Current methods and new directions in resting state fMRI

• Published in: Clinical imaging Vol. 65; pp. 47 - 53
• Main Authors: Yang, Jackie, Gohel, Suril, Vachha, Behroze
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.09.2020; Elsevier Limited
• Subjects: Alzheimer's disease; Big data; Brain cancer; Brain mapping; Brain research; Cognition; Dynamic-connectivity; Functional magnetic resonance imaging; Functional-connectivity; IVA; Magnetic resonance imaging; Neural networks; Neuroimaging; Noise; Power; Resting-state-fMRI; Schizophrenia; Time series; Vector analysis; Dynamic-connectivity; Resting-state-fMRI; Big data; IVA; Functional-connectivity
• ISSN: 0899-7071; 1873-4499; 1873-4499
• DOI: 10.1016/j.clinimag.2020.04.004

Resting state functional connectivity magnetic resonance imaging (rsfcMRI) has become a key component of investigations of neurocognitive and psychiatric behaviors. Over the past two decades, several methods and paradigms have been adopted to utilize and interpret data from resting-state fluctuations in the brain. These findings have increased our understanding of changes in many disease states. As the amount of resting state data available for research increases with big datasets and data-sharing projects, it is important to review the established traditional analysis methods and recognize areas where research methodology can be adapted to better accommodate the scale and complexity of rsfcMRI analysis. In this paper, we review established methods of analysis as well as areas that have been receiving increasing attention such as dynamic rsfcMRI, independent vector analysis, multiband rsfcMRI and network of networks.