ArticleJournal of computational neuroscience2026
Network state transitions under deep brain stimulation: A Wilson-Cowan model of Parkinson's Disease.
Article in Journal of computational neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
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Abstract
Parkinson's disease is characterized by pathological beta-band oscillations ([Formula: see text]) arising from dopamine-depletion-induced instability in the basal ganglia-thalamocortical network. Although deep brain stimulation of the subthalamic nucleus is the most effective therapy for advanced Parkinson's disease, the mechanistic relationship between stimulation amplitude and network-state transitions remains poorly delineated, limiting the rational design of adaptive closed-loop protocols. We addressed this gap using a seven-population Wilson-Cowan mean-field model in which the Parkinsonian state was induced by reducing the STN→ GPe coupling weight from 19 to 5 and the DCN→ Th(Vim) cerebellar drive from 25 to 20. Six complementary analyses were applied across a continuous deep brain stimulation amplitude sweep of [Formula: see text]-15 a.u.: time-domain dynamics, Welch power spectral density, steady-state population profiling, Dose-response characterization using three validated metrics, phase-portrait geometry and bifurcation analysis. Three novel Dose-response metrics were introduced and supports: beta-band power suppression [Formula: see text], thalamic relay preservation and STN oscillation amplitude reduction. The Parkinson's disease network produced sustained [Formula: see text] beta oscillations, a multi-harmonic spectral profile and a large-amplitude STN→ GPe limit cycle. Sub-therapeutic stimulation ([Formula: see text]) left pathological dynamics unchanged; intermediate stimulation ([Formula: see text]) partially disrupted the oscillatory cycle; and high-amplitude stimulation ([Formula: see text]) abolished both beta oscillations and thalamic relay function via the GPi inhibitory cascade, constituting a model of functional thalamotomy. A transitional therapeutic window [Formula: see text] was identified in which beta suppression and thalamic preservation coexist, corroborated across all six analytical perspectives. Bifurcation analysis confirmed that reduced STN→ GPe gain is the primary instability mechanism, with cerebellar drive as a modulatory parameter. These findings provide mechanistically rigorous explanations of amplitude-dependent network state changes and offer a quantitative framework for adaptive closed-loop deep brain stimulation design.
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