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

2026-02-03

AI Summary

Current fMRI analyses predominantly focus on activation or functional connectivity, lacking quantitative measures of information storage and transfer during cognitive tasks. This study proposes the first task-based fMRI framework for quantifying information processing by integrating task and resting-state data. Leveraging cross-mutual information to address challenges of small sample sizes and non-stationarity, the framework systematically computes active information storage (AIS), transfer entropy (TE), and net synergy to characterize regional information maintenance, directed information flow, and higher-order interactions. Applied to N-back task data from 470 participants, the approach reveals increasing AIS in frontoparietal regions with working memory load, enhanced TE along control pathways, and a global shift from synergistic to redundant information integration, thereby uncovering the dynamic information processing mechanisms underlying working memory.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Information Theory
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Web Mining and Content Analysis: Content-based information diffusion
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems

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.

Problem

• information processing
• fMRI
• working memory
• information theory
• cognitive tasks

Innovation

• information processing
• transfer entropy
• active information storage
• cross mutual information
• task-based fMRI