The Structured Mind at Rest: Low-Frequency Oscillations Reflect Interactive Dynamics Between Spontaneous Brain Activity and a Common Architecture for Task Control

Abstract

The Common Model of Cognition (CMC) has been proposed as a high level framework through which functional neuroimaging data can be predicted and interpreted. Previous work has found the CMC is capable of predicting brain activity across a variety of tasks, but it has not been tested on resting state data. This paper adapts a previously used method for comparing theoretical models of brain structure, Dynamic Causal Modeling, for the task-free environment of resting state, and compares the CMC against six alternate architectural frameworks while also separately modeling spontaneous low-frequency oscillations. For a large sample of subjects from the Human Connectome Project, the CMC provides the best account of resting state brain activity, suggesting the presence of a general purpose structure of connections in the brain that drives activity when at rest and when performing directed task behavior. At the same time, spontaneous brain activity was found to be present and significant across all frequencies and in all regions. Together, these results suggest that, at rest, spontaneous low-frequency oscillations interact with the general cognitive architecture for task-based activity. The possible functional implications of these findings are discussed.

Resting state brain subnetwork relates to prosociality and compassion in adolescents

“…While the present study cautiously suggests these relationships, it is important to test these model predictions specifically with a priori hypotheses in future studies. One explicit way to test these predictions in future resting state or task-based studies could be through Dynamic Causal Modeling (DCM), which could directly assess the validity of the Do-GooD model compared to other possible formulations ( Friston et al, 2013 ; Sibert et al, 2022 ).…”

Inferring a Cognitive Architecture from Multitask Neuroimaging Data: A Data‐Driven Test of the Common Model of Cognition Using Granger Causality

“…In addition, the images were then smoothed with an isotropic 8.0 mm full‐width half‐maximum Gaussian kernel. Although other preprocessing steps are often carried out (e.g., linear regression of motion components and removal of frequencies in the range of physiological noise as done in Sibert, Hake, & Stocco, 2022), they were omitted in this analysis to make it directly comparable to the original data of Stocco et al. (2021).…”