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




Shahni karamzadeh, Nader

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Recently, 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.



Computer science, Neurosciences, Classification, Cluster Analysis, Functional Biomarker, Functional Connectivity, Machine learning, Time series Feature Extraction