Evidence map›Paper›PMID 37807084›Full record

ArticleBrain : a journal of neurology2023

Identification of different MRI atrophy progression trajectories in epilepsy by subtype and stage inference.

Fenglai Xiao, Lorenzo Caciagli, Britta Wandschneider, Daichi Sone, Alexandra L Young, Sjoerd B Vos, Gavin P Winston, Yingying Zhang, Wenyu Liu, Dongmei An and 8 more

Open access · hybridAbstract read
In one paragraph

Article in Brain : a journal of neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
9.3field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 42 citations in OpenAlex.

  1. Article
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  6. AI in epilepsy neuroimaging.Current opinion in neurology · 2026
    Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

18 authors at 9 institutions in 7 countries.

Fenglai XiaoDepartment of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK.ORCID 0000-0003-1308-6539
Lorenzo CaciagliDepartment of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK.ORCID 0000-0001-7189-9699
Britta WandschneiderDepartment of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK.
Daichi SoneDepartment of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK.
Alexandra L YoungCentre for Medical Image Computing, Departments of Computer Science, Medical Physics, and Biomedical Engineering, UCL, London, WC1E 6BT, UK.ORCID 0000-0002-7772-781X
Sjoerd B VosCentre for Medical Image Computing, Departments of Computer Science, Medical Physics, and Biomedical Engineering, UCL, London, WC1E 6BT, UK.
Gavin P WinstonDepartment of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK.ORCID 0000-0001-9395-1478
Yingying ZhangDepartment of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, China.ORCID 0000-0001-6520-6438
Wenyu LiuDepartment of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, China.
Dongmei AnDepartment of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, China.
Baris KanberCentre for Medical Image Computing, Departments of Computer Science, Medical Physics, and Biomedical Engineering, UCL, London, WC1E 6BT, UK.
Dong ZhouDepartment of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, China.
Josemir W SanderDepartment of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK.ORCID 0000-0001-6041-9661
Maria ThomDepartment of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK.
John S DuncanDepartment of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK.ORCID 0000-0002-1373-0681
Daniel C AlexanderCentre for Medical Image Computing, Departments of Computer Science, Medical Physics, and Biomedical Engineering, UCL, London, WC1E 6BT, UK.
Marian GalovicDepartment of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK.
Matthias J KoeppDepartment of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK.
Sichuan University · CNNational Hospital for Neurology and Neurosurgery · GBUniversity College London · GBJikei University School of Medicine · JPKing's College London · GBQueen's University · CAThe University of Western Australia · AUUniversity Hospital of Zurich · CHUniversity of Bern · CH

Funding

Department of HealthMedical Research Council G0802012Medical Research Council MR/M00841X/1Medical Research Council MR/T005335/1Medical Research Council MR/T027800/1Wellcome TrustWellcome Trust 079474Wellcome Trust 083148Wellcome Trust 218380Wellcome Trust 221915Wellcome Trust 221934/Z/20/Z
6 · The paper itself

Abstract

Artificial intelligence (AI)-based tools are widely employed, but their use for diagnosis and prognosis of neurological disorders is still evolving. Here we analyse a cross-sectional multicentre structural MRI dataset of 696 people with epilepsy and 118 control subjects. We use an innovative machine-learning algorithm, Subtype and Stage Inference, to develop a novel data-driven disease taxonomy, whereby epilepsy subtypes correspond to distinct patterns of spatiotemporal progression of brain atrophy.In a discovery cohort of 814 individuals, we identify two subtypes common to focal and idiopathic generalized epilepsies, characterized by progression of grey matter atrophy driven by the cortex or the basal ganglia. A third subtype, only detected in focal epilepsies, was characterized by hippocampal atrophy. We corroborate external validity via an independent cohort of 254 people and confirm that the basal ganglia subtype is associated with the most severe epilepsy.Our findings suggest fundamental processes underlying the progression of epilepsy-related brain atrophy. We deliver a novel MRI- and AI-guided epilepsy taxonomy, which could be used for individualized prognostics and targeted therapeutics.

Indexed as

BrainEpilepsyArtificial IntelligenceAtrophyCross-Sectional StudiesHumansMagnetic Resonance Imagingbrain atrophydisease progressionepilepsyMRIsubtype and stage inference

Identifiers

PMID37807084
PMCPMC10629797
OpenAlexW4387440239

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.