The Effect of Multi-Channel tDCS on the Directed Connectivity Patterns of a Case with Focal Epilepsy Using a Multi-Feature Machine Learning Evaluation

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.