https://doi.org/10.1140/epjs/s11734-026-02310-1
Regular Article
Guiding self-organization in motor networks: a closed-loop, activity-dependent neuromodulation paradigm
1
Research Center in Artificial Intelligence, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, 23 Gagarin Ave., 603022, Nizhny Novgorod, Nizhny Novgorod region, Russian Federation
2
Department of Neurotechnology, Lobachevsky State University, 23 Gagarin Ave., 603022, Nizhny Novgorod, Nizhny Novgorod region, Russian Federation
3
Neuromorphic Computing Center, Neimark University, 6 Nartov St., 603081, Nizhny Novgorod, Nizhny Novgorod region, Russian Federation
a
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Received:
3
February
2026
Accepted:
3
April
2026
Published online:
20
April
2026
Abstract
The human brain functions as a complex, self-organizing system whose dynamics arise from intricate interactions across multiple scales. Following a stroke, this delicate balance can be disrupted, trapping the motor network in a pathological attractor state characterized by impairments like paresis and spasticity. Guiding the system out of this state and towards a functional reconfiguration remains a primary challenge for restorative therapy. This study introduces a closed-loop neuromodulation system designed to act as a controlled perturbation, nudging these intrinsic self-organizing dynamics. Our system bridges voluntary motor intention, detected via electromyography (EMG), with targeted transcranial magnetic stimulation (TMS) of the primary motor cortex (M1). By delivering stimulation contingent upon the detection of muscle contraction, we establish a precise temporal contingency that can reinforce task-specific neural states. In tests with sixteen healthy participants, the system demonstrated high-performance operation (100% detection accuracy, 1.14 ms latency) and induced significantly larger motor evoked potentials (MEPs) during grasping compared to rest. In a proof-of-concept study with a post-stroke patient, four training sessions promoted a shift in neural dynamics, evidenced by increased MEPs and improved muscle relaxation. We interpret these findings as evidence of the network being reconfigured towards a new, more functional attractor state. The results underscore the potential of such closed-loop systems not only for rehabilitation but also for fundamental research into how targeted perturbations can shape the self-organizing properties of neural networks and their higher-order interactions.
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjs/s11734-026-02310-1.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2026
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.
