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A STRUCTURAL AND FUNCTIONAL ANALYSIS OF HUMAN BRAIN MRI WITH ATTENTION DEFICIT HYPERACTIVITY DISORDER
Title
A STRUCTURAL AND FUNCTIONAL ANALYSIS OF HUMAN BRAIN MRI WITH ATTENTION DEFICIT HYPERACTIVITY DISORDER.
Creator
Watane, Arjun A, Bagci, Ulas, University of Central Florida
Abstract / Description
Attention Deficit Hyperactivity Disorder (ADHD) affects 5-10% of children worldwide. Its effects are mainly behavioral, manifesting in symptoms such as inattention, hyperactivity, and impulsivity. If not monitored and treated, ADHD may adversely affect a child's health, education, and social life. Furthermore, the neurological disorder is currently diagnosed through interviews and opinions of teachers, parents, and physicians. Because this is a subjective method of identifying ADHD, it is...
Show moreAttention Deficit Hyperactivity Disorder (ADHD) affects 5-10% of children worldwide. Its effects are mainly behavioral, manifesting in symptoms such as inattention, hyperactivity, and impulsivity. If not monitored and treated, ADHD may adversely affect a child's health, education, and social life. Furthermore, the neurological disorder is currently diagnosed through interviews and opinions of teachers, parents, and physicians. Because this is a subjective method of identifying ADHD, it is easily prone to error and misdiagnosis. Therefore, there is a clear need to develop an objective diagnostic method for ADHD. The focus of this study is to explore the use of machine language classifiers on information from the brain MRI and fMRI of both ADHD and non-ADHD subjects. The imaging data are preprocessed to remove any intra-subject and inter-subject variation. For both MRI and fMRI, similar preprocessing stages are performed, including normalization, skull stripping, realignment, smoothing, and co-registration. The next step is to extract features from the data. For MRI, anatomical features such as cortical thickness, surface area, volume, and intensity are obtained. For fMRI, region of interest (ROI) correlation coefficients between 116 cortical structures are determined. A large number of image features are collected, yet many of them may include redundant and useless information. Therefore, the features used for training and testing the classifiers are selected in two separate ways, feature ranking and stability selection, and their results are compared. Once the best features from MRI and fMRI are determined, the following classifiers are trained and tested through leave-one-out cross validation, experimenting with varying feature numbers, for each imaging modality and feature selection method: support vector machine, support vector regression, random forest, and elastic net. Thus, there are four experiments (MRI-rank, MRI-stability, fMRI-rank, fMRI-stability) with four classifiers in each for a total of 16 classifiers trained per each feature count attempted. The results of each classifier are the decisions of each subject, ADHD or non-ADHD. Finally, a classifier decision ensemble is created through the combination of the outputs of the best classifiers in a majority voting method that includes results of both the MRI and fMRI classifiers and keeps both feature selection results independent. The results suggest that ADHD is more easily identified through fMRI because the classification accuracies are a lot higher using fMRI data rather than MRI data. Furthermore, significant activity correlation differences exist between the brain's frontal lobe and cerebellum and also the left and right hemispheres among ADHD and non-ADHD subjects. When including MRI decisions with fMRI in the classifier ensemble, performance is boosted to a high ADHD detection accuracy of 96.2%, suggesting that MRI information assists in validating fMRI classification decisions. This study is an important step towards the development of an automatic and objective method for ADHD diagnosis. While more work is needed to externally validate and improve the classification accuracy, new applications of current methods with promising results are introduced here.
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Date Issued
2017
Identifier
CFH2000203, ucf:45978
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFH2000203
TISSUE ENGINEERED MYELINATION AND THE STRETCH REFLEX ARC SENSORY CIRCUIT: DEFINED MEDIUM FORMULATION, INTERFACE DESIGN AND MICROFABRICATION
Title
TISSUE ENGINEERED MYELINATION AND THE STRETCH REFLEX ARC SENSORY CIRCUIT: DEFINED MEDIUM FORMULATION, INTERFACE DESIGN AND MICROFABRICATION.
Creator
Rumsey, John, Hickman, James, University of Central Florida
Abstract / Description
The overall focus of this research project was to develop an in vitro tissue-engineered system that accurately reproduced the physiology of the sensory elements of the stretch reflex arc as well as engineer the myelination of neurons in the systems. In order to achieve this goal we hypothesized that myelinating culture systems, intrafusal muscle fibers and the sensory circuit of the stretch reflex arc could be bioengineered using serum-free medium formulations, growth substrate interface...
Show moreThe overall focus of this research project was to develop an in vitro tissue-engineered system that accurately reproduced the physiology of the sensory elements of the stretch reflex arc as well as engineer the myelination of neurons in the systems. In order to achieve this goal we hypothesized that myelinating culture systems, intrafusal muscle fibers and the sensory circuit of the stretch reflex arc could be bioengineered using serum-free medium formulations, growth substrate interface design and microfabrication technology. The monosynaptic stretch reflex arc is formed by a direct synapse between motoneurons and sensory neurons and is one of the fundamental circuits involved in motor control. The circuit serves as a proprioceptive feedback system, relaying information about muscle length and stretch to the central nervous system (CNS). It is composed of four elements, which are split into two circuits. The efferent or motor circuit is composed of an α-motoneuron and the extrafusal skeletal muscle fibers it innervates, while the afferent or sensory circuit is composed of a Ia sensory neuron and a muscle spindle. Structurally, the two muscular units are aligned in parallel, which plays a critical role modulating the system's performance. Functionally, the circuit acts to maintain appropriate muscle length during activities as diverse as eye movement, respiration, locomotion, fine motor control and posture maintenance. Myelination of the axons of the neuronal system is a vertebrate adaptation that enables rapid conduction of action potentials without a commensurate increase in axon diameter. In vitro neuronal systems that reproduce these effects would provide a unique modality to study factors influencing sensory neuronal deficits, neuropathic pain, myelination and diseases associated with myelination. In this dissertation, results for defined in vitro culture conditions resulting in myelination of motoneurons by Schwann cells, pattern controlled myelination of sensory neurons, intrafusal fiber formation, patterned assembly of the mechanosensory complex and integration of the complex on bio-MEMS cantilever devices. Using these systems the stretch sensitive sodium channel BNaC1 and the structural protein PICK1 localized at the sensory neuron terminals associated with the intrafusal fibers was identified as well as the Ca2+ waves associated with sensory neuron electrical activity upon intrafusal fiber stretch on MEMS cantilevers. The knowledge gained through these multi-disciplinary approaches could lead to insights for spasticity inducing diseases like Parkinson's, demyelinating diseases and spinal cord injury repair. These engineered systems also have application in high-throughput drug discovery. Furthermore, the use of biomechanical systems could lead to improved fine motor control for tissue-engineered prosthetic devices.
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Date Issued
2009
Identifier
CFE0002904, ucf:48013
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0002904
GUIDELINES FOR TWENTY-FIRST CENTURY INSTRUCTIONAL DESIGN AND TECHNOLOGY USE: TECHNOLOGIES' INFLUENCE ON THE BRAIN
Title
GUIDELINES FOR TWENTY-FIRST CENTURY INSTRUCTIONAL DESIGN AND TECHNOLOGY USE: TECHNOLOGIES' INFLUENCE ON THE BRAIN.
Creator
Gabriel, Jennifer, Flammia, Madelyn, University of Central Florida
Abstract / Description
The increasingly global environment has spurred the economy in the United States as well as the economies in nearly every other nation. Although the U.S. remains the world leader in the global economy, research shows that the United States is at risk of losing its place as the world leader in science and innovation. Policymakers have recognized the need for research addressing global competitiveness. President Bush signed the America Competes Act, which calls for increased investment in...
Show moreThe increasingly global environment has spurred the economy in the United States as well as the economies in nearly every other nation. Although the U.S. remains the world leader in the global economy, research shows that the United States is at risk of losing its place as the world leader in science and innovation. Policymakers have recognized the need for research addressing global competitiveness. President Bush signed the America Competes Act, which calls for increased investment in innovation and education to improve U.S. competitiveness and President Barack Obama has named a platform, "Science, Technology and Innovation for a New Generation" which will extend and prioritize the efforts to improve math and science education. K‐12 U.S. students are graduating from high school unprepared to pursue degrees in science, technology, engineering and math (STEM) in college. Without STEM degrees they will be unable to pursue technology jobs after graduation. Statistics show that the U.S. is failing to produce as many graduates in STEM as other countries. In an increasingly global world, without graduates in STEM courses the U.S. is at risk of losing its position as the economic world leader. Government, industry and academia all agree that the U.S. needs to address education on a K‐12 level to ensure that U.S. students are equipped with twenty‐first century skills to compete in a twenty‐first century global economy. Twenty‐first century students are different from students of previous generations. Researchers argue that changes in the environment, specifically an increased exposure to technology, have changed the brains of twenty‐first century students; twenty‐first century students learn differently. However, twenty‐first century students are being taught with an instructional curriculum that was designed for a previous generation that did not have the same exposure to technology. This is causing a digital‐divide that is hindering the achievement of students. The instructional curriculum needs to be updated to meet the needs of twenty‐first century students. This thesis addresses this need from a technical communication perspective by arguing that the instructional design of twenty‐first century learning materials should be improved by adhering to guidelines for twenty‐first century learning characteristics and twenty‐first century technology use. The guidelines support a national goal to improve K‐12 achievement in order to increase U.S. STEM graduates and increase the U.S.'s ability to compete in a global economy.
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Date Issued
2009
Identifier
CFE0002704, ucf:48183
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0002704
Characterization of a Spiking Neuron Model via a Linear Approach
Title
Characterization of a Spiking Neuron Model via a Linear Approach.
Creator
Jabalameli, Amirhossein, Behal, Aman, Hickman, James, Haralambous, Michael, University of Central Florida
Abstract / Description
In the past decade, characterizing spiking neuron models has been extensively researched as anessential issue in computational neuroscience. In this thesis, we examine the estimation problemof two different neuron models. In Chapter 2, We propose a modified Izhikevich model withan adaptive threshold. In our two-stage estimation approach, a linear least squares method anda linear model of the threshold are derived to predict the location of neuronal spikes. However,desired results are not...
Show moreIn the past decade, characterizing spiking neuron models has been extensively researched as anessential issue in computational neuroscience. In this thesis, we examine the estimation problemof two different neuron models. In Chapter 2, We propose a modified Izhikevich model withan adaptive threshold. In our two-stage estimation approach, a linear least squares method anda linear model of the threshold are derived to predict the location of neuronal spikes. However,desired results are not obtained and the predicted model is unsuccessful in duplicating the spikelocations. Chapter 3 is focused on the parameter estimation problem of a multi-timescale adaptivethreshold (MAT) neuronal model. Using the dynamics of a non-resetting leaky integrator equippedwith an adaptive threshold, a constrained iterative linear least squares method is implemented tofit the model to the reference data. Through manipulation of the system dynamics, the thresholdvoltage can be obtained as a realizable model that is linear in the unknown parameters. This linearlyparametrized realizable model is then utilized inside a prediction error based framework to identifythe threshold parameters with the purpose of predicting single neuron precise firing times. Thisestimation scheme is evaluated using both synthetic data obtained from an exact model as well asthe experimental data obtained from in vitro rat somatosensory cortical neurons. Results show theability of this approach to fit the MAT model to different types of reference data.
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Date Issued
2015
Identifier
CFE0005958, ucf:50803
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005958
Development of human and rodent based in vitro systems toward better translation of bench to bedside in vivo results
Title
Development of human and rodent based in vitro systems toward better translation of bench to bedside in vivo results.
Creator
Berry, Bonnie, Hickman, James, Khaled, Annette, Lambert, Stephen, Sugaya, Kiminobu, University of Central Florida
Abstract / Description
Prospective medicinal compounds progress through multiple testing phases before becoming licensed drugs. Testing of novel compounds includes a preclinical phase where the potential therapeutic is tested in vitro and/or in animal models in vivo to predict its potential efficacy and/or toxicity in humans. The failure of preclinical models to accurately predict human drug responses can lead to potentially dangerous compounds being administered to humans, or potentially beneficial compounds being...
Show moreProspective medicinal compounds progress through multiple testing phases before becoming licensed drugs. Testing of novel compounds includes a preclinical phase where the potential therapeutic is tested in vitro and/or in animal models in vivo to predict its potential efficacy and/or toxicity in humans. The failure of preclinical models to accurately predict human drug responses can lead to potentially dangerous compounds being administered to humans, or potentially beneficial compounds being kept in development abeyance. Moreover, inappropriate choice in model organism for studying disease states may result in pushing forward inappropriate drug targets and/or compounds and wasting valuable time and resources in producing much-needed medications. In this dissertation, models for basic science research and drug testing are investigated with the intention of improving current preclinical models in order to drive drugs to market faster and more efficiently. We found that embryonic rat hippocampal neurons, commonly used to study neurodegenerative disease mechanisms in vitro, take 3-4 weeks to achieve similar, critical ion-channel expression profiles as seen in adult rat hippocampal cultures. We also characterized a newly-available commercial cell line of human induced pluripotent stem cell-derived neurons for their applicability in long-term studies, and used them to develop a more pathologically relevant model of early Alzheimer's Disease in vitro. Finally, we attempted to create an engineered, layered neural network of human neurons to study drug responses and synaptic mechanisms. Utilization of the results and methods described herein will help push forward the development of better model systems for translation of laboratory research to successful clinical human drug trials.
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Date Issued
2015
Identifier
CFE0006261, ucf:51031
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006261