Population-level Task-evoked Functional Connectivity via Fourier Analysis

TL;DR

This paper introduces a new method for estimating population-level task-evoked functional connectivity in fMRI data, providing interpretability and demonstrating its effectiveness through simulations and real motor-task data analysis.

Contribution

It offers a rigorous, interpretable definition of task-evoked functional connectivity at the population level and an algorithm for its estimation, validated with simulations and real data.

Findings

The proposed algorithm outperforms existing frameworks in simulations.

It successfully estimates task-evoked connectivity in a human motor-task study.

The method provides interpretable connectivity measures in task-fMRI analysis.

Abstract

Functional magnetic resonance imaging (fMRI) is a non-invasive 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.