ArticleProceedings of the National Academy of Sciences of the United States of America2026
Quantifying the compressibility of the human brain.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Developmental tuning of functional manifold dimensionality across the human brain.bioRxiv : the preprint server for biology · 2026Article
- Minimax entropy: The statistical physics of optimal models.Physical review. E · 2025Article
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4 authors.
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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.
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