Featured Publication

Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson’s disease

Timon Merk, Victoria Peterson, Witold J Lipski, Benjamin Blankertz, Robert S Turner, Ningfei Li, Andreas Horn, Robert Mark Richardson, Wolf-Julian Neumann (2022) Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson’s disease eLife 11:e75126

https://doi.org/10.7554/eLife.75126

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Interventional & Cognitive Neuromodulation

led by Wolf-Julian Neumann (Dr. med.)
part of the Movement Disorders Unit,
Department of Neurology,
Charité - Berlin, Germany

In our lab, we combine multimodal research methods including invasive human neurophysiology (local field potentials and electrocorticography), non-invasive neurophysiology (magneto-/electroencephalography), and structural and functional neuroimaging with access to 3T MRI in the department of neuroradiology.

Our projects explore how neural activity is synchronized across multiple nodes of the motor circuit.

We investigate how such activity is implicated in neurological disorders and how these findings can be translated into next-generation therapeutic neuromodulation approaches.

Key research points

Brain Oscillations

Functional relevance of oscillatory activity on cognitive and motor control

Machine Learning

based and real-time prediction of human behaviour from invasive recordings

Parkinson’s Disease

Mechanisms of synaptic plasticity in the pathophysiology of the disease

Data Science

Developing solutions for multimodal clinical neuroscience research data

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DATA WEEK GALLERY

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Research

RESEARCH

Clinical Neurophysiological Studies

Deep brain recordings on the patient

Our working group deals with the importance of oscillatory basal ganglia activity, which can be recorded intra- and perioperatively via DBS electrodes in patients with movement disorders. The aim of our work is the therapy optimization of deep brain stimulation, e.g. through adaptive closed-loop stimulation. To do this, we research disease-specific activity patterns and investigate the modulation of this activity by the therapy. Through motor and cognitive experiments, we work out the differential connection of the basal ganglia in motor and non-motor loops. The parallel recording of 125 channel magnetoencephalography or 64 channel electroencephalography offers the unique opportunity to Characterize cortico-subcortical connectivity in the cortex-basal ganglia loop. We assume that the modulation of disease-specific network activity is one of the mechanisms of action of DBS and can therefore serve as the key to better therapy using adaptive neuromodulation.

Sensor-based motion analysis

In order to display therapy effects independently of the examiner and in the highest possible resolution, we use various sensor systems for the quantitative recording of motor skills in case of movement disorders. This enables us to map and quantify subtle but relevant effects for the patient as well as undesirable side effects of deep brain stimulation and to optimize the therapy accordingly. Thanks to the large number of movement parameters that can be examined and the combination with imaging, electrode localization and the determination of the anatomical structures that are each achieved by the deep brain stimulation, we develop a better understanding of the basal ganglia function, which has resulted in a more targeted application of DBS for so far difficult to treat symptoms can follow.

From experiment to open metadata repository - OSF

Computational reproducibility for FAIR translational neuromodulation research in clinical neuroscience.

We are generally working on the characterization of biomarkers and the development of algorithms for invasive adaptive neurostimulation. Our research has immediate translational potential for therapy optimization. However, a lack of scientific transparency and reproducibility hinders the integration of promising innovations in everyday clinical practice. The SWTFTPF will help us develop a groundbreaking data strategy for multimodal neuromodulation research. The aim of this project is to provide automated data flow algorithms from data acquisition to computationally reproducible publication.