Abstract
Human brain functional connectivity (FC) is often measured as the similarity of functional MRI responses across brain regions when a brain is either resting or performing a task. This paper aims to statistically analyze the dynamic nature of FC by representing the collective time-series data, over a set of brain regions, as a trajectory on the space of covariance matrices, or symmetric-positive definite matrices (SPDMs). We use a recently developed metric on the space of SPDMs for quantifying differences across FC observations, and for clustering and classification of FC trajectories. To facilitate large scale and high-dimensional data analysis, we propose a novel, metric-based dimensionality reduction technique to reduce data from large SPDMs to small SPDMs. We illustrate this comprehensive framework using data from the Human Connectome Project (HCP) database for multiple subjects and tasks, with task classification rates that match or outperform state-of-the-art techniques.
Highlights
T HERE has been a great interest in studying brain functional connectome generated from functional MRI, which measures the blood oxygen level dependent (BOLD) contrast signals inside the brain over a period of time
In this paper we have presented a comprehensive framework for comparing multivariate functional MRI (fMRI) time series, based on a distance on trajectories in the space symmetric-positive definite matrices (SPDMs)
We derive a method for SPDM dimension reduction, which saves significant computational costs while preserving pairwise distances as much as possible
Summary
T HERE has been a great interest in studying brain functional connectome generated from functional MRI (fMRI), which measures the blood oxygen level dependent (BOLD) contrast signals inside the brain over a period of time. The recent Human Connectome Project (HCP) [1] facilitates such research by providing high-quality publicly available dataset. The functional connectome, referred to as functional connectivity (FC), is estimated as a set of statistical dependencies of fMRI signals among remote anatomical regions. These dependencies are expressed as quantifications of similarity, correlation, or covariance between simultaneous. BOLD measurements across regions in human brain.
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