Neumann, W. J. | Search bioRxiv

bioRxiv2025

Dynamic neural states underpin bradykinesia severity in Parkinsons disease

BackgroundBradykinesia in Parkinsons 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. MethodsWe address this question by using Hidden Markov Models (HMMs) to identify neural states from chronic motor cortical and STN recordings in five PD patients (1,046 hours from 10 hemispheres), with concurrent measurements of bradykinesia using wearable sensors. FindingsWe identified four neural states with distinct spectral and temporal features. 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. However, STN beta power alone was not consistently predictive, challenging traditional beta-centric views. These states also displayed compensatory features associated with bradykinesia amelioration, including cortical delta/alpha activity, cortical high gamma, and cortico-STN high gamma coherence. Two additional states affected bradykinesia through temporal rather than spectral properties. Prolonged lifetimes of one of these state worsened symptoms, whereas increased occurrences of another, marked by local beta without cortico-STN beta coherence, improved motor function. InterpretationOur findings highlight the multidimensional nature of bradykinesia and suggest that state-aware, adaptive interventions targeting state features--rather than single frequency bands--may offer new opportunities for improved deep brain stimulation in PD. FundingAO is supported by an MRC Clinician Scientist Fellowship (MR/W024810/1) and a Rosetrees Trust/Race Against Dementia Team award. BA and AO acknowledge funding support from the Oxford University Hospitals Charity and Jon Moulton Trust. TL acknowledges funding support from the China Scholarship Council. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSPrevious work has demonstrated that subthalamic nucleus oscillatory activity at beta (15-30 Hz) frequencies correlates positively with motor symptoms in Parkinsons disease. This has led to beta activity being used as a biomarker for adaptive Deep Brain Stimulation. It remains unclear however whether other oscillatory features within the broader motor cortical-subthalamic nucleus network could provide improved biomarkers for tracking symptom severity. Added value of this studyWe address this by performing chronic motor cortical and subthalamic nucleus recordings in Parkinsons disease patients during activities of daily living. Simultaneous measurements of symptom severity were captured using wearable sensors. We used Hidden Markov Models to identify transient states of neural activity and related these to symptom severity. Although cortical beta and cortico-STN beta coherence predicted worsening motor symptoms, STN beta activity alone was not a consistent predictor. Interestingly, we identified spectral features associated with motor symptom improvements, including cortical delta/alpha activity, cortical high gamma, and cortico-STN high gamma coherence. Additionally, there was a compensatory state characterised by short-lived cortical and subthalamic nucleus beta activity, whose increased occurrence was associated with symptomatic improvements. Implications of all the available evidenceOur findings highlight the importance of prolonged, high temporal resolution measurements of both neural activity and symptom severity for discovering adaptive Deep Brain Stimulation biomarkers. Crucially, we identify new target states and spectral features for improving motor symptoms in Parkinsons disease.

neuroscience↗

bioRxiv2018

Subthalamic nucleus and sensorimotor cortex activity during speech production

The sensorimotor cortex is somatotopically organized to represent the vocal tract articulators, such as lips, tongue, larynx, and jaw. How speech and articulatory features are encoded at the subcortical level, however, remains largely unknown. We analyzed local field potential (LFP) recordings from the subthalamic nucleus (STN) and simultaneous electrocorticography recordings from the sensorimotor cortex of 11 patients (1 female) with Parkinsons disease during implantation of deep brain stimulation (DBS) electrodes, while patients read aloud three-phoneme words. The initial phonemes involved either articulation primarily with the tongue (coronal consonants) or the lips (labial consonants). We observed significant increases in high gamma (60-150 Hz) power in both the STN and the sensorimotor cortex that began before speech onset and persisted for the duration of speech articulation. As expected from previous reports, in the sensorimotor cortex, the primary articulator involved in the production of the initial consonant was topographically represented by high gamma activity. We found that STN high gamma activity also demonstrated specificity for the primary articulator, although no clear topography was observed. In general, subthalamic high gamma activity varied along the ventral-dorsal trajectory of the electrodes, with greater high gamma power recorded in the more dorsal locations of the STN. These results demonstrate that articulator-specific speech information is contained within high gamma activity of the STN, with similar temporal but less specific topographical organization, compared to similar information encoded in the sensorimotor cortex.\n\nSIGNIFICANCE STATEMENTClinical and electrophysiological evidence suggests that the subthalamic nucleus is involved in speech, however, this important basal ganglia node is ignored in current models of speech production. We previously showed that subthalamic nucleus neurons differentially encode early and late aspects of speech production, but no previous studies have examined subthalamic functional organization for speech articulators. Using simultaneous local field potential recordings from the sensorimotor cortex and the subthalamic nucleus in patients with Parkinsons disease undergoing deep brain stimulation surgery, we discovered that subthalamic nucleus high gamma activity tracks speech production at the level of vocal tract articulators, with high gamma power beginning to increase prior to the onset of vocalization, similar to cortical articulatory encoding.

neuroscience↗