Decoding task specific and task general functional architectures of the brain

Abstract

Functional magnetic resonance imaging (fMRI) is used to capture complex and dynamic interactions between brain regions while performing tasks. Task related alterations in the brain have been classified as task specific and task general, depending on whether they are particular to a task or common across multiple tasks. Using recent attempts in interpreting deep learning models, we propose an approach to determine both task specific and task general architectures of the functional brain. We demonstrate our methods with a reference-based decoder on deep learning classifiers trained on 12,500 rest and task fMRI samples from the Human Connectome Project (HCP). The decoded task general and task specific motor and language architectures were validated with findings from previous studies. We found that unlike intersubject variability that is characteristic of functional pathology of neurological diseases, a small set of connections are sufficient to delineate the rest and task states. The nodes and connections in the task general architecture could serve as potential disease biomarkers as alterations in task general brain modulations are known to be implicated in several neuropsychiatric disorders.

Keywords: brain decoding; deep learning; functional MRI; functional connectivity; task general architecture; task specific architecture.

FIGURE 1

The decoded task general brain architecture obtained from consensus over multiple runs. Edge thickness corresponds to the number of runs these connections were classified as relevant for distinguishing rest and task states, and the node color corresponds to the node's modular membership

FIGURE 2

The task general architectures in the brain at the modular level. The part figures (a: negative task general) and (b: positive task general) depict the modular salience matrices for negative and positive task general connections, respectively

FIGURE 3

The motor task specific architecture in the brain. Figure shows the top 0.1% task specific connections for the motor task. The edge thickness corresponds to the salience score and the node color corresponds to the node's modular membership

FIGURE 4

The language task specific architecture in the brain. Figure shows the top 0.1% task specific connections for the language task. The edge thickness corresponds to the salience score and the node color corresponds to the node's modular membership

FIGURE 5

The salient anatomical connections for the task specific language architecture in the brain. The part figures (a: Language positive task specific) and (b: Language negative task specific) depict the salient connections between regions for the positive and negative language task specific connections, respectively

FIGURE 6

Intersubject variability in task related architectures. Figures (a: Task general features; b: Motor task specific features; c: Language task specific features) shows the scatter plot for the mean and variability in the task general and task specific salience scores across subjects. The blue markers represent the decoded task related features selected and the red curve represents the quadratic polynomial regression fit for the selected features