Functional connectivity-based attractor dynamics of the human brain in rest, task, and disease

Functional brain connectivity has been instrumental in uncovering the large-­scale organization of the brain and its relation to various behavioral and clinical phenotypes. Understanding how this functional architecture relates to the brain’s dynamic activity repertoire is an essential next step towards interpretable generative models of brain function. We propose functional connectivity-­ based Attractor Neural Networks (fcANNs), a theoretically inspired model of macro-­scale brain dynamics, simulating recurrent activity flow among brain regions based on first principles of self-­ organization. In the fcANN framework, brain dynamics are understood in relation to attractor states; neurobiologically meaningful activity configurations that minimize the free energy of the system. We provide the first evidence that large-­scale brain attractorsas reconstructed by fcANNsexhibit an approximately orthogonal organization, which is a signature of the self-­orthogonalization mechanism of the underlying theoretical framework of free-­energy-­minimizing attractor networks. Analyses of seven distinct human neuroimaging datasets demonstrate that fcANNs can accurately reconstruct and predict brain dynamics under a wide range of conditions, including resting and task states, and brain disorders. By establishing a formal link between connectivity and activity, fcANNs offer a simple and interpretable computational alternative to conventional descriptive analyses. Research Article *For correspondence: tamas.spisak@uk-essen.de Competing interest: The authors declare that no competing interests exist. Funding: See page 27 Preprint posted 11 April 2024 Sent for Review 28 May 2024 Reviewed preprint posted 05 September 2024 Reviewed preprint revised 24 December 2025 Version of Record published 18 March 2026 Reviewing Editor: Spase Petkoski, Institut de Neurosciences des Systèmes, Marseille, France ‍ ‍ Copyright Englert et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.