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Neurophysiologic Biomarkers of Invasive Neuromodulation Therapy for Epilepsy.

Francois Okoroafor1, Zekai Qiang1, Sophie Rosenke1

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Summary

Electroencephalography (EEG) biomarkers like interictal epileptiform discharges (IEDs) and synchronization measures show promise for predicting treatment success in drug-resistant epilepsy (DRE) neuromodulation. Methodological choices may influence these predictive biomarker findings.

Keywords:
BiomarkersEEGdrug-resistant epilepsyneuromodulationsynchronization

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Clinical Neurology

Background:

  • Epilepsy impacts 65 million globally, with 30% developing drug-resistant epilepsy (DRE).
  • Neuromodulation (DBS, RNS, VNS) offers treatment but efficacy assessment is lengthy.
  • Predictive biomarkers could expedite treatment selection and enable closed-loop neuromodulation.

Purpose of the Study:

  • To systematically review electroencephalography (EEG)-derived biomarkers for invasive neuromodulation efficacy in DRE.
  • To explore the influence of preprocessing and methodological variations on biomarker performance.

Main Methods:

  • Systematic review of PubMed (2004-2025) for human studies on EEG biomarkers post-invasive neuromodulation for DRE.
  • Inclusion criteria: ≥4 participants, single neuromodulation, EEG measures linked to seizure outcomes.
  • Qualitative synthesis and exploratory logistic regression on preprocessing, methods, and biomarker types.

Main Results:

  • 30 studies included (DBS, RNS, VNS); biomarkers included node-level, edge-level, and graph-theory metrics.
  • Reductions in interictal epileptiform discharge (IED) rate correlate with seizure reduction.
  • Short-term aperiodic parameter changes and reduced synchronization predict improved seizure outcomes.
  • Early EEG measures predicted longer-term outcomes in four studies.
  • Exploratory analysis indicated trends favoring larger sample sizes, higher sampling rates, normalization, and edge-level metrics; artifact removal showed nonsignificant trends toward reduced association.

Conclusions:

  • EEG biomarkers, especially IEDs, aperiodic, and synchronization measures, show potential for predicting neuromodulation efficacy in DRE.
  • Methodological and preprocessing variations may impact biomarker predictive power.
  • Comprehensive reporting of signal processing and biomarker performance is recommended for robust inference.