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Neuroimage. 2019 Mar;188:502-514. doi: 10.1016/j.neuroimage.2018.12.037. Epub 2018 Dec 18.

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

Author information

1
Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA; Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA. Electronic address: huaxie@stanford.edu.
2
Machine Learning Team, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
3
Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
4
Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA; Functional MRI Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
5
The Mind Research Network, Albuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA.
6
Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA.

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 inter-regional 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 window-based methods: sliding window correlation (SWC), sliding window correlation with L1-regularization (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 delete-d 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 segments are not temporally continuous). We found that all window-based dFC methods performed well for commonly used window lengths (WL ≥ 30sec), with sliding window methods (SWC, SWC_L1) as well as the hybrid DCC_MA approach performing slightly better. For shorter window lengths (WL ≤ 15sec), DCC_MA and dJC produced the best results. Neither framewise method (i.e., DCC and JC) led to dFC estimates with high accuracy.

KEYWORDS:

Cognitive information; Dynamic conditional correlation; Dynamic functional connectivity; Jackknife correlation; Multiplication of temporal derivatives; Sliding window correlation

PMID:
30576850
PMCID:
PMC6401299
[Available on 2020-03-01]
DOI:
10.1016/j.neuroimage.2018.12.037

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