https://doi.org/10.1140/epjs/s11734-025-01702-z
Regular Article
Reservoir computing reconstructs blood-oxygen-level-dependent signals: whole-brain modeling study
1
Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 14 A. Nevskogo ul., 236016, Kaliningrad, Russia
2
A. V. Gaponov-Grekhov Institute of Applied Physics of the Russian Academy of Sciences, 46 Ul’yanov Street, 603155, Nizhny Novgorod, Russia
3
National Research University Higher School of Economics, 25/12 Bol’shaya Pecherskaya street, 603155, Nizhny Novgorod, Russia
Received:
1
April
2025
Accepted:
19
May
2025
Published online:
25
May
2025
Understanding and reconstructing brain dynamics from partial or noisy neuroimaging data remains a critical challenge in computational neuroscience. This study presents a novel framework combining neural mass modeling and reservoir computing (RC) to recover missing blood-oxygen-level-dependent (BOLD) signals while preserving functional connectivity patterns. We first simulate whole-brain dynamics using a Wilson–Cowan neural mass model with biologically realistic structural connectivity, optimizing parameters to match empirical functional connectivity matrices. Next, we employ RC to reconstruct individual BOLD signals using only the remaining signals as inputs. Our results demonstrate that RC achieves high-fidelity signal recovery, particularly for strongly interconnected regions. Crucially, the functional connectivity matrices derived from reconstructed signals show near-perfect agreement with the original simulated matrices, despite minor amplitude discrepancies. This work establishes RC as an effective tool for neuroimaging data reconstruction, with direct applications in both research and clinical settings where data loss or artifacts may compromise analyses.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2025
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
