| Literature DB >> 32353718 |
Jackie Yang1, Suril Gohel2, Behroze Vachha3.
Abstract
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.Entities:
Keywords: Big data; Dynamic-connectivity; Functional-connectivity; IVA; Resting-state-fMRI
Year: 2020 PMID: 32353718 DOI: 10.1016/j.clinimag.2020.04.004
Source DB: PubMed Journal: Clin Imaging ISSN: 0899-7071 Impact factor: 1.605