The Effect of Multi-Channel tDCS on the Directed Connectivity Patterns of a Case with Focal Epilepsy Using a Multi-Feature Machine Learning Evaluation
Tsipourakis A, Antonakakis M, Kaiser F, Rampp S, Kovac S, Kellinghaus C, Möddel G, Wolters CH, Zervakis M (2025)
Publication Language: English
Publication Type: Conference contribution
Publication year: 2025
Publisher: Institute of Electrical and Electronics Engineers Inc.
Pages Range: 38-45
Conference Proceedings Title: 2024 IEEE 24th International Conference on Bioinformatics and Bioengineering (BIBE)
Event location: Kragujevac
ISBN: 979-8-3315-1863-9
DOI: 10.1109/BIBE63649.2024.10820462
Abstract
The present study explores the effect of multichannel transcranial Direct Current Stimulation (mc-tDCS) on directed EEG connectivity patterns in a patient with refractory focal epilepsy. A double-blind, sham-controlled N -of- 1 trial was conducted, where mc-tDCS was applied over a two-week period, with EEG recordings acquired before and after stimulation and sham procedures accordingly. After artifact reduction on the EEG recordings, Generalized Partial Directed Coherence (gPDC) was utilized, to investigate effective connectivity alterations in the patient's EEG recordings. Machine learning models were also employed to evaluate the connectivity findings and the interictal spike-related class (spike / non-spike) separability. The connectivity analysis demonstrated a significant reduction in gPDC connectivity around the key EEG channels associated with epileptic activity, specifically interictal epileptiform discharges (IEDs), following mc-tDCS, with no significant changes observed in the sham condition. Following feature extraction from the connectivity analysis, machine learning validation supported these findings, revealing a potential decrease in the severity of epileptic activity, as indicated by IEDs. The results suggest that mc-tDCS effectively moderates brain connectivity in refractory focal epilepsy, with implications for reducing the frequency of IEDs. This study highlights the potential of integrating advanced connectivity analysis with machine learning for evaluating mctDCS and similar neuromodulation therapies in epilepsy, particularly in modulating pathological brain network dynamics associated with epileptic discharges.
Authors with CRIS profile
Involved external institutions
Klinikum Osnabrück
Germany (DE)
Universitätsklinikum Münster
Germany (DE)
Westfälische Wilhelms-Universität (WWU) Münster
Germany (DE)
Technical University of Crete (TUC) / Πολυτεχνείο Κρήτης
Greece (GR)
Germany (DE)
Universitätsklinikum Münster
Germany (DE)
Westfälische Wilhelms-Universität (WWU) Münster
Germany (DE)
Technical University of Crete (TUC) / Πολυτεχνείο Κρήτης
Greece (GR)
How to cite
APA:
Tsipourakis, A., Antonakakis, M., Kaiser, F., Rampp, S., Kovac, S., Kellinghaus, C.,... Zervakis, M. (2025). The Effect of Multi-Channel tDCS on the Directed Connectivity Patterns of a Case with Focal Epilepsy Using a Multi-Feature Machine Learning Evaluation. In Nenad Filipovic (Eds.), 2024 IEEE 24th International Conference on Bioinformatics and Bioengineering (BIBE) (pp. 38-45). Kragujevac, RS: Institute of Electrical and Electronics Engineers Inc..
MLA:
Tsipourakis, Alexandra, et al. "The Effect of Multi-Channel tDCS on the Directed Connectivity Patterns of a Case with Focal Epilepsy Using a Multi-Feature Machine Learning Evaluation." Proceedings of the 24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024, Kragujevac Ed. Nenad Filipovic, Institute of Electrical and Electronics Engineers Inc., 2025. 38-45.
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