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On the Use of Independent Component Analysis & Functional Network Connectivity Analysis: Evaluation on Two Distinct Large-Scale Psychopathology Studies

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1928/11141

On the Use of Independent Component Analysis & Functional Network Connectivity Analysis: Evaluation on Two Distinct Large-Scale Psychopathology Studies

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Title: On the Use of Independent Component Analysis & Functional Network Connectivity Analysis: Evaluation on Two Distinct Large-Scale Psychopathology Studies
Author: Juárez, Michelle; 1982
Advisor(s): Calhoun, Vince
Committee Member(s): Pattichis, Marios
Clark, Vince
Kiehl, Kent
Department: University of New Mexico. Dept. of Electrical and Computer Engineering
Subject(s): functional connectivity
functional network connectivity
independent component analysis
schizophrenia
psychopathy
ICA
FNC
fMRI
LC Subject(s): Brain--Magnetic resonance imaging--Data processing.
Brain--Localization of functions--Multilevel models (Statistics)
Psychology, Pathological--Data processing.
Degree Level: Masters
Abstract: Medical image analysis techniques are becoming ever useful in allowing us to better understand the complexities and constructs of the brain and its functions. These analysis methods have proven to be integral in revealing trends in brain activity within individuals with mental disorders that are distinguishable from what would be considered “normal” activity within healthy populations. This has led us to gaining a better understanding of functional connectivity within the brain, especially within populations suffering from mental disorders. Functional magnetic resonance imaging (fMRI) is one of the leading techniques currently being implemented to explore cognitive function and aberrant brain activity resulting from mental illness. Functional connectivity has investigated the associations of spatially-remote neuronal activations in the brain. Independent component analysis (ICA) is the leading analytical method in functional connectivity research and has been extensively implemented in the analysis of fMRI data, allowing us to draw group inferences from that data. However, there is mounting interest in the functional network connectivity (FNC) among components estimated through group ICA. This type of analysis allows us to delve further into the temporal dependencies among components or “regions” within the brain. In this thesis, we investigate the implementation of group ICA and FNC analysis on two large-scale psychopathology studies -– the first from a multi-site study involving the comparison of schizophrenia patients with healthy controls using a sensorimotor task paradigm, and the second from an investigation in psychopathy in prisoners performing an auditory oddball task. In both studies, we analyzed the fMRI data with group ICA and implemented FNC analysis on the resulting ICA output. The purpose of these studies was to investigate differences in modulation of task-related and default mode networks, identifying any potential temporal dependencies among selected components. Our ultimate objective was to demonstrate the effective application of group ICA and FNC analysis together on two large-scale studies of two, distinct psychopathologies. The results of this combined method of analysis establish the practicality and general applicability of group ICA-FNC analysis in the growing fields of functional connectivity and functional network connectivity.
Graduation Date: July 2010
URI: http://hdl.handle.net/1928/11141

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