Efficacy of different dynamic functional connectivity methods to capture cognitively relevant information

Abstract

Given the dynamic nature of the human brain, there has been an increasing interest in investigating short-term temporal changes in functional connectivity, also known as dynamic functional connectivity (dFC), i.e., the time-varying interregional statistical dependence of blood oxygenation level-dependent (BOLD) signal within the constraints of a single scan. Numerous methodologies have been proposed to characterize dFC during rest and task, but few studies have compared them in terms of their efficacy to capture behavioral and clinically relevant dynamics. This is mostly due to lack of a well-defined ground truth, especially for rest scans. In this study, with a multitask dataset (rest, memory, video, and math) serving as ground truth, we investigated the efficacy of several dFC estimation techniques at capturing cognitively relevant dFC modulation induced by external tasks. We evaluated two framewise methods (dFC estimates for a single time point): dynamic conditional correlation (DCC) and jackknife correlation (JC); and five windowbased methods: sliding window correlation (SWC), sliding window correlation with L1regularization (SWC_L1), a combination of DCC and SWC called moving average DCC (DCC_MA), multiplication of temporal derivatives (MTD), and a variant of jackknife correlation called deleted jackknife correlation (dJC). The efficacy is defined as each dFC metric's ability to successfully subdivide multitask scans into cognitively homogenous segments (even if those Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

A unified approach for characterizing static/dynamic connectivity frequency profiles using filter banks

“…A sliding window will also act as a low pass filter if used with these and any other estimators. For a detailed comparison of various dFNC estimators see (Xie, et al, 2019).…”

Manifold Learning of Dynamic Functional Connectivity Reliably Identifies Functionally Consistent Coupling Patterns in Human Brains

“…A sliding-window correlation approach was applied to characterize dynamic functional connectivity (dFC) [4], which had been proven to be a high efficacy framewise dFC method in the numerous dFC estimation methodologies [28]. The temporal window, parameterized by its window size, was shifted by a slide step along a set of timecourses which were extracted from the fMRI images using the 160 ROIs.…”

On the nature of time-varying functional connectivity in resting fMRI

“…There is robust evidence that static BOLD FC patterns flexibly reconfigure across a range of experimental scenarios (Cohen and D'Esposito 2016;Cole, et al 2014;Cole, et al 2013;Gratton, et al 2018;Mattar, et al 2015;Shirer, et al 2011;Xie, et al 2019a;Xie, et al 2019b) (see for a recent review of this literature). The most well studied examples involve the modulation of connectivity during cued cognitive task performance.…”