Computational Neuroscience Commons^™

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. …

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 …

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 M. Khan, Alice H. Grant, Anais Martinez, Gully Apc Burns, Brendan S. Thatcher, Vishwanath T. Anekonda, Benjamin W. Thompson, Zachary S. Roberts, Daniel H. Moralejo, James E. Blevins

Arshad M. Khan, Ph.D.

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 …

Navigating The "Little Brain": Comprehensive Mapping Of Functional Organisation, Maedbh King

Electronic Thesis and Dissertation Repository

Two decades of neuroimaging research suggests that the cerebellum is functionally involved in a range of cognitive and motor processes. However, missing from the literature is a comprehensive map detailing a clear functional organisation of the cerebellum. Previous studies have used a restricted task-mapping approach to localise task-specific functional activation to cerebellar lobules. However, this approach, which is often limited to one or two functional domains within individual subjects, fails to characterise the full breadth of functional specialisation within the cerebellum. To overcome this restricted task-mapping problem, we tested 17 subjects on a condition-rich task battery (61 task conditions) across …

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 …

The Effect Of The R1648h Sodium Channel Mutation On Neuronal Excitability: A Model Study, Christopher Locandro, Robert Clewley

Georgia State Undergraduate Research Conference

No abstract provided.

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 Apc Burns, Arshad M. Khan, Shahram Ghandeharizadeh, Mark O'Neill, Yi-Shin Chen

Arshad M. Khan, Ph.D.

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. …