ICNeuromodulation Neumann Group
At our lab, we are combining multimodal research methods including intraoperative invasive human neurophysiology (Local field potentials and electrocorticography), non-invasive neurophysiology with magneto-/electroencephalography ( e.g. using newest generations of optically pumped magnetometers OPM ), 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, how such activity is implicated in cognition and neurological disorders and how these findings can be used for next-generation brain stimulation approaches for therapeutic translation.
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.
Non-invasive stimulation
In non-invasive stimulation techniques such as transcranial magnetic stimulation (TMS) or transcranial direct current stimulation (tDCS), the cortical activity is modulated by magnetic pulses or electric fields, whereby specific functions such as plasticity and inhibition can be examined. On the one hand, this is intended to characterize disease-specific changes in these functions in various movement disorders and to enable a better understanding of the pathophysiology. The effects of deep brain stimulation on these functions can also be examined. By combining activating and inhibiting stimulation techniques, the cortex-basal ganglia interaction is to be differentially modulated and examined with regard to possible therapeutically useful effects.
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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PUBLICATIONS
Andreas Horn, Ningfei Li, Till A Dembek, Ari Kappel, Chadwick Boulay, Siobhan Ewert, Anna Tietze, Andreas Husch, Thushara Perera, Wolf-Julian Neumann, Marco Reisert, Hang Si, Robert Oostenveld, Christopher Rorden, Fang-Cheng Yeh, Qianqian Fang, Todd M Herrington, Johannes Vorwerk, Andrea A Kühn
Ewgenia Barow, Wolf-Julian Neumann, Christof Brücke, Julius Huebl, Andreas Horn, Peter Brown, Joachim K Krauss, Gerd-Helge Schneider, Andrea A Kühn
Wolf‐Julian Neumann, Katharina Degen, Gerd‐Helge Schneider, Christof Brücke, Julius Huebl, Peter Brown, Andrea A Kühn
Wolf-Julian Neumann, Ashwani Jha, Antje Bock, Julius Huebl, Andreas Horn, Gerd-Helge Schneider, Tillmann H Sander, Vladimir Litvak, Andrea A Kühn
Wolf-Julian Neumann, Robert S Turner, Benjamin Blankertz, Tom Mitchell, Andrea A Kühn, R Mark Richardson
Meet The Team
Alessia Cavallo
GRADUATE STUDENT
Saeed Salehi
GRADUATE STUDENT
Richard Köhler
MEDICAL DOCTOR
Safaa A. Malkawi
MASTER 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.
Hakimeh Pourakbari
VISITING RESEARCHER