Biology subjects

Wehmeyer, L.

Publications and source records attributed to Wehmeyer, L..

3 recordsLinked to original sources

bioRxiv2025

The role of STN beta oscillations on lower extremity muscle activity in Parkinsonian stepping

Freezing of gait (FOG) is a devastating symptom of Parkinsons disease (PD) often resulting in disabling falls and loss of independence. It affects half of patients, yet current therapeutic strategies are insufficient, and the underlying neural mechanisms remain poorly understood. This study investigated beta oscillation dynamics in the STN during different locomotor states, while examining the effects of levodopa. In particular, it aimed to identify pathological activity by analysing the relationship between the STN and lower limb muscles during stepping. Local field potentials (LFP) in the STN and muscle activity (EMG) of the gastrocnemius and peroneus longus were recorded in 14 PD patients during standing and stepping, ON and OFF levodopa. Levodopa reduced stepping variability, implying improved stepping abilities. Distinct STN beta patterns were observed between stepping and standing, with lower high-beta and higher low-beta during stepping compared to standing, suggesting a distinct role of these frequency bands in motor control during postural and movement states. Levodopa reduced low-beta but increased high-beta activity, highlighting a potential physiological function of high-beta in the STN during standing and stepping. In addition, step-phase specific effects of levodopa included reduced broad-beta band activity in the STN and lower limb muscles during the late-stance and pushing-off phase of the contralateral leg when ON medication. Further analyses suggest that pathological STN activity amplifies muscle activation around movement initiation, potentially reducing the ability of the patient to move freely. These findings offer insight for developing phase-specific stimulation strategies targeting STN beta oscillations during gait.

neuroscience↗

bioRxiv2024

Thalamo-frontal connectivity patterns in Tourette Syndrome: Insights from combined intracranial DBS and EEG recordings

Thalamic deep brain stimulation (DBS) has shown clinical improvement for patients with treatment-refractory Tourette Syndrome (TS). Advancing DBS for TS requires identifying reliable electrophysiological markers. Recognising TS as a network disorder, we investigated thalamo-cortical oscillatory connectivity by combining local field potential (LFP) recordings from the DBS thalamic target region using the PerceptTM PC neurostimulator with high-density EEG in eight male TS patients (aged 27 to 38) while stimulation was off. We identified a spatially and spectrally distinct oscillatory network connecting the medial thalamus and frontal regions in the alpha band (8-12 Hz), with connectivity strength negatively correlated with TS symptom severity. Moreover, reduced thalamo-frontal alpha connectivity before tic onset, localised in sensorimotor regions and the inferior parietal cortex, suggests its direct role in tic generation. Importantly, associations with symptoms and pre-tic dynamics were specific to connectivity patterns and not evident in the pure power spectra. These findings underscore the importance of investigating electrophysiological oscillatory connectivity to characterise pathological network connections in TS, potentially guiding stimulation-based interventions and future research on closed-loop DBS for TS.

neuroscience↗

bioRxiv2024

Evoked resonant neural activity outperforms spectral markers in decoding sleep from the subthalamic nucleus

BackgroundDeep brain stimulation is a treatment for advanced Parkinsons disease and currently tuned to target motor symptoms during daytime. Parkinsons disease is associated with multiple nocturnal symptoms such as akinesia, insomnia and sleep fragmentation which may require adjustments of stimulation during sleep for best treatment outcome. ObjectivesThere is a need for a robust biomarker to guide stimulation titration across sleep stages. This study aimed to investigate whether evoked resonant neural activity (ERNA) is modulated during sleep. MethodsWe recorded local field potentials from the subthalamic nucleus of four Parkinsons patients with externalised electrodes while applying single stimulation pulses to investigate the effect of sleep on ERNA. ResultsWe found that ERNA features change with wakefulness and sleep stages, and are correlated with canonical frequency bands and heart rate. We further evaluated the performance of machine learning models in classifying non-REM sleep versus wakefulness and found that ERNA amplitude outperforms all spectral markers. ConclusionsGiven the heterogeneity of spectral features during sleep, their susceptibility to movement artefacts and superior classification accuracy of models using ERNA features, this study paves the way for ERNA as a marker for automatic stimulation titration during sleep and improved patient care.

neuroscience↗