Measurement of steady state functional connectivity in the human brain using functional magnetic resonance imaging

Abstract

Functional magnetic resonance imaging (fMRI) is a noninvasive imaging technique capable of mapping cognitive networks in the human brain that has become widely used for both research and clinical applications. The activity of focal regions within the brain can be inferred based on their blood oxygen level dependent (BOLD) signal changes through time by recording a series of images while subjects perform carefully designed tasks. Steady state functional connectivity methods generate similar maps of neural networks through analyses of underlying activity. Resting state functional connectivity measurements reduce the demands placed on patient/subject compliance and can potentially extend fMRI to a wider range of applications. However, the factors that affect measurements of steady state functional connectivity remain unclear, and methods of making these measurements may be further improved.

This dissertation addresses three different aspects of functional connectivity measured with fMRI. First, we study the effects of cognitive load on measurements of functional connectivity in the working memory and default mode networks, building on previous work in the motor network. We report increases in functional connectivity within both networks, and present evidence of changing connectivity between them. Second, we apply functional connectivity analyses to electroencephalography and fMRI data recorded simultaneously to investigate the regions underlying variance in frontal theta power. Our results show both positive and negative correlations to theta power across working memory loads, and changing correlations in the parahippocampal gyrus, among other regions. Third, we demonstrate two methodological improvements to the measurement of functional connectivity using data acquired at ultra high field (7T) and applying nonlinear measurements of connectivity. We show that detection and significance of functional connectivity can be improved by decreasing voxel volumes to sizes that are not practically achievable without ultra-high magnetic fields, presumably due to decreases in partial volume effects. In addition, we show that mutual information can be used to detect functional connectivity between regions that are commonly left unidentified by measurements based on linear correlation coefficients.