Automated seizure onset zone locator from resting-state functional MRI in drug-resistant epilepsy

Ayan Banerjee; Payal Kamboj; Sarah N Wyckoff; Bethany L Sussman; Sandeep K S Gupta; Varina L Boerwinkle

doi:10.3389/fnimg.2022.1007668

Automated seizure onset zone locator from resting-state functional MRI in drug-resistant epilepsy

Front Neuroimaging. 2023 Jan 4:1:1007668. doi: 10.3389/fnimg.2022.1007668. eCollection 2022.

Authors

Ayan Banerjee¹, Payal Kamboj¹, Sarah N Wyckoff², Bethany L Sussman², Sandeep K S Gupta¹, Varina L Boerwinkle³

Affiliations

¹ School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, United States.
² Division of Neuroscience, Barrow Neurological Institute at Phoenix Children's Hospital, Phoenix, AZ, United States.
³ Division of Child Neurology, University of North Carolina Department of Neurology, Chapel Hill, NC, United States.

Abstract

Objective: Accurate localization of a seizure onset zone (SOZ) from independent components (IC) of resting-state functional magnetic resonance imaging (rs-fMRI) improves surgical outcomes in children with drug-resistant epilepsy (DRE). Automated IC sorting has limited success in identifying SOZ localizing ICs in adult normal rs-fMRI or uncategorized epilepsy. Children face unique challenges due to the developing brain and its associated surgical risks. This study proposes a novel SOZ localization algorithm (EPIK) for children with DRE.

Methods: EPIK is developed in a phased approach, where fMRI noise-related biomarkers are used through high-fidelity image processing techniques to eliminate noise ICs. Then, the SOZ markers are used through a maximum likelihood-based classifier to determine SOZ localizing ICs. The performance of EPIK was evaluated on a unique pediatric DRE dataset (n = 52). A total of 24 children underwent surgical resection or ablation of an rs-fMRI identified SOZ, concurrently evaluated with an EEG and anatomical MRI. Two state-of-art techniques were used for comparison: (a) least squares support-vector machine and (b) convolutional neural networks. The performance was benchmarked against expert IC sorting and Engel outcomes for surgical SOZ resection or ablation. The analysis was stratified across age and sex.

Results: EPIK outperformed state-of-art techniques for SOZ localizing IC identification with a mean accuracy of 84.7% (4% higher), a precision of 74.1% (22% higher), a specificity of 81.9% (3.2% higher), and a sensitivity of 88.6% (16.5% higher). EPIK showed consistent performance across age and sex with the best performance in those < 5 years of age. It helped achieve a ~5-fold reduction in the number of ICs to be potentially analyzed during pre-surgical screening.

Significance: Automated SOZ localization from rs-fMRI, validated against surgical outcomes, indicates the potential for clinical feasibility. It eliminates the need for expert sorting, outperforms prior automated methods, and is consistent across age and sex.

Keywords: drug resistant epilepsy; expert knowledge driven classification; resting state fMRI; resting state network; seizure onset zone.