Dataset Information

White matter disturbances in major depressive disorder: a coordinated analysis across 20 international cohorts in the ENIGMA MDD working group.

ABSTRACT: Alterations in white matter (WM) microstructure have been implicated in the pathophysiology of major depressive disorder (MDD). However, previous findings have been inconsistent, partially due to low statistical power and the heterogeneity of depression. In the largest multi-site study to date, we examined WM anisotropy and diffusivity in 1305 MDD patients and 1602 healthy controls (age range 12-88 years) from 20 samples worldwide, which included both adults and adolescents, within the MDD Working Group of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium. Processing of diffusion tensor imaging (DTI) data and statistical analyses were harmonized across sites and effects were meta-analyzed across studies. We observed subtle, but widespread, lower fractional anisotropy (FA) in adult MDD patients compared with controls in 16 out of 25 WM tracts of interest (Cohen's d between 0.12 and 0.26). The largest differences were observed in the corpus callosum and corona radiata. Widespread higher radial diffusivity (RD) was also observed (all Cohen's d between 0.12 and 0.18). Findings appeared to be driven by patients with recurrent MDD and an adult age of onset of depression. White matter microstructural differences in a smaller sample of adolescent MDD patients and controls did not survive correction for multiple testing. In this coordinated and harmonized multisite DTI study, we showed subtle, but widespread differences in WM microstructure in adult MDD, which may suggest structural disconnectivity in MDD.

SUBMITTER: van Velzen LS

PROVIDER: S-EPMC7055351 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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White matter disturbances in major depressive disorder: a coordinated analysis across 20 international cohorts in the ENIGMA MDD working group.

van Velzen Laura S LS Kelly Sinead S Isaev Dmitry D Aleman Andre A Aftanas Lyubomir I LI Bauer Jochen J Baune Bernhard T BT Brak Ivan V IV Carballedo Angela A Connolly Colm G CG Couvy-Duchesne Baptiste B Cullen Kathryn R KR Danilenko Konstantin V KV Dannlowski Udo U Enneking Verena V Filimonova Elena E Förster Katharina K Frodl Thomas T Gotlib Ian H IH Groenewold Nynke A NA Grotegerd Dominik D Harris Mathew A MA Hatton Sean N SN Hawkins Emma L EL Hickie Ian B IB Ho Tiffany C TC Jansen Andreas A Kircher Tilo T Klimes-Dougan Bonnie B Kochunov Peter P Krug Axel A Lagopoulos Jim J Lee Renick R Lett Tristram A TA Li Meng M MacMaster Frank P FP Martin Nicholas G NG McIntosh Andrew M AM McLellan Quinn Q Meinert Susanne S Nenadić Igor I Osipov Evgeny E Penninx Brenda W J H BWJH Portella Maria J MJ Repple Jonathan J Roos Annerine A Sacchet Matthew D MD Sämann Philipp G PG Schnell Knut K Shen Xueyi X Sim Kang K Stein Dan J DJ van Tol Marie-Jose MJ Tomyshev Alexander S AS Tozzi Leonardo L Veer Ilya M IM Vermeiren Robert R Vives-Gilabert Yolanda Y Walter Henrik H Walter Martin M van der Wee Nic J A NJA van der Werff Steven J A SJA Schreiner Melinda Westlund MW Whalley Heather C HC Wright Margaret J MJ Yang Tony T TT Zhu Alyssa A Veltman Dick J DJ Thompson Paul M PM Jahanshad Neda N Schmaal Lianne L

Molecular psychiatry 20190830 7

Alterations in white matter (WM) microstructure have been implicated in the pathophysiology of major depressive disorder (MDD). However, previous findings have been inconsistent, partially due to low statistical power and the heterogeneity of depression. In the largest multi-site study to date, we examined WM anisotropy and diffusivity in 1305 MDD patients and 1602 healthy controls (age range 12-88 years) from 20 samples worldwide, which included both adults and adolescents, within the MDD Worki ...[more]

PMID: 31471575

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Project description:Accurately predicting individual antidepressant treatment response could expedite the lengthy trial-and-error process of finding an effective treatment for major depressive disorder (MDD). We tested and compared machine learning-based methods that predict individual-level pharmacotherapeutic treatment response using cortical morphometry from multisite longitudinal cohorts. We conducted an international analysis of pooled data from six sites of the ENIGMA-MDD consortium (n = 262 MDD patients; age = 36.5 ± 15.3 years; 154 (59%) female; mean response rate = 57%). Treatment response was defined as a ≥ 50% reduction in symptom severity score after 4-12 weeks post-initiation of antidepressant treatment. Structural MRI was acquired before, or < 14 days after, treatment initiation. The cortex was parcellated using FreeSurfer, from which cortical thickness and surface area were measured. We tested several machine learning pipeline configurations, which varied in (i) the way we presented the cortical data (i.e., average values per region of interest, as a vector containing voxel-wise cortical thickness and surface area measures, and as cortical thickness and surface area projections), (ii) whether we included clinical data, and the (iii) machine learning model (i.e., gradient boosting, support vector machine, and neural network classifiers) and (iv) cross-validation methods (i.e., k-fold and leave-one-site-out) we used. First, we tested if the overall predictive performance of the pipelines was better than chance, with a corrected 10-fold cross-validation permutation test. Second, we compared if some machine learning pipeline configurations outperformed others. In an exploratory analysis, we repeated our first analysis in three subpopulations, namely patients (i) from a single site, (ii) with comparable response rates, and (iii) showing the least (first quartile) and the most (fourth quartile) treatment response, which we call the extreme (non-)responders subpopulation. Finally, we explored the effect of including subcortical volumetric data on model performance. Overall, performance predicting antidepressant treatment response was not significantly better than chance (balanced accuracy = 50.5%; p = 0.66) and did not vary with alternative pipeline configurations. Exploratory analyses revealed that performance across models was only significantly better than chance in the extreme (non-)responders subpopulation (balanced accuracy = 63.9%, p = 0.001). Including subcortical data did not alter the observed model performance. Cortical structural MRI alone could not reliably predict individual pharmacotherapeutic treatment response in MDD. None of the used machine learning pipeline configurations outperformed the others. In exploratory analyses, we found that predicting response in the extreme (non-)responders subpopulation was feasible on both cortical data alone and combined with subcortical data, which suggests that specific MDD subpopulations may exhibit response-related patterns in structural data. Future work may use multimodal data to predict treatment response in MDD.

Dataset Information

White matter disturbances in major depressive disorder: a coordinated analysis across 20 international cohorts in the ENIGMA MDD working group.

Publications

White matter disturbances in major depressive disorder: a coordinated analysis across 20 international cohorts in the ENIGMA MDD working group.

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