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

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77Institutions associées
15Pays d’affiliation

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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. Key points Question Can z-scores derived from normative modelling shed light on the heterogeneous group-level findings of cortical thickness abnormalities in major depression and what characterises individuals at the extreme ends of cortical thickness abnormalities? Finding We confirmed a large overlap in z-score distributions between depressed individuals and healthy controls and a heterogeneous spatial distribution of extreme z-deviations across brain regions across individual patients. Lower z-scores for cortical thickness were related to more severe clinical characteristics. Meaning Our findings confirm the heterogeneity in individual variation in the location and extent of CT abnormalities across patients with MDD and stress the importance of individualised predictions when examining cortical thickness abnormalities.

Institutions

Radboud University NijmegenThe University of MelbourneRadboud University Medical CenterOrygen Youth HealthMental Health AustraliaOrygenAmsterdam NeuroscienceAmsterdam University Medical CentersVrije Universiteit AmsterdamLeiden University Medical CenterUniversity DermatologyUniversity RadiologyVita-Salute San Raffaele UniversityIstituti di Ricovero e Cura a Carattere ScientificoUniversity of MünsterKlinik für PsychosomatikPhilipps University of MarburgUniversitätsmedizin GreifswaldCentre National de la Recherche ScientifiqueAllen Institute for Brain ScienceInstitut national de recherche en sciences et technologies du numériqueInsermThe University of QueenslandSorbonne UniversitéAssistance Publique – Hôpitaux de ParisPitié-Salpêtrière HospitalInstitut du CerveauUniversity of MinnesotaUniversity of Minnesota SystemWellcome Centre for Human NeuroimagingCity, University of LondonKing's College LondonHologic (Germany)University of East LondonFidmag Sisters HospitallersHospital Mare de Déu de la MercèOxford Health NHS Foundation TrustUniversity of OxfordEge UniversityUniversitätsmedizin GöttingenUniversity of GöttingenStanford UniversityUniversity of Cape TownHeidelberg UniversityMcMaster UniversityUniversity of BergenThe University of SydneyUniversity of California, Los AngelesCognitive Neuroimaging LabHiroshima UniversityUniversity Hospital BonnJena University HospitalUniversity of EdinburghQIMR Berghofer Medical Research InstituteThe University of Texas Health Science CenterIstituto di Ricovero e Cura a Carattere Scientifico San RaffaeleThe University of Texas Health Science Center at HoustonGGZ inGeestGGD AmsterdamUniversitat Autònoma de BarcelonaCentro de Investigación Biomédica en Red de Salud MentalHospital de Sant PauHospital das Clínicas da Faculdade de Medicina da Universidade de São PauloHarvard UniversityMassachusetts General HospitalMax Planck Institute of PsychiatryUniversity of the RyukyusNational University of SingaporeNanyang Technological UniversityInstitute of Mental HealthNeuroscience InstituteCenter for Behavioral Brain SciencesUniversity of Southern CaliforniaGerman Centre for Cardiovascular ResearchUniversity of California, San FranciscoNational Institute of Mental HealthCalifornia Southern University

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Functional Brain Connectivity StudiesTreatment of Major DepressionAdvanced Neuroimaging Techniques and Applications

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