Tools of the trade: Estimating time-varying connectivity patterns from fMRI data

• DOI: 10.31234/osf.io/mvqj4
• Publication Date: 2020-03-27T19:21:32Z
• Authors: Armin Iraji; Ashkan Faghiri; Noah Lewis; Zening Fu; Srinivas Rachakonda; Vince D Calhoun
• Keywords: bepress|Life Sciences|Neuroscience and Neurobiology; Brain Diseases; Brain Mapping; 03 medical and health sciences; 0302 clinical medicine; PsyArXiv|Neuroscience; Brain; Humans; Original Manuscript; Neuroimaging; Magnetic Resonance Imaging; Neuroscience

Given the dynamic nature of the brain, there has always been a motivation to move beyond “static” functional connectivity, which characterizes functional interactions over an extended period of time. Progress in data acquisition and advances in analytical neuroimaging methods now allow us to assess the whole brain’s dynamic functional connectivity (dFC) and its network-based analog, dynamic functional network connectivity (dFNC) at the macroscale (mm) using fMRI. This has resulted in the rapid growth of analytical approaches, some of which are very complex, requiring technical expertise that could daunt researchers and neuroscientists. Meanwhile, making real progress toward understanding the association between brain dynamism and brain disorders can only be achieved through research conducted by domain experts, such as neuroscientists and psychiatrists. This article aims to provide a gentle introduction to the application of dFC. We first explain what dFC is and the circumstances under which it can be used. Next, we review two major categories of analytical approaches to capture dFC. We discuss caveats and considerations in dFC analysis. Finally, we walk readers through an openly accessible toolbox to capture dFC properties and briefly review some of the dynamic metrics calculated using this toolbox.