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[Preprint]. 2025 Mar 18:2025.03.17.643677.
doi: 10.1101/2025.03.17.643677.

Dissecting heterogeneity in cortical thickness abnormalities in major depressive disorder: a large-scale ENIGMA MDD normative modelling study

J M M Bayer  1   2   3   4 L S van Velzen  2   3 E Pozzi  2   3 C Davey  5 L K M Han  6 S E E C Bauduin  7 J Bauer  8 F Benedetti  9 K Berger  10 L M Bonnekoh  11   12 K Brosch  13 R Bülow  14 B Couvy-Duchesne  15   16   17   18 K R Cullen  19 U Dannlowski  11 D Dima  20   21 K Dohm  11 J W Evans  22 C H Y Fu  23 P Fuentes-Claramonte  24 B R Godlewska  25   26 J Goltermann  11 A Gonul  27 R Goya-Maldonado  28 H J Grabe  29 N A Groenewold  30 D Grotegerd  11 O Gruber  31 T Hahn  11 G B Hall  32 J Hamilton  33 B J Harrison  5 S N Hatton  34 M Hermesdorf  10 I B Hickie  34 T C Ho  35 N Jahanshad  36 A Jansen  13 A J Jamieson  5 T Kamishikiryo  37 T Kircher  13 B Klimes-Dougan  19 B Krämer  31 A Kraus  11 A Krug  38 E J Leehr  11 R Leenings  11 M Li  39 A McIntosh  40 S E Medland  41 S Meinert  11   42 E Melloni  9 B Mwangi  43 I Nenadić  13 G Okada  44 M Oudega  6   45   46   47 M J Portella  48 E Rodríguez  24 L Romaniuk  40 P G Rosa  49 M D Sacchet  50 R Salvador  24 P G Sämann  51 H Shinzato  52 K Sim  53   54   55 E Simulionyte  56 J C Soares  43 D J Stein  57 F Stein  13 A Stolicyn  40 B Straube  13   58 L T Strike  59 L Teutenberg  13 F Thomas-Odenthal  13 S I Thomopoulos  60 P Usemann  13 N J A van der Wee  61 H Völzke  60 M Wagenmakers  62   45 M Walter  63   39   64   65 H C Whalley  40 S Whittle  2   3 N R Winter  11 K Wittfeld  66 M Wu  43 T T Yang  67 C A Zarate  68 G B Zunta-Soares  43 P M Thompson  69 D J Veltman  70 A F Marquand  1   4 L Schmaal  2   3
Affiliations

Dissecting heterogeneity in cortical thickness abnormalities in major depressive disorder: a large-scale ENIGMA MDD normative modelling study

J M M Bayer et al. bioRxiv. .

Abstract

Importance: Major depressive disorder (MDD) is highly heterogeneous, with marked individual differences in clinical presentation and neurobiology, which may obscure identification of structural brain abnormalities in MDD. To explore this, we used normative modeling to index regional patterns of variability in cortical thickness (CT) across individual patients.

Objective: To use normative modeling in a large dataset from the ENIGMA MDD consortium to obtain individualised CT deviations from the norm (relative to age, sex and site) and examine the relationship between these deviations and clinical characteristics.

Design setting and participants: A normative model adjusting for age, sex and site effects was trained on 35 CT measures from FreeSurfer parcellation of 3,181 healthy controls (HC) from 34 sites (40 scanners). Individualised z-score deviations from this norm for each CT measure were calculated for a test set of 2,119 HC and 3,645 individuals with MDD. For each individual, each CT z-score was classified as being within the normal range (95% of individuals) or within the extreme range (2.5% of individuals with the thinnest or thickest cortices).

Main outcome measures: Z-score deviations of CT measures of MDD individuals as estimated from a normative model based on HC.

Results: Z-score distributions of CT measures were largely overlapping between MDD and HC (minimum 92%, range 92-98%), with overall thinner cortices in MDD. 34.5% of MDD individuals, and 30% of HC individuals, showed an extreme deviation in at least one region, and these deviations were widely distributed across the brain. There was high heterogeneity in the spatial location of CT deviations across individuals with MDD: a maximum of 12% of individuals with MDD showed an extreme deviation in the same location. Extreme negative CT deviations were associated with having an earlier onset of depression and more severe depressive symptoms in the MDD group, and with higher BMI across MDD and HC groups. Extreme positive deviations were associated with being remitted, of not taking antidepressants and less severe symptoms.

Conclusions and relevance: Our study illustrates a large heterogeneity in the spatial location of CT abnormalities across patients with MDD and confirms a substantial overlap of CT measures with HC. We also demonstrate that individualised extreme deviations can identify protective factors and individuals with a more severe clinical picture.

Keywords: ENIGMA; Normative modelling; cortical thickness; depression; individualised predictions; neuroimaging.

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Conflict of interest statement

K.B has received research funding as principal or coordinating investigator from the German Ministry of Education and Research (BMBF). HJG has received travel grants and speakers honoraria from Fresenius Medical Care, Neuraxpharm and Janssen Cilag as well as research funding from Fresenius Medical Care. IBH is the Co-Director, Health and Policy at the Brain and Mind Centre (BMC) University of Sydney. The BMC operates an early-intervention youth services at Camperdown under contract to headspace. He is the Chief Scientific Advisor to, and a 3.2% equity shareholder in, InnoWell Pty Ltd which aims to transform mental health services through the use of innovative technologies. J.C.S states that within the last twelve (12) months of the date set forth below, neither h nor, to the best of my knowledge, any member of his family has any interest in or has taken any action which would violate the Conflict of Interest Policy, except such interest or action as he has fully disclosed below: ALKERMES, ALLERGAN, ASOFARMA, ATAI, BOEHRINGER Ingelheim, COMPASS, JOHNSON & JOHNSO, LIVANOVA, PFIZER, PULVINAR NEURO LLC, RELMADA, SANOFI, SUNOVIAN. G.B.S-Z is a full-time U.S government employee. He is listed as a coinventor on a patent for the use of ketamine in major depression and suicidal ideation. Dr. Zarate is listed as a coinventor on a patent for the use of (2R,6R)-hydroxynorketamine, (S)-dehydronorketamine and other stereoisomeric dehydro and hydroxylated metabolites of (R,S)-ketamine metabolites in the treatment of depression and neuropathic pain. G.B.S-Z is listed as co-inventor on a patent application for the use of (2R,6R)-hydroxynorketamine and (2S,6S)-hydroxynorketamine in the treatment of depression, anxiety, anhedonia, suicidal ideation and post-traumatic stress disorders. G.B.S-Z has assigned his patent rights to the U.S. government but will share a percentage of any royalties that may be received by the government. The views expressed are his own and do not necessarily represent the views of the National Institutes of Health, the Department of Health and Human Services, or the United States Government.

Figures

Figure 1.
Figure 1.
Distribution of age and sex for the three partitions of the data used in this study. A) Training set, healthy controls. B) Test set, healthy controls. M= male, F=female C) Test set, MDD. D) Illustrated Pre-Processing pipeline. The sample consisted of 34 regional features and one whole brain measure from 5300 healthy controls and 3645 individuals with MDD from the ENIGMA MDD consortium.
Figure 2.
Figure 2.
A, B, C: Distributional overlap between individuals with MDD and HCs (HC) for the A. fusiform gyrus, B: the pars opercularis of the inferior frontal gyrus and C: average CT, showing the lowest distributional overlap between MDD and HC groups. D, E: Cohen’s d effect sizes of differences in z-scores between MDD and HCs for each significant cortical region (FDR corrected, only significant regions are displayed). Red: Individuals with MDD have lower z-scores (thinner cortices) on average than HCs. An overview of Cohen’s d effect sizes for z-score differences in different cortical regions is provided in Table S1.7 in the supplementary material. D) medial view. E) lateral view. F: Spider plot showing the average z-scores for individuals with MDD and HCs for all cortical regions. All regions that show significant differences are plotted in bold.
Figure 3.
Figure 3.
A. B: Heterogeneity in cortical thickness deviations in depression. Percentage of individuals with MDD with an infra-normal z-scores (extreme negative deviations; z < −1.96) and supra-normal z-scores (extreme positive deviations; z >1.96) per cortical region. (A) infra-normal z-scores; (B) supra-normal z-scores. C: Distribution of positive load score (number of extreme negative deviation; z > 1.96) (C, top) and negative load score (number of extreme positive deviations; z < −1.96) (C, bottom) in individuals with MDD and healthy controls.

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