Quantifying the compressibility of the human brain
Year: 2026
Type: article
Abstract: In the human brain, the allowed patterns of activity are constrained by the correlations between brain regions. Yet it remains unclear which correlations-and how many-are needed to predict large-scale neural activity. Here, we present an information-theoretic framework to identify the most important correlations, which provide the most accurate predictions of neural states. Applying our framework to cortical activity in humans, we find that the vast majority of variance in activity is explained by a small number of correlations. This means that the brain is highly compressible: Only a sparse network of correlations is needed to predict large-scale activity. We find that this compressibility is strikingly consistent across different individuals and cognitive tasks and that, counterintuitively, the most important correlations are not necessarily the strongest. Together, these results suggest that nearly all correlations are not needed to predict neural activity, and we provide the tools to uncover the key correlations that are.
Institutions: Yale University, Indiana University, University of Minnesota
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
Awards: G2803855152, NCS-FO: Edge-centric maps of functional brain network organization and dynamics, G8589369146, Dynamic approaches to understanding social cognitive aging: A social network neuroscience approach, Minimax entropy: Optimal statistical models for neural activity and chromatin structure
OpenAlex ID: W7125368808