Analysis Techniques for Dynamic Functional Connectivity Analysis Using fMRI

TOPICAL COLLECTIONS

Guest Editor

Matthew DeSalvo

Harvard Medical School

Dr. Matthew DeSalvo is a neuroradiologist at Brigham and Women's Hospital in Boston, MA and an Instructor in Radiology at Harvard Medical School. His primary research interest is using advanced MRI techniques such as functional MRI and diffusion imaging to better understand chronic neurological conditions such as epilepsy. He has previously conducted neuroimaging research in both animal models and human patients with epilepsy at both Yale School of Medicine and Massachusetts General Hospital to study the pathophysiology of epilepsy as well as to improve presurgical planning. In particular, he is interested in evaluating structural and functional brain connectivity in both the healthy state as well as in the setting of pathology.

Collection Overview

Analysis of resting-state functional connectivity using magnetic resonance imaging has emerged as a powerful non-invasive tool to study a variety of physiologic and pathological processes in the brain. This analysis has been principally limited to measuring temporal coherence or correlation between blood-oxygen-level-dependent (BOLD) signals among brain regions(under the assumption that functional connectivity is static over time). However, there is considerable evidence to suggest that functional connectivity is a dynamic process; even over short time intervals in which variations in both the strength and number of connections between various brain regions have been observed. Several techniques have been developed to study functional connectivity dynamics using fMRI; these include (1) sliding temporal window analysis, (2) time-frequency analysis, (3) temporal independent component analysis and (4) deep learning methods. This method collection describes a set of approaches to analyze functional connectivity dynamics using fMRI, although these methods can be applied to or combined with other neuroimaging modalities.