Rationale: Time-frequency connectivity (TFC) analysis is a framework of statistical methods that quantifies the interactions between brain signals across varying frequency intervals. Existing evidence suggests that correlation and coherence networks within this framework can effectively characterize some temporal and spatial seizure features, including the seizure onset zone. This study aims to investigate features in interictal network structures that are closely linked to the surgical outcomes of seizure interventions, specifically resection or ablation procedures.
Methods: The study used a dataset collected at the University of Pennsylvania (Bernabei, Sinha, et al. 2022) comprised of 27 participants: 14 females (age: 36.1+/-11.8) and 13 males (age: 30.5+/-8.8), who underwent ablation (n=18) or resection (n=9) procedures to treat focal seizures. Stereo-electroencephalograms (SEEG) from these patients were recorded from the frontal lobe (n=8), temporal lobe (n=7), and medial temporal lobe (n=12) prior to the surgical intervention. Based on medical follow-ups at least six months after surgery, each patient's surgical outcome was assessed using the Engel outcome scale (ablation: 1.8+/-0.9; resection: 1.9+/-1.1). An intervention was considered good (G) for an Engel-I or ILAE 1–2 scale; otherwise, it was regarded as a poor (P) outcome.
Six segments of each patient's interictal data were extracted for analysis, each lasting five seconds. Functional (FC) and effective (EC) connectivity were estimated using vector autoregressive models for each segment (Ombao & Pinto, 2022). Each type of connectivity was estimated in low broadband EEG frequencies (lbEEG, 1-45Hz) and high-frequency oscillations (HFO, 100-200Hz). Centrality and assortativity coefficients were computed to summarize the estimated networks' structural characteristics.
References: Bernabei, J., Sinha, N., Arnold, T., Conrad, E., Ong, I., Pattnaik, A., Stein, J., Shinohara, R., Lucas, T., Bassett, D., and Davis, K (2022). Normative intracranial EEG maps epileptogenic tissues in focal epilepsy. Brain.
Ombao, H., and Pinto, M. (2022). Spectral dependence. Econometrics and Statistics.
Results: Our findings revealed that EC/FC metrics are associated with the intervention outcome. Maximum Laplacian centrality metrics in EC networks were different in both groups in the lbEEG (p=.039) and HFO range (p < .001). Average closeness centrality in FC is also different in lbEEG (p < .001) and HFO intervals (p=.004). However, FC assortativity coefficients were significantly different in the lbEEG interval (p=.004), but not in the HFO range (p=.238).
Conclusions: Preliminary analysis suggests that frequency-varying EC and FC structures in SEEG signals exhibit promise for predicting the surgical outcomes of resection or ablation procedures. These findings provide a better understanding of the neuronal networks that generate seizures.
Funding: Research reported in this publication was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under Award Number R01NS116273.