Multiscale Functional Clustering Reveals Frequency Dependent Brain Organization in Type II Focal Cortical Dysplasia With Sleep Hypermotor Epilepsy

• Published in: IEEE transactions on biomedical engineering Vol. 66; no. 10; pp. 2831 - 2839
• Main Authors: Coelli, Stefania, Maggioni, Eleonora, Rubino, Annalisa, Campana, Chiara, Nobili, Lino, Bianchi, Anna M.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brain; Brain architecture; Clustering; Convulsions & seizures; Cortex; Discrete Wavelet Transform; Discrete wavelet transforms; Dysplasia; EEG; Epilepsy; Finite impulse response filters; focal cortical dysplasia (FCD); Frequencies; Frequency dependence; functional clustering; Microsoft Windows; Power harmonic filters; SEEG; Seizures; Sleep; Time-frequency analysis; Wavelet analysis; Wavelet transforms
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2019.2896893

Objective: A multiscale functional clustering approach is proposed to investigate the organization of the epileptic networks during different sleep stages and in relation with the occurrence of seizures. Method: Stereo-electroencephalographic signals from seven pharmaco-resistant epileptic patients (focal cortical dysplasia type II) were analyzed. The discrete wavelet transform provided a multiscale framework on which a data-driven functional clustering procedure was applied, based on multivariate measures of integration and mutual information. The most interacting functional clusters (FCs) within the sampled brain areas were extracted. Results: FCs characterized by strongly integrated activity were observed mostly in the beta and alpha frequency bands, immediately before seizure onset and in deep sleep stages. These FCs generally included the electrodes from the epileptogenic zone. Furthermore, repeatable patterns were found across ictal events in the same patient. Conclusion: In line with previous studies, our findings provide evidence of the important role of beta and alpha activity in seizures generation and support the relation between epileptic activity and sleep stages. Significance: Despite the small number of subjects included in the study, the present results suggest that the proposed multiscale functional clustering approach is a useful tool for the identification of the frequency-dependent mechanisms underlying seizure generation.