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Lookup NU author(s): Professor Peter TaylorORCiD, Professor Yujiang WangORCiD, Callum Simpson, Vyte Janiukstyte, Jonathan Horsley, Dr Karoline LeibergORCiD, Dr Beth LittleORCiD, Harry Clifford
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2024 The Author(s). Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy. Objective: Magnetic resonance imaging (MRI) is a crucial tool for identifying brain abnormalities in a wide range of neurological disorders. In focal epilepsy, MRI is used to identify structural cerebral abnormalities. For covert lesions, machine learning and artificial intelligence (AI) algorithms may improve lesion detection if abnormalities are not evident on visual inspection. The success of this approach depends on the volume and quality of training data. Methods: Herein, we release an open-source data set of pre-processed MRI scans from 442 individuals with drug-refractory focal epilepsy who had neurosurgical resections and detailed demographic information. We also share scans from 100 healthy controls acquired on the same scanners. The MRI scan data include the preoperative three-dimensional (3D) T1 and, where available, 3D fluid-attenuated inversion recovery (FLAIR), as well as a manually inspected complete surface reconstruction and volumetric parcellations. Demographic information includes age, sex, age a onset of epilepsy, location of surgery, histopathology of resected specimen, occurrence and frequency of focal seizures with and without impairment of awareness, focal to bilateral tonic–clonic seizures, number of anti-seizure medications (ASMs) at time of surgery, and a total of 1764 patient years of post-surgical followup. Crucially, we also include resection masks delineated from post-surgical imaging. Results: To demonstrate the veracity of our data, we successfully replicated previous studies showing long-term outcomes of seizure freedom in the range of ~50%. Our imaging data replicate findings of group-level atrophy in patients compared to controls. Resection locations in the cohort were predominantly in the temporal and frontal lobes. Significance: We envisage that our data set, shared openly with the community, will catalyze the development and application of computational methods in clinical neurology.
Author(s): Taylor PN, Wang Y, Simpson C, Janiukstyte V, Horsley J, Leiberg K, Little B, Clifford H, Adler S, Vos SB, Winston GP, McEvoy AW, Miserocchi A, de Tisi J, Duncan JS
Publication type: Article
Publication status: Published
Journal: Epilepsia
Year: 2024
Pages: Epub ahead of print
Online publication date: 05/12/2024
Acceptance date: 08/11/2024
Date deposited: 17/12/2024
ISSN (print): 0013-9580
ISSN (electronic): 1528-1167
Publisher: John Wiley and Sons Inc.
URL: https://doi.org/10.1111/epi.18192
DOI: 10.1111/epi.18192
Data Access Statement: Data are shared on the openneuro.org platform and publicly searchable without restriction. Links to data can also be found in Table S3 and at www.cnnp-lab.com/ideas-data.
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