Neurophysiologic Biomarkers of Invasive Neuromodulation Therapy for Epilepsy

This review included 30 studies (2005-2024), covering DBS (n = 8), RNS (n = 7), and VNS (n = 15). Ten of 30 studies were of focal DRE; three of 30 studies were generalized DRE, and half of the included studies did not specify whether participants had focal or generalized epilepsy. Biomarkers spanned node-level (eg, interictal epileptiform discharges, spectral power, n = 14), edge-level (eg, phase-amplitude coupling, coherence, n = 13), and graph-theory metrics (n = 3). Key findings were as follows: 1) interictal epileptiform discharge (IED) rate reductions commonly correlate with seizure frequency reduction; 2) short-term changes in the aperiodic parameters appear to be predictive of longer-term clinical outcome; and 3) reduced synchronization is repeatedly associated with improved seizure outcomes across RNS, VNS, and some DBS contexts. Four studies reported early measures (intraoperative or ≤six months post implant) that predicted longer-term seizure outcomes. Exploratory logistic regression suggested nonsignificant trends favoring larger sample sizes, higher sampling rates, data normalization, and edge-level metrics; use of artifact-removal algorithms tended to reduce the likelihood of a reported significant association. All regression estimates were imprecise (nonsignificant, wide 95% CIs) CONCLUSION: In conclusion, EEG-derived biomarkers, particularly IEDs, aperiodic measures, and synchronization measures, show promise for predicting neuromodulation efficacy in DRE. Nonsignificant trends revealed through logistic regression analysis suggest that methodologic and preprocessing variations may influence biomarker predictive efficacy. We would recommend more comprehensive reporting of the signal processing protocols and biomarker performance to enable more robust causal inference.