513 Presurgical Identification of Seizure Onset and Propagative Zones Without Ictal Recordings: A Combined Diffusion MRI and SEEG Study. (April 2023)

• Record Type: Journal Article
• Authors: Johnson, Graham Walter; Doss, Derek; Shless, Jared; Paulo, Danika Lea; Morgan, Victoria; Bick, Sarah K.B.; Englot, Dario J.
• Is Part Of: Neurosurgery. Volume 69(2023)Supplement 1
• Page Start: 114
• Page End: 114
• Publication Date: 2023-04
• Subjects: Nervous system -- Surgery -- Periodicals; 617.48005
• DOI: 10.1227/neu.0000000000002375_513
• Languages: English
• ISSNs: 0148-396X

Abstract

Abstract : INTRODUCTION: Increasing evidence has suggested that identification of regions involved in early seizure propagation ("Propagation Zones", PZ) is important to predict seizure freedom after epilepsy surgery (Andrews 2019). Resting-state connectivity analyses using stereo-electroencephalography (SEEG) have shown promise in efficiently characterizing seizure onset zones (SOZ) but have found difficulty in distinguishing both SOZs and PZs from non-involved brain regions. Recently, evidence has suggested that ictal structure-function coupling can be used to delineate brain regions important in seizure dynamics (Shah 2019). METHODS: We calculated the resting-state SEEG directed connectivity of 26 consented patients with focal epilepsy undergoing presurgical evaluation. Using preoperative diffusion MRI, we then implemented a custom technique to obtain structural connectivity metrics between SEEG contacts. We calculated the structural connectivity and structure-function coupling of SOZs, PZs (spread within 10 seconds), and non-involved regions over a range of Euclidean distances. Finally, we generated models using a support vector machine to classify SOZs, PZs, and non-involved regions. RESULTS: SOZs and PZs exhibit comparably high local structural connectivity compared to non-involved regions despite SOZs demonstrating significantly greater functional connectivity to both PZs and non-involved regions. However, PZs exhibit significantly higher local structure-function coupling to that of non-involved regions, with SOZs exhibiting the highest local structure-function coupling. A support vector machine to classify SOZs, PZs, and non-involved regions and was able to significantly increase model accuracy by incorporating local structure-function coupling. CONCLUSIONS: SOZs and PZs demonstrate a distinct local structure-function coupling to that of non-involved regions and each other. This distinct coupling profile can be used to accurately classify SOZs, PZs and non-involved regions.