Novel Bayesian method for simultaneous detection of activation signatures and background connectivity for task fMRI data

TL;DR

This paper presents a Bayesian method for analyzing task fMRI data that detects activation signatures and background connectivity simultaneously, using a scalable tensor-based spatial-temporal basis approach.

Contribution

The paper introduces a novel hybrid tensor spatial-temporal basis strategy for scalable Bayesian analysis of fMRI data, capturing complex intervoxel correlations and background connectivity.

Findings

Increased sensitivity for identifying activation signatures.

Revealed biological insights through background connectivity analysis.

Applied to Human Connectome Project data to uncover brain activation patterns.

Abstract

In this paper, we introduce a new Bayesian approach for analyzing task fMRI data that simultaneously detects activation signatures and background connectivity. Our modeling involves a new hybrid tensor spatial-temporal basis strategy that enables scalable computing yet captures nearby and distant intervoxel correlation and long-memory temporal correlation. The spatial basis involves a composite hybrid transform with two levels: the first accounts for within-ROI correlation, and second between-ROI distant correlation. We demonstrate in simulations how our basis space regression modeling strategy increases sensitivity for identifying activation signatures, partly driven by the induced background connectivity that itself can be summarized to reveal biological insights. This strategy leads to computationally scalable fully Bayesian inference at the voxel or ROI level that adjusts for multiple testing. We apply this model to Human Connectome Project data to reveal insights into brain activation patterns and background connectivity related to working memory tasks.