Timon Merk

The paper develops a pipeline to estimate neuronal tuning for word meaning from naturalistic speech and validates it using patient data and ground-truth controls.

This study uses deep neurobehavioral phenotyping to identify neural features associated with gait deficits in Parkinson's disease, revealing biomarkers for potential therapeutic applications.

This study shows that state-dependent deep brain stimulation in Parkinson's disease increases movement speed more effectively during fast movements, using machine learning to decode brain signals for real-time adjustments.

The paper identifies movement execution as a distinct neural state in Parkinson's dyskinesia, showing it enhances decoding of dyskinesia through specific oscillatory patterns.

This study identifies how dopamine and deep brain stimulation affect cortex-basal ganglia networks at different scales, showing distinct mesoscale effects and shared macroscale suppression of network synchrony.

This study identifies a shared brain network modulated by dopamine that underlies beta oscillations, revealing their role in cortico-subcortical circuits beyond motor function.

This study investigates how dopamine and deep brain stimulation (DBS) affect neural dynamics in Parkinson's disease by measuring motor intention latency and oscillatory communication patterns.

This paper develops a movement decoding pipeline using spatio-spectral features from cortical and subcortical signals, showing improved decoding with spatial information.

This paper compares electrocorticography (ECoG) and subthalamic local field potentials (LFP) for movement decoding in Parkinson's disease, finding ECoG superior and demonstrating the impact of PD pathophysiology on decoding performance.