Identification of Effective Connectivity Subregions

TL;DR

This paper introduces two graphical search methods to identify voxel subregions responsible for connectivity between larger brain regions in high-resolution fMRI data, revealing detailed functional and anatomical insights beyond standard ROI aggregation.

Contribution

The paper presents novel scalable algorithms that pinpoint key voxel subregions driving ROI connectivity, improving upon traditional correlation-based methods by reducing false positives.

Findings

Both methods identified consistent voxel subregions across sessions and hemispheres.

Algorithms outperform Pearson and partial correlation in robustness against false positives.

Methods are computationally efficient for large-scale voxelwise connectivity analysis.

Abstract

Standard fMRI connectivity analyses depend on aggregating the time series of individual voxels within regions of interest (ROIs). In certain cases, this spatial aggregation implies a loss of valuable functional and anatomical information about smaller subsets of voxels that drive the ROI level connectivity. We use two recently published graphical search methods to identify subsets of voxels that are highly responsible for the connectivity between larger ROIs. To illustrate the procedure, we apply both methods to longitudinal high-resolution resting state fMRI data from regions in the medial temporal lobe from a single individual. Both methods recovered similar subsets of voxels within larger ROIs of entorhinal cortex and hippocampus subfields that also show spatial consistency across different scanning sessions and across hemispheres. In contrast to standard functional connectivity methods, both algorithms applied here are robust against false positive connections produced by common causes and indirect paths (in contrast to Pearson's correlation) and common effect conditioning (in contrast to partial correlation based approaches). These algorithms allow for identification of subregions of voxels driving the connectivity between regions of interest, recovering valuable anatomical and functional information that is lost when ROIs are aggregated. Both methods are specially suited for voxelwise connectivity research, given their running times and scalability to big data problems.