Event-based modeling in temporal lobe epilepsy demonstrates progressive atrophy from cross-sectional data

for the ENIGMA-Epilepsy Working Group

Research output: Contribution to journalArticlepeer-review

Abstract

Objective: Recent work has shown that people with common epilepsies have characteristic patterns of cortical thinning, and that these changes may be progressive over time. Leveraging a large multicenter cross-sectional cohort, we investigated whether regional morphometric changes occur in a sequential manner, and whether these changes in people with mesial temporal lobe epilepsy and hippocampal sclerosis (MTLE-HS) correlate with clinical features. Methods: We extracted regional measures of cortical thickness, surface area, and subcortical brain volumes from T1-weighted (T1W) magnetic resonance imaging (MRI) scans collected by the ENIGMA-Epilepsy consortium, comprising 804 people with MTLE-HS and 1625 healthy controls from 25 centers. Features with a moderate case–control effect size (Cohen d ≥.5) were used to train an event-based model (EBM), which estimates a sequence of disease-specific biomarker changes from cross-sectional data and assigns a biomarker-based fine-grained disease stage to individual patients. We tested for associations between EBM disease stage and duration of epilepsy, age at onset, and antiseizure medicine (ASM) resistance. Results: In MTLE-HS, decrease in ipsilateral hippocampal volume along with increased asymmetry in hippocampal volume was followed by reduced thickness in neocortical regions, reduction in ipsilateral thalamus volume, and finally, increase in ipsilateral lateral ventricle volume. EBM stage was correlated with duration of illness (Spearman ρ =.293, p = 7.03 × 10−16), age at onset (ρ = −.18, p = 9.82 × 10−7), and ASM resistance (area under the curve =.59, p =.043, Mann–Whitney U test). However, associations were driven by cases assigned to EBM Stage 0, which represents MTLE-HS with mild or nondetectable abnormality on T1W MRI. Significance: From cross-sectional MRI, we reconstructed a disease progression model that highlights a sequence of MRI changes that aligns with previous longitudinal studies. This model could be used to stage MTLE-HS subjects in other cohorts and help establish connections between imaging-based progression staging and clinical features.

Original languageEnglish
JournalEpilepsia
DOIs
Publication statusAccepted/In press - 2022
Externally publishedYes

Bibliographical note

Funding Information:
B.Ben. is cofounder of AIRAmed, a company that offers brain segmentation. C.D.W. is an employee of Biogen. D.J.S. has received research grants and/or consultancy honoraria from Lundbeck and Sun. K.H. has received honoraria and speaker fees from UCB, Eisai, and GW Pharma L.V. reports research funding from Biogen Australia, Life Molecular Imaging, and Eisai. N.K.F. has received honoraria from Arvelle, Bial, Eisai, Philips/EGI, and UCB. N.J. is MPI of a research grant from Biogen for work unrelated to the contents of this article. P.S. has received speaker fees and served on advisory boards for Biomarin, Zogenyx, GW and Pharmaceuticals; has received research funding from ENECTA, GW Pharmaceuticals, Kolfarma, and Eisai. P.M.T. has received a research grant from Biogen and was a paid consultant for Kairos Venture Capital for projects unrelated to this work. None of the other authors has any conflict of interest to disclose. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.

Funding Information:
The ENIGMA‐Epilepsy working group thanks and acknowledges all working group members ( http://enigma.ini.usc.edu/ongoing/enigma‐epilepsy/ ). A.A. holds an MRC eMedLab Medical Bioinformatics Career Development Fellowship; this work was partly supported by the Medical Research Council (grant number MR/L016311/1). A.B. is supported by CIHR MOP‐57840. B.Ber. acknowledges research support from the NSERC (Discovery‐1304413), CIHR (FDN‐154298, PJT‐174995), Azrieli Center for Autism Research of the Montreal Neurological Institute (ACAR‐TACC), SickKids Foundation (NI17‐039), Helmholtz International BigBrain Analytics and Learning Laboratory (Hiball), and Tier‐2 Canada Research Chairs program and salary support from the FRQS (Chercheur Boursier Junior 1). C.D.W. is supported by the Health Research Board of Ireland PhD scholarship. C.L.Y. is supported by FAPESP‐BRAINN (2013/07599‐3) and CNPQ (403726/2016‐6). C.R.M. is supported by NIH grants R01 NS065838 and R21 NS107739. D.J.S. is supported by the South African Medical Research Council. The UNICAMP research center (Brazilian Institute of Neuroscience and Neurotechnology) was funded by FAPESP (São Paulo Research Foundation; contract grant number 2013/07559‐3). F.C. was supported by the Conselho Nacional de Pesquisa, Brazil (grant number 311231/2019‐5). G.D.J. is supported by the National Health and Medical Research Council Australian Medical Research Future Fund with support from the Victorian State Government infrastructure fund. G.P.W. was supported by the MRC (G0802012, MR/M00841X/1). J.S.D. is supported by the National Institute for Health Research (NIHR) Clinical Research Time Award and BRAIN Unit Infrastructure Award (grant number UA05) funded by Welsh Government through Health and Care Research Wales. L.M.A. has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement 666992. L.Co. is supported by the Mexican Council of Science and Technology (CONACYT 181508, 232676, 251216, and 280283) and UNAM‐DGAPA (IB201712). M.A. is supported by FAPESP 15/17066‐0. M.E.M.‐S. is supported by NIH funding. M.R. is supported by a Medical Research Council program grant (MR/K013998/1), the Medical Research Council Centre for Neurodevelopmental Disorders (MR/N026063/1), the NIHR Biomedical Research Centre at South London, and Maudsley NHS Foundation Trust. N.Be. is supported by CIHR MOP‐123520 and CIHR MOP‐130516. N.J. is supported in part by NIH grants R01AG059874 and R01MH117601. N.K.F. is supported by DFG FO750/5‐1. N.P.O. is a UKRI Future Leaders Fellow (MR/S03546X/1) and acknowledges funding from the NIHR University College London Hospitals Biomedical Research Centre. O.D. is supported by FACES (Finding a Cure for Epilepsy and Seizures). P.K. is supported by NIH grants S10OD023696 and R01EB015611. P.M. is supported by the PATE program (F1315030) of the University of Tübingen. P.M.T. is supported by NIH grants R01MH116147, P41EB015922, and R01AG058854. P.N.T. is supported by a UKRI Future Leaders Fellowship (MR/T04294X/1). Work was developed within the framework of the DINOGMI Department of Excellence of MIUR 2018‐2022 (legge 232 del 2016). R.H.T. is supported by Epilepsy Research UK. The Bern research center was funded by the Swiss National Science Foundation (grant 180365). R.W. received support from the Swiss League Against Epilepsy. S.E.M. is supported in part by NHMRC APP1172917. S.I.T. is supported in part by NIH grants R01MH116147, P41EB015922, and R01AG058854. S.L. is funded by the CIHR. S.M. is supported by Italian Ministry of Health funding grant NET‐2013‐02355313. S.M.L. acknowledges support of an EPSRC Doctoral Training Partnership studentship (EP/R513143/1). S.S.K. is supported by the Medical Research Council (MR/S00355X/1 and MR/K023152/1) and Epilepsy Research UK (1085). T.J.O. is supported by an NHMRC program grant. T.W.O is supported by the Centre for Doctoral Training in Cloud Computing for Big Data (EP/L015358/1). The work was supported by the Epilepsy Society. This work was partly carried out at the NIHR University College London Hospitals Biomedical Research Centre, which receives a proportion of funding from the UK Department of Health's NIHR Biomedical Research Centres funding scheme.

Publisher Copyright:
© 2022 The Authors. Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.

Keywords

  • MTLE
  • disease progression
  • duration of illness
  • event-based model
  • patient staging

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