Estimating measures of information processing during cognitive tasks using functional magnetic resonance imaging

TL;DR

This paper introduces a new framework for quantifying information processing in task-based fMRI, measuring storage, transfer, and synergy to better understand cognitive functions during tasks.

Contribution

It presents a novel methodology using information-theoretic measures applied to fMRI data, addressing limitations of previous analyses focused on activation.

Findings

AIS increases in fronto-parietal regions with working memory load

TE shows enhanced directed information flows across control pathways

Net synergy shifts towards redundancy during tasks

Abstract

Cognition is increasingly framed in terms of information processing, yet most fMRI analyses focus on activation or functional connectivity rather than quantifying how information is stored and transferred. To remedy this problem, we propose a framework for estimating measures of information processing: active information storage (AIS), transfer entropy (TE), and net synergy from task-based fMRI. AIS measures information maintained within a region, TE captures directed information flow, and net synergy contrasts higher-order synergistic to redundant interactions. Crucially, to enable this framework we utilised a recently developed approach for calculating information-theoretic measures: the cross mutual information. This approach combines resting-state and task data to address the challenges of limited sample size, non-stationarity and context in task-based fMRI. We applied this framework to the working memory (N-back) task from the Human Connectome Project (470 participants). Results show that AIS increases in fronto-parietal regions with working memory load, TE reveals enhanced directed information flows across control pathways, and net synergy indicates a global shift to redundancy. This work establishes a novel methodology for quantifying information processing in task-based fMRI.