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Complementary structural and functional abnormalities to localise epileptogenic tissue

Lookup NU author(s): Jonathan Horsley, Dr Rhys ThomasORCiD, Dr Yujiang WangORCiD, Dr Peter TaylorORCiD



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2023 The Authors. Background: When investigating suitability for epilepsy surgery, people with drug-refractory focal epilepsy may have intracranial EEG (iEEG) electrodes implanted to localise seizure onset. Diffusion-weighted magnetic resonance imaging (dMRI) may be acquired to identify key white matter tracts for surgical avoidance. Here, we investigate whether structural connectivity abnormalities, inferred from dMRI, may be used in conjunction with functional iEEG abnormalities to aid localisation of the epileptogenic zone (EZ), improving surgical outcomes in epilepsy. Methods: We retrospectively investigated data from 43 patients (42% female) with epilepsy who had surgery following iEEG. Twenty-five patients (58%) were free from disabling seizures (ILAE 1 or 2) at one year. Interictal iEEG functional, and dMRI structural connectivity abnormalities were quantified by comparison to a normative map and healthy controls. We explored whether the resection of maximal abnormalities related to improved surgical outcomes, in both modalities individually and concurrently. Additionally, we suggest how connectivity abnormalities may inform the placement of iEEG electrodes pre-surgically using a patient case study. Findings: Seizure freedom was 15 times more likely in patients with resection of maximal connectivity and iEEG abnormalities (p = 0.008). Both modalities separately distinguished patient surgical outcome groups and when used simultaneously, a decision tree correctly separated 36 of 43 (84%) patients. Interpretation: Our results suggest that both connectivity and iEEG abnormalities may localise epileptogenic tissue, and that these two modalities may provide complementary information in pre-surgical evaluations. Funding: This research was funded by UKRI, CDT in Cloud Computing for Big Data, NIH, MRC, Wellcome Trust and Epilepsy Research UK.

Publication metadata

Author(s): Horsley JJ, Thomas RH, Chowdhury FA, Diehl B, McEvoy AW, Miserocchi A, de Tisi J, Vos SB, Walker MC, Winston GP, Duncan JS, Wang Y, Taylor PN

Publication type: Article

Publication status: Published

Journal: eBioMedicine

Year: 2023

Volume: 97

Print publication date: 01/11/2023

Online publication date: 27/10/2023

Acceptance date: 11/10/2023

Date deposited: 08/01/2024

ISSN (electronic): 2352-3964

Publisher: Elsevier BV


DOI: 10.1016/j.ebiom.2023.104848


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Funder referenceFunder name
208940/Z/17/ZWellcome Trust
Epilepsy Society
Epilepsy Research UK
National Institute for Health Research University College London Hospitals Biomedical Research Centre
NIH National Institute of Neurological Disorders and Stroke
UKRI Future Leaders Fellowship