Population-level task-evoked functional connectivity via Fourier analysis

J R Stat Soc Ser C Appl Stat. 2024 Mar 14;73(4):857-879. doi: 10.1093/jrsssc/qlae015. eCollection 2024 Aug.

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.

Keywords: AMUSE algorithm; Human Connectome Project; motor-task; weakly stationary with mean zero.