Dynamic neural states underpin motor symptom severity in Parkinson's disease: a longitudinal analysis of chronic cortico-subthalamic nucleus recordings.

BACKGROUND: Motor symptoms in Parkinson's disease (PD) may arise due to transient, network-wide neural dynamics that extend beyond beta-band oscillatory activity within the motor cortical-subthalamic nucleus (STN) circuit. METHODS: We applied a four-state Hidden Markov Model (HMM) to identify states of local and interregional oscillatory synchrony from chronic motor cortical and STN recordings (1046 h from 10 hemispheres) in five patients with PD (mean age 49 years), with concurrent measurements of bradykinesia, dyskinesia and tremor quantified using wearable sensors. FINDINGS: Neural states exhibited distinct spectral and temporal features relating to symptom severity. Two states exhibited spectral signatures-particularly STN low and high gamma, STN delta/alpha, cortical beta, and cortico-STN beta coherence-that predicted worsening bradykinesia. STN beta oscillations were not consistent predictors of bradykinesia (p = 0.52), but did predict worsening tremor (p < 0.01) and also improvements in dyskinesia severity (p < 0.001), in a state specific manner. These states also displayed compensatory features associated with bradykinesia amelioration, including cortical delta/alpha activity, cortical high gamma, and cortico-STN high gamma coherence. Additionally, we identified a state, marked by STN beta without cortico-STN beta coherence, whose increased lifetimes and occurrence improved motor function (p < 0.001). INTERPRETATION: Our findings highlight the multidimensional nature of motor impairments in PD and suggest that adaptive interventions targeting state features-rather than single frequency bands-may offer new opportunities for personalised neuromodulation. FUNDING: AO: MRC Clinician Scientist Fellowship (MR/W024810/1), Rosetrees Trust/Race Against Dementia Team award, Oxford Hospitals Charity, and the Jon Moulton Charity Trust. TL: China Scholarship Council.