Causally informed activity flow models provide mechanistic insight into the emergence of cognitive processes from brain network interactions

Abstract

AbstractBrain activity flow models estimate the movement of task-evoked activity over brain connections to help explain the emergence of task-related functionality. Activity flow estimates have been shown to accurately predict task-evoked brain activations across a wide variety of brain regions and task conditions. However, these predictions have had limited explanatory power, given known issues with causal interpretations of the standard functional connectivity measures used to parameterize activity flow models. We show here that functional/effective connectivity (FC) measures grounded in causal principles facilitate mechanistic interpretation of activity flow models. Starting from Pearson correlation (the current field standard), we progress from FC measures with poor to excellent causal grounding, demonstrating a continuum of causal validity using simulations and empirical fMRI data. Finally, we apply a causal FC method to a dorsolateral prefrontal cortex region, demonstrating causal network mechanisms contributing to its strong activation during a 2-back (relative to a 0-back) working memory task. Together, these results reveal the promise of parameterizing activity flow models using causal FC methods to identify network mechanisms underlying cognitive computations in the human brain.Highlights-Activity flow models provide insight into how cognitive neural effects emerge from brain network interactions.-Functional connectivity methods grounded in causal principles facilitate mechanistic interpretations of task activity flow models.-Mechanistic activity flow models accurately predict task-evoked neural effects across a wide variety of brain regions and cognitive tasks.