enhance The Fidelity Of Synaptic Integration, Chenguang Li, Allan Gulledge
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 …
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
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 …
Neuron Morphology Influences Axon Initial Segment Plasticity, Allan T. Gulledge, Jaime J. Bravo
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 …
Modeling Neurovascular Coupling From Clustered Parameter Sets For Multimodal Eeg-Nirs, M. Tanveer Talukdar, H. Robert Frost, Solomon G. G. Diamond
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 …
Derivation Of A Novel Efficient Supervised Learning Algorithm From Cortical-Subcortical Loops, Ashok Chandrashekar, Richard Granger
Derivation Of A Novel Efficient Supervised Learning Algorithm From Cortical-Subcortical Loops, Ashok Chandrashekar, Richard Granger
Dartmouth Scholarship
Although brain circuits presumably carry out powerful perceptual algorithms, few instances of derived biological methods have been found to compete favorably against algorithms that have been engineered for specific applications. We forward a novel analysis of a subset of functions of cortical-subcortical loops, which constitute more than 80% of the human brain, thus likely underlying a broad range of cognitive functions. We describe a family of operations performed by the derived method, including a non-standard method for supervised classification, which may underlie some forms of cortically dependent associative learning. The novel supervised classifier is compared against widely used algorithms for …