Task-related edge density (TED) - a new method for revealing large-scale network formation in fMRI data of the human brain

TL;DR

The paper introduces Task-related Edge Density (TED), a novel fMRI analysis method that detects large-scale, task-related brain network formation without relying on hemodynamic models or data segmentation.

Contribution

TED is a new data analysis algorithm that identifies dynamic, task-related brain networks in fMRI data, overcoming limitations of traditional methods.

Findings

TED effectively detects large-scale brain networks during tasks.

TED reveals networks missed by traditional GLM analysis.

The method is scalable to large voxel datasets.

Abstract

The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges in a brain network that differentially respond in unison to a task onset and that occur in dense packs with similar characteristics. Hence, we call this approach "Task-related Edge Density" (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function.