Electrical and Computer Engineering ETDs

Publication Date

Fall 12-17-2016

Abstract

One of the earliest and fundamental observation in scientific study of the brain was discovering the relation between activities in different local regions of brain and some core functions of the brain. This was later followed by observing that not only local activities of regions but also synchronous activities between distributed brain regions play a key role in high-level brain functions. Synchronous activity related to the functions of the brain is commonly referred to as functional connectivity (FC) and is studied in the form of connectivity states of the brain which measure degree of interactions between distributed parts of the brain. Functional connectivity has been studied with different imaging modalities such as electroencephalogram (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). The latter has the advantage of having relatively higher spatial resolution of the underlying functional regions and is our choice for the source of the data in this work. Functional connectivity analysis of the human brain in fMRI researches focuses on identifying meaningful brain networks that have coherent activity either during a task or in the resting state. These networks are generally identified either as collections of voxels whose time series correlate strongly with a pre-selected region or voxel, or using data-driven methodologies such as independent component analysis (ICA) that compute sets of maximally spatially independent voxel weightings (component spatial maps (SMs)), each associated with a single time course (TC). Recent studies of functional connectivity have shed light on new aspects of functional connectivity. For example, connectivity during a resting state (RS) of the brain had long been know to constitute a single state of connectivity and just recently it is argued that RS-connectivity, varies in time and has a dynamic nature. In this work, we investigate new aspects of RS-connectivity jointly with its dynamic aspect. As part of the new observations, we discuss that RS-connectivity is in fact frequency dependent in addition to be temporally dynamic. This discovery allows to capture RS-coonectivity at a given time as the superposition of multiple connectivity states along frequency dimension. Later, we also show that interaction between fMRI networks is not only frequency dependent and temporally dynamic but also may occur cross different frequency spectra which is the first evidennce of cross-frequency depenence between fMRI functional networks. We also discuss that all of these observations would enable us to design novel measures to explain RS-connectivity variation among different group of subjects such as healthy and diseased or males and females which would have clinical diagnosis applications and could possibly serve as new bio-markers to study underlying functions of the brain.

Keywords

Medical Imaging, Biomedical Engineering, Functional magnetic resonance imaging, Brain Connectivity, Signal Processing, Mental Disorder, Schizophrenia

Sponsors

National Institutes of Health National Science Foundation

Document Type

Dissertation

Language

English

Degree Name

Computer Engineering

Level of Degree

Doctoral

Department Name

Electrical and Computer Engineering

First Committee Member (Chair)

Calhoun, Vince D.

Second Committee Member

Miller, Robyn L.

Third Committee Member

Erhardt, Erik B.

Fourth Committee Member

Martinez-Ramon, Manel

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