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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.

Structural covariance analysis is a widely used structural MRI analysis method which characterises the co-relations of morphology between brain regions over a group of subjects. To our knowledge, little has been investigated in terms of the comparability of results between different data sets of healthy human subjects, as well as the reliability of results over the same subjects in different rescan sessions, image resolutions, or FreeSurfer versions. In terms of comparability, our results show substantial differences in the structural covariance matrix between data sets of age- and sex-matched healthy human adults. These differences persist after univariate site correction, they are exacerbated by low sample sizes, and they are most pronounced when using average cortical thickness as a morphological measure. Down-stream graph theoretic analyses further show statistically significant differences. In terms of reliability, substantial differences were also found when comparing repeated scan sessions of the same subjects, image resolutions, and even FreeSurfer versions of the same image. We could further estimate the relative measurement error and showed that it is largest when using cortical thickness as a morphological measure. Using simulated data, we argue that cortical thickness is least reliable because of larger relative measurement errors. Practically, we make the following recommendations (1) combining subjects across sites into one group should be avoided, particularly if sites differ in image resolutions, subject demographics, or preprocessing steps; (2) surface area and volume should be preferred as morphological measures over cortical thickness; (3) a large number of subjects (n≫30 for the Desikan-Killiany parcellation) should be used to estimate structural covariance; (4) measurement error should be assessed where repeated measurements are available; (5) if combining sites is critical, univariate (per ROI) site-correction is insufficient, but error covariance (between ROIs) should be explicitly measured and modelled.

Previous studies investigating associations between white matter alterations and duration of temporal lobe epilepsy (TLE) have shown differing results, and were typically limited to univariate analyses of tracts in isolation. In this study, we apply a multivariate measure (the Mahalanobis distance), which captures the distinct ways white matter may differ in individual patients, and relate this to epilepsy duration. Diffusion MRI, from a cohort of 94 subjects (28 healthy controls, 33 left‐TLE and 33 right‐TLE), was used to assess the association between tract fractional anisotropy (FA) and epilepsy duration. Using ten white matter tracts, we analysed associations using the traditional univariate analysis (z‐scores) and a complementary multivariate approach (Mahalanobis distance), incorporating multiple white matter tracts into a single unified analysis. For patients with right‐TLE, FA was not significantly associated with epilepsy duration for any tract studied in isolation. For patients with left‐TLE, the FA of two limbic tracts (ipsilateral fornix, contralateral cingulum gyrus) were significantly negatively associated with epilepsy duration (Bonferonni corrected p, Previous studies investigating associations between white matter alterations and duration of temporal lobe epilepsy (TLE) have shown differing results, and were typically limited to univariate analyses of tracts in isolation. In this study, we apply a multivariate measure to capture the distinct ways white matter may differ in individual patients, and relate this to epilepsy duration.