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Multicenter Study
. 2022 Aug;63(8):2081-2095.
doi: 10.1111/epi.17316. Epub 2022 Jun 25.

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

Seymour M Lopez  1 Leon M Aksman  1   2 Neil P Oxtoby  3 Sjoerd B Vos  1   4 Jun Rao  5 Erik Kaestner  5 Saud Alhusaini  6   7 Marina Alvim  8 Benjamin Bender  9 Andrea Bernasconi  10 Neda Bernasconi  10 Boris Bernhardt  11 Leonardo Bonilha  12 Lorenzo Caciagli  11   13 Benoit Caldairou  10 Maria Eugenia Caligiuri  14 Angels Calvet  15 Fernando Cendes  8 Luis Concha  16 Estefania Conde-Blanco  17   18 Esmaeil Davoodi-Bojd  19 Christophe de Bézenac  20 Norman Delanty  7   21 Patricia M Desmond  22 Orrin Devinsky  23 Martin Domin  24 John S Duncan  12   25 Niels K Focke  26 Sonya Foley  27 Francesco Fortunato  28 Marian Galovic  13   29 Antonio Gambardella  14   28 Ezequiel Gleichgerrcht  30 Renzo Guerrini  31 Khalid Hamandi  27   32 Victoria Ives-Deliperi  33 Graeme D Jackson  34   35   36 Neda Jahanshad  37 Simon S Keller  38 Peter Kochunov  39 Raviteja Kotikalapudi  9   40   41 Barbara A K Kreilkamp  38   42 Angelo Labate  14   28 Sara Larivière  11 Matteo Lenge  43   44 Elaine Lui  22 Charles Malpas  45   46 Pascal Martin  39 Mario Mascalchi  47 Sarah E Medland  48 Stefano Meletti  49   50 Marcia E Morita-Sherman  51   52 Thomas W Owen  53 Mark Richardson  54 Antonella Riva  55   56 Theodor Rüber  57 Ben Sinclair  58   59 Hamid Soltanian-Zadeh  19   60 Dan J Stein  61 Pasquale Striano  55   56 Peter N Taylor  13   53 Sophia I Thomopoulos  37 Paul M Thompson  37 Manuela Tondelli  49   62 Anna Elisabetta Vaudano  49   50 Lucy Vivash  58   59 Yujiang Wang  13   53 Bernd Weber  63 Christopher D Whelan  7 Roland Wiest  64 Gavin P Winston  13   25   65 Clarissa Lin Yasuda  8 Carrie R McDonald  5 Daniel C Alexander  3 Sanjay M Sisodiya  13   25 Andre Altmann  1 ENIGMA-Epilepsy Working Group
Collaborators, Affiliations
Multicenter Study

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

Seymour M Lopez et al. Epilepsia. 2022 Aug.

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.

Keywords: MTLE; disease progression; duration of illness; event-based model; patient staging.

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Conflict of interest statement

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.

Figures

FIGURE 1
FIGURE 1
Event‐based model (EBM) workflow. A set of k biomarkers and case–control status are provided for each subject. Then, mixture modeling is used to estimate distributions of the biomarkers in cases and controls, respectively. The maximum likelihood sequence (i.e., optimal ordering) of the k biomarkers is estimated using Markov chain Monte Carlo (MCMC) with 500 000 iterations. The MCMC sequence is initialized using 10 random starting solutions and a greedy ascent (GA) run for 10 000 iterations. Finally, in a third step, we used 100 bootstrap samples to determine the uncertainty and variability of the sequence. Init, initialization.
FIGURE 2
FIGURE 2
Regional differences in sclerosis of hippocampus or mesial temporal lobe (MTLE‐HS) compared to controls. Effect sizes between MTLE‐HS cases and controls measured as robust Cohen d for surface area, cortical thickness, and volume are depicted ipsilateral or contralateral to the seizure focus (top three rows). The bottom two rows depict effect sizes for asymmetry features.
FIGURE 3
FIGURE 3
Sequential accumulation of pathology in sclerosis of hippocampus or mesial temporal lobe. Data‐driven sequence of atrophy or increased asymmetry of brain regions is shown. Color intensity in the positional variance diagram (PVD) the proportion of certainty (.0 in white to 1.0 in dark blue) in which biomarkers (y‐axis) appear in a particular position (x‐axis) in the event order obtained through bootstrapping. BASI, brain asymmetry index; C, contralateral; CT, cortical thickness; I, ipsilateral; V, volume.
FIGURE 4
FIGURE 4
Event‐based model (EBM) stage distribution. Histogram shows stages (x‐axis) assigned to controls and people with mesial temporal lobe epilepsy and hippocampal sclerosis (MTLE‐HS) and the corresponding count (y‐axis). Stage 0 is assigned to subjects with no statistically detectable abnormal brain region based on the T1‐weighted magnetic resonance imaging scans. EBM places subjects with abnormal features progressively, such that subjects in Stage 7 exhibit abnormality in all seven regional measures.
FIGURE 5
FIGURE 5
Distribution of duration of illness per event‐based model (EBM) stage. Violin plots showing distribution of duration of illness (in years) of corresponding EBM Stages 0–7 of sclerosis of hippocampus or mesial temporal lobe (MTLE‐HS) patients. MTLE‐HS cases assigned to EBM Stage 0 showed a shorter duration of illness compared to cases assigned to the remaining EBM stages.

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