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
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hih-tuebingen.de
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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hih-tuebingen.de
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.
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Meet The Team
Alessia Cavallo
MASTER STUDENT
Thomas Samuel Binns
DOCTORAL ROTATION STUDENT
Medical doctor and scientist working on deep brain stimulation for movement disorders.
Experienced in human electrophysiology recordings, including electrocorticography, DBS and behavioral measures.
Interested in open-source software development.
Visit my GitHub profile to check out some of my openly available projects.
Richard Köhler, MD
RESEARCH ASSOCIATE
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Mousa Mustafa
DOCTORAL ROTATION STUDENT
Hakimeh Pourakbari
DOCTORAL STUDENT
Clara Sofia Heil
MASTER STUDENT
Elsa-Henriette Harms
MASTER STUDENT
Ali Dzaye
DOCTORAL ROTATION
MEDICAL STUDENT
Prarthita Sharma
MASTER STUDENT
Maria Mikhailenko
DOCTORAL ROTATION STUDENT
Martina Hysi
MASTER ROTATION STUDENT
Laura Freire Lyra
MASTER ROTATION STUDENT
Patricia Zvarova
MASTER ROTATION STUDENT
Wolf-Julian Neumann,
MD, PhD
PRINCIPLE INVESTIGATOR
DOCTORAL STUDENT
Jonathan Vanhoecke
I am an interdisciplinary scientist with a passion for programming. I am trying to help other researchers with their data, in how to standardize and automatize data processing. How can we obtain a better reproducibility in neuroimaging?
Other than the syntax in Python or Matlab, I love to study German, Spanish and other languages!
Meera Chikermane
DOCTORAL STUDENT
DOCTORAL STUDENT
Timon Merk
Through my research I am trying to understand neurological disorders like Parkinson’s disease and how treatment could be more effective. How can Brain Computer Interfaces in Deep Brain Stimulation be used to inform about pathology as well as behavior? And how can advanced signal processing and Machine Learning methods be utilized for a better care?
Towards that, I am analyzing electrophysiological signals in terms of movement decoding as well as pathological biomarker characterization, to propose novel methods for adaptive Deep Brain Stimulation.
