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Complementary structural and functional abnormalities to localise epileptogenic tissue

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

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


Abstract

© 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

URL: https://doi.org/10.1016/j.ebiom.2023.104848

DOI: 10.1016/j.ebiom.2023.104848

Data Access Statement: Data to reproduce the main findings will be made available upon request.


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Funding

Funder referenceFunder name
208940/Z/17/ZWellcome Trust
Epilepsy Research UK
EP/L015358/1EPSRC
Epilepsy Society
G0802012
MR/M00841X/1
MR/V026569/1
MRC
National Institute for Health Research University College London Hospitals Biomedical Research Centre
MR/T04294X/1
NIH National Institute of Neurological Disorders and Stroke
U01-NS090407
UKRI Future Leaders Fellowship
WT106882

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