Machine Learning Approaches to Provide Spatio-Temporal Characterization of Human Functional Activities

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dc.contributor.advisorWegman, EdwardGandjbakhche, Amir
dc.contributor.authorShahni karamzadeh, Nader
dc.creatorShahni karamzadeh, Nader
dc.date.accessioned2016-09-28T10:23:05Z
dc.date.available2016-09-28T10:23:05Z
dc.date.issued2016
dc.description.abstractRecently, the interest in pattern recognition approaches to the analysis of clinical neuroimaging data has increased substantially. A crucial advantage of multivariate pattern recognition algorithms in comparison to the traditional univartiate approaches is that they provide predictions on the level of individual subjects. It is this multivariate nature of pattern recognition algorithms that results in increased sensitivity over univariate methods and has led to numerous applications in clinical research. Meanwhile, advances in neuroimaging technologies have improved our understanding of brain function in psychiatric and neurological disorders such as mood disorders, drug abuse and addiction, schizophrenia, Alzheimer’s disease, traumatic brain injury,-. These promising advances in functional neuroimaging technology and multivariate pattern recognition’s applications in neuroimaging data analysis motivated the work presented in this dissertation. Monitoring and evaluating of human brain performance during the execution of functional experiments have revealed evidence regarding distinctive pattern of brain activity between healthy individuals and individuals with brain functional disorders. Except for certain cases, to date, the results of these studies have had minimal clinical impact and despite much interest in the use of brain scans for diagnostic and prognostic purposes, traditional and often ineffective diagnostic and prognostic approaches are the common practice for neurologists and psychiatrists.
dc.format.extent148 pages
dc.identifier.urihttps://hdl.handle.net/1920/10456
dc.language.isoen
dc.rightsCopyright 2016 Nader Shahni karamzadeh
dc.subjectComputer science
dc.subjectNeurosciences
dc.subjectClassification
dc.subjectCluster Analysis
dc.subjectFunctional Biomarker
dc.subjectFunctional Connectivity
dc.subjectMachine learning
dc.subjectTime series Feature Extraction
dc.titleMachine Learning Approaches to Provide Spatio-Temporal Characterization of Human Functional Activities
dc.typeDissertation
thesis.degree.disciplineComputational Sciences and Informatics
thesis.degree.grantorGeorge Mason University
thesis.degree.levelPh.D.

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