Computational Neuroscience Commons^™

Generating Predictive Gene Expression Signatures For Alzheimer's Disease Using Postmortem Brain Tissue, Ashley Duche

Pharmaceutical Sciences (PhD) Dissertations

Background: Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder characterized by the accumulation of amyloid-beta (Aβ) plaques and tau protein aggregates. These pathological features develop in specific brain regions, but why some areas are more vulnerable to early AD-related changes remains unclear. To address this, predictive gene expression signatures were developed to explore the molecular mechanisms underlying regional susceptibility to AD pathology.

Methods: This was performed using postmortem brain (PMB) tissue from participants in the Religious Orders Study and Memory and Aging Project (ROSMAP), Mayo Clinic, and Mount Sinai Brain Bank (MSBB) to generate gene expression signatures from six brain …

Topodino: Self-Supervised Topological Representation Learning For Neuronal Morphologies, Yasser Binbisher

Master's Theses

Neuronal cell types are categorized by transcriptomic identity, yet their morphological heterogeneity defies this classification. In response, researchers have adopted unsupervised graph representation learning as a tool to reveal morphological variation within single-class transcriptomic types. However, the complex geometry of neuronal morphology—especially long axons and dense dendrites—challenges graph neural networks, which struggle with message propagation across extended structures. To mitigate this, current approaches enforce sub-sampling on neuronal graphs and omit axons entirely, sacrificing critical biological features for computational efficiency. To overcome this trade-off, this thesis introduces TopoDINO, a self-supervised, topology-aware representation learning model designed to preserve the full hierarchical organization …

Early Onset Alzheimer’S Disease Markers In Mouse Hippocampus Unveiled By Single-Cell Transcriptomic Analysis Following Cranial Radiotherapy, Tuba Aksoy

Dissertations and Theses (Open Access)

Cranial radiation therapy plays an integral role in the treatment of brain tumors but can lead to progressive cognitive deficits in survivors by mechanisms that are poorly understood. To develop preventive or mitigative strategies, it is crucial to better understand the underlying pathogenesis of radiation-induced cognitive impairments. The study investigated single-cell transcriptomics and DNA methylation changes as potential drivers of persistent cellular dysfunction after radiation exposure, specifically concentrating on the CA1-3 regions of the hippocampus and the prefrontal cortex due to their role in cognitive functions. Thirteen-week-old mice underwent whole-brain radiation at clinically relevant doses. Following whole-brain radiation, an assessment …

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 …

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 …

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 …

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 …

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. …

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

Graduate Theses, Dissertations, and Problem Reports (ETD)

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 …

Multimodal Neuron Classification Based On Morphology And Electrophysiology, Aqib Ahmad

Graduate Theses, Dissertations, and Problem Reports (ETD)

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 …

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 …

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 …

Mapping Molecular Datasets Back To The Brain Regions They Are Extracted From: Remembering The Native Countries Of Hypothalamic Expatriates And Refugees, Arshad Khan, Alice Grant, Anais Martinez, Gully Burns, Brendan Thatcher, Vishwanath Anekonda, Benjamin Thompson, Zachary Roberts, Daniel Moralejo, James Blevins

Selected Works Temporary Series

This article, which includes novel unpublished data along with commentary and analysis,focuses on approaches to link transcriptomic, proteomic, and peptidomic datasets mined frombrain tissue to the original locations within the brain that they are derived from using digital atlasmapping techniques. We use, as an example, the transcriptomic, proteomic and peptidomicanalyses conducted in the mammalian hypothalamus. Following a brief historical overview, wehighlight studies that have mined biochemical and molecular information from the hypothalamusand then lay out a strategy for how these data can be linked spatially to the mapped locations in acanonical brain atlas where the data come from, thereby allowing …

Mapping Molecular Datasets Back To The Brain Regions They Are Extracted From: Remembering The Native Countries Of Hypothalamic Expatriates And Refugees, Khan, Alice Grant, Anais Martinez, Gully Burns, Brendan Thatcher, Vishwanath Anekonda, Benjamin Thompson, Zachary Roberts, Daniel Moralejo, James Blevins

Departmental Papers (Biology)

This article, which includes novel unpublished data along with commentary and analysis,focuses on approaches to link transcriptomic, proteomic, and peptidomic datasets mined frombrain tissue to the original locations within the brain that they are derived from using digital atlasmapping techniques. We use, as an example, the transcriptomic, proteomic and peptidomicanalyses conducted in the mammalian hypothalamus. Following a brief historical overview, wehighlight studies that have mined biochemical and molecular information from the hypothalamusand then lay out a strategy for how these data can be linked spatially to the mapped locations in acanonical brain atlas where the data come from, thereby allowing …

Efficient Reduced Bias Genetic Algorithm For Generic Community Detection Objectives, Aditya Karnam Gururaj Rao

Theses

The problem of community structure identification has been an extensively investigated area for biology, physics, social sciences, and computer science in recent years for studying the properties of networks representing complex relationships. Most traditional methods, such as K-means and hierarchical clustering, are based on the assumption that communities have spherical configurations. Lately, Genetic Algorithms (GA) are being utilized for efficient community detection without imposing sphericity. GAs are machine learning methods which mimic natural selection and scale with the complexity of the network. However, traditional GA approaches employ a representation method that dramatically increases the solution space to be searched by …

Morphogenesis And Growth Driven By Selection Of Dynamical Properties, Yuri Cantor

Dissertations, Theses, and Capstone Projects

Organisms are understood to be complex adaptive systems that evolved to thrive in hostile environments. Though widely studied, the phenomena of organism development and growth, and their relationship to organism dynamics is not well understood. Indeed, the large number of components, their interconnectivity, and complex system interactions all obscure our ability to see, describe, and understand the functioning of biological organisms.

Here we take a synthetic and computational approach to the problem, abstracting the organism as a cellular automaton. Such systems are discrete digital models of real-world environments, making them more accessible and easier to study then their physical world …

Can Cone Signals In The Wild Be Predicted From The Past?, David H. Foster, Iván Marín-Franch

MODVIS Workshop

In the natural world, the past is usually a good guide to the future. If light from the sun and sky is blue earlier in the day and yellow now, then it is likely to be more yellow later, as the sun's elevation decreases. But is the light reflected from a scene into the eye as predictable as the light incident upon the scene, especially when lighting changes are not just spectral but include changes in local shadows and mutual reflections? The aim of this work was to test the predictability of cone photoreceptor signals in the wild over the …

Network Exploration Of Correlated Multivariate Protein Data For Alzheimer's Disease Association, Matthew J. Lane

Theses

Alzheimer Disease (AD) is difficult to diagnose by using genetic testing or other traditional methods. Unlike diseases with simple genetic risk components, there exists no single marker determining as to whether someone will develop AD. Furthermore, AD is highly heterogeneous and different subgroups of individuals develop the disease due to differing factors. Traditional diagnostic methods using perceivable cognitive deficiencies are often too little too late due to the brain having suffered damage from decades of disease progression. In order to observe AD at early stages prior to the observation of cognitive deficiencies, biomarkers with greater accuracy are required. By using …

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 …

Computational Modeling Of Biological Neural Networks On Gpus: Strategies And Performance, Byron Galbraith

Master's Theses (2009 -)

Simulating biological neural networks is an important task for computational neuroscientists attempting to model and analyze brain activity and function. As these networks become larger and more complex, the computational power required grows significantly, often requiring the use of supercomputers or compute clusters. An emerging low-cost, highly accessible alternative to many of these resources is the Graphics Processing Unit (GPU) - specialized massively-parallel graphics hardware that has seen increasing use as a general purpose computational accelerator thanks largely due to NVIDIA's CUDA programming interface. We evaluated the relative benefits and limitations of GPU-based tools for large-scale neural network simulation and …

Tools And Approaches For The Construction Of Knowledge Models From The Neuroscientific Literature, Gully Burns, Arshad Khan, Shahram Ghandeharizadeh, Mark O'Neill, Yi-Shin Chen

Selected Works Temporary Series

Within this paper, we describe a neuroinformatics project (called "NeuroScholar," http://www.neuroscholar.org/) that enables researchers to examine, manage, manipulate, and use the information contained within the published neuroscientific literature. The project is built within a multi-level, multi-component framework constructed with the use of software engineering methods that themselves provide code-building functionality for neuroinformaticians. We describe the different software layers of the system. First, we present a hypothetical usage scenario illustrating how NeuroScholar permits users to address large-scale questions in a way that would otherwise be impossible. We do this by applying NeuroScholar to a "real-world" neuroscience question: How is stress-related information …

Tools And Approaches For The Construction Of Knowledge Models From The Neuroscientific Literature, Burns, Arshad Khan, Shahram Ghandeharizadeh, Mark O'Neill, Yi-Shin Chen

Departmental Papers (Biology)

Within this paper, we describe a neuroinformatics project (called "NeuroScholar," http://www.neuroscholar.org/) that enables researchers to examine, manage, manipulate, and use the information contained within the published neuroscientific literature. The project is built within a multi-level, multi-component framework constructed with the use of software engineering methods that themselves provide code-building functionality for neuroinformaticians. We describe the different software layers of the system. First, we present a hypothetical usage scenario illustrating how NeuroScholar permits users to address large-scale questions in a way that would otherwise be impossible. We do this by applying NeuroScholar to a "real-world" neuroscience question: How is stress-related information …

First Principles Of Physio-Informatic Systems: Neurocosmology, David Jay Warner

Loma Linda University Electronic Theses, Dissertations & Projects

Physio-informatics is a new systems model for linking human physiologic systems to information systems in the most general way. Physio-informatics is used here to denote a systems based, physiologically robust reference architecture for designing and refining interactive human-computer interface systems in ways that increase operational throughput of information. In this dissertation, a systems model for interactive human-computer interface systems is developed. This model is a physiologically based reference architecture for designing and developing interactive human computer interface systems to match the human nervous system’s ability to transduce, transmit, and render to consciousness the necessary information to interact intelligently with information. …