Spatiotemporal EEG Dynamics Across Early and Late Preictal Periods in Pediatric Focal Epilepsy

Abstract

Objective: This study aimed to quantify the temporal and spatial dynamics of preictal cortical networks in pediatric refractory focal epilepsy using scale-independent EEG measurements. Method: For this purpose, the preictal (−35 to −5 min), interictal, early preictal (−35 to −20 min), and late preictal (−20 to −5 min) periods were examined in long-term scalp EEG recordings from pediatric patients in the CHB–MIT database. For the selected segments, the detrended fluctuation analysis (DFA) scale exponent, median power frequency (MPF), and aperiodic 1/f exponent (β) were calculated using SpecParam/FOOOF and IRASA. The metrics were summarized on an individual basis and analyzed using two-way repeated measures ANOVA with Šídák-corrected post-hoc tests and topographic mapping. Findings: The preictal period was characterized by increased DFA-α and MPF in fronto-temporal regions compared to the interictal period; during the interictal period, MPF foci shifted to posterior areas. Aperiodic β values showed steepening in frontal regions during the preictal period and exhibited a posterior-temporal gradient during the interictal period. Critically, during the transition from the early to late preictal period, fronto-temporal DFA-α and MPF values decreased, accompanied by a posterior-to-anterior spread of aperiodic β steepening. Conclusion: These findings indicate that cortical networks experience a collapse in long-range temporal correlations and a reorganization of the aperiodic spectral structure as seizure onset approaches. The observed loss of complexity, spectral slowing, and frontal aperiodic steepening, occurring simultaneously, suggest a heterogeneous nature of the preictal process and highlight the potential of scale-independent metrics in monitoring seizure risk.

Keywords

• Aperiodic 1/f activity
• EEG dynamics
• epilepsy
• Long-range temporal correlations
• Median power frequency
• Preictal brain states
• Seizure prediction

Supporting Institution

No funding was used in this study.

Ethical Statement

This study did not require additional Ethics Committee Approval as it involves a secondary analysis of a completely anonymized, publicly available dataset (CHB-MIT Scalp EEG Database, PhysioNet; https://physionet.org/content/chbmit/1.0.0). The original data collection was approved by the Boston Children's Hospital and Massachusetts Institute of Technology (MIT) Institutional Review Boards and conducted in accordance with the Declaration of Helsinki.

Thanks

The author gratefully acknowledges the investigators at Boston Children's Hospital and the Massachusetts Institute of Technology (MIT) for collecting the data and making the CHB-MIT Scalp EEG Database publicly available via PhysioNet. The author also thanks the open-source community for developing the Python tools (MNE, SciPy, FOOOF) used in this research.

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