Computational Neurology
Christian Meisel
The Computational Neurology group develops theory- and data-based methods with the goal of achieving a more comprehensive understanding of neurological system function. In addition to the general goal of a more quantitative neurology, this approach should contribute to a more objective, real-time monitoring with continuous risk assessment and, based on this, enable proactive therapy approaches in epilepsy, intensive care and other areas.
To do this, we use multimodal time-series data from across the health-disease continuum, including data from wearables, neuromonitoring, video, and EEG.
An initial focus is on data analytics during inpatient monitoring. As part of the Data Warehouse Concept, we are developing computational methods for real-time monitoring and prediction of condition deterioration in the ICU and stroke unit.
A second focus is long-term ambulatory monitoring with wearables. Recent developments in ambulatory sensor technology combined with methodological advances in multimodal data analysis (machine learning, cloud-based analytics) offer opportunities for improved diagnostics through continuous long-term monitoring in the familiar, home environment and automated, computational data analysis. - Currently, we are investigating to what extent wearables enable a more objective recording of the clinical condition of epilepsy patients and detection or anticipation of epileptic seizures.
As a third focus, we are investigating the relevance of dynamical systems approaches to understanding cortical network activity, including in particular the hypothesis of critical brain activity.
Akkordeon
Publications
Low-frequency electrical stimulation reduces cortical excitability in the human brain
F. Manzouri, C. Meisel, L. Kunz, M. Dümpelmann, T. Stieglitz, A. Schulze-Bonhage
Brain Stimulation (submitted)
Machine learning from wristband sensor data for wearable, non-invasive seizure forecasting
C. Meisel, R. El Atrache, M. Jackson, S. Schubach, C. Ufongene, T. Loddenkemper
Epilepsia (under review), arXiv:1906.00511
Video-based detection of GTCSs using deep learning
Y. Yang, R. Sarkis, R. El Atrache, T. Loddenkemper, C. Meisel
IEEE (under review)
Implementation of an open communication platform for seizure forecasting into electronic seizure diaries: A technical report
S. Chiang, R. Moss, J. Bidwell, C. Meisel, T. Loddenkemper
JAMIA (under review)
Identifying markers of the preictal period in wristband sensor data with seizure surrogat data testing
S. Vieluf, R. El Atrache, M. Jackson, C. Reinsberger, T. Loddenkemper, C. Meisel
Scientific Reports (in print)
Controversies on the network theory of epilepsy: Debates held during the ICTALS 2019 conference
H. Zaveri, B. Schelter, C.A. Schevon, P. Jiruska, J. Jeffreys, G. Worrell, A. Schulze-Bonhage, R. Joshi, V. Jirsa, M. Goodfellow, C. Meisel, K. Lehnertz
Seizure: European Journal of Epilepsy. DOI: 10.1016/j.seizure.2020.03.010 (2020)
Antiepileptic drugs induce subcritical dynamics in human cortical networks
C. Meisel
Proc Natl Acad Sci USA; DOI: 10.1073/pnas.1911461117 (2020)
Critical slowing down as a biomarker for seizure susceptibility
M. Maturana, C. Meisel, et al.
Nature Communications; DOI: 10.1038/s41467-020-15908-3 (2020)