Human cortical networks trade communication efficiency for computational reliability.

Overview

1. MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
2. Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
3. Department of Electrical and Electronic Engineering, Imperial College London, London, UK
4. Imperial-X, Imperial College London, London, UK
5. Centre for Eudaimonia and Human Flourishing, Linacre College and Department of Psychiatry, University of Oxford, Oxford, UK
6. St. John’s College, University of Cambridge, Cambridge, UK
7. Montreal Neurological Institute, Montreal, Canada
8. Developmental Imaging, Murdoch Children’s Research Institute, Parkville, Australia
9. School of Psychological Sciences, The Turner Institute for Brain and Mental Health, Monash University, Clayton, Australia
10. Department of Psychiatry, University of Cambridge, Cambridge, UK
11. Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
12. Department of Health Sciences, Boston University, Boston, MA, USA

Abstract

Brains are often described as cost-efficient communication networks that optimally balance long-connection costs against fast communication. Inspired by the “use it or lose it” principle, we present a game-theoretic model of self-organizing neural units showing the brain is suboptimal in both regards. Regional competition for connectivity under propagative dynamics yields networks resembling the human cortex yet more efficient and economical. In addition, using a reservoir computing framework, we find comparable information processing capacity, but synthetic optimal communication networks show lower computational reliability. Last, virtual lesions reveal why these networks are fragile: To optimize communication, they funnel information through a spatially clustered “oligarchy” of transmodal hubs. The human brain instead uses a distributed “rich-club” backbone that better resists targeted attacks, despite higher wiring costs and less efficient communication. Cortical networks thus trade both cost and efficiency for reliable computation, highlighting computational reliability as an overlooked and perhaps even more prominent driver of brain connectivity than wiring cost or communication efficiency.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• humanconnectome.org/study/hcp-young-adult, at Human Connectome Project; found in “Data, code, and materials availability:”

Data, code, and materials availability

All data and code needed to evaluate and reproduce the conclusions in the paper are present in the paper and/or the Supplementary Materials. This study did not generate new materials. Specifically, the simulated optimal communication networks are available at: https://doi.org/10.5281/zenodo.20446126. The dataset used in this work is available at: www.humanconnectome.org/study/hcp-young-adult/document/900-subjects-data-release, and the following open-source Python libraries were used: YANAT (Yet Another Network Analysis Toolkit; https://kuffmode.github.io/YANAT/) for the game-theoretic framework, Echoes (https://fabridamicelli.github.io/echoes/) for neuromorphic modeling, Netneurotools (https://netneurotools.readthedocs.io/en/latest/) for thresholding empirical networks, Brain Connectivity Toolbox (BCT; https://github.com/aestrivex/bctpy) for constructing degree-preserved null models, Networkx (https://networkx.org/) for constructing fully random networks, and Neuromaps (https://netneurolab.github.io/neuromaps/) for comparing brain maps. A plug-and-play tutorial for the game-theoretic generative model, including features that were not explored here, is also available at: https://github.com/kuffmode/YANAT/blob/main/examples/generative_game_theoric.ipynb.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 6 MeSH terms, 2 funders, 99 references.

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This paper

Fakhar, K., Akarca, D., Luppi, A. I., Oldham, S., Hadaeghi, F., Vértes, P. E., Bullmore, E., Hilgetag, C., & Astle, D. (2026). Human cortical networks trade communication efficiency for computational reliability. Science advances, 12(36), eaef2894. https://doi.org/10.1126/sciadv.aef2894

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