Population-level task-evoked functional connectivity via Fourier analysis

Abstract Functional magnetic resonance imaging (fMRI) is a noninvasive and in-vivo imaging technique essential for measuring brain activity. Functional connectivity is used to study associations between brain regions, either while study subjects perform tasks or during periods of rest. In this paper...

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Published inJournal of the Royal Statistical Society Series C: Applied Statistics Vol. 73; no. 4; pp. 857 - 879
Main Authors Meng, Kun, Eloyan, Ani
Format Journal Article
LanguageEnglish
Published UK Oxford University Press 01.08.2024
Subjects
Original
Human Connectome Project
AMUSE algorithm
weakly stationary with mean zero
motor-task
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Summary:Abstract Functional magnetic resonance imaging (fMRI) is a noninvasive and in-vivo imaging technique essential for measuring brain activity. Functional connectivity is used to study associations between brain regions, either while study subjects perform tasks or during periods of rest. In this paper, we propose a rigorous definition of task-evoked functional connectivity at the population level (ptFC). Importantly, our proposed ptFC is interpretable in the context of task-fMRI studies. An algorithm for estimating the ptFC is provided. We present the performance of the proposed algorithm compared to existing functional connectivity frameworks using simulations. Lastly, we apply the proposed algorithm to estimate the ptFC in a motor-task study from the Human Connectome Project.
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Conflicts of interest: None declared.
ISSN:0035-9254
1467-9876
DOI:10.1093/jrsssc/qlae015