Cortical profiles of numerous psychiatric disorders and normal development share a common pattern.
- Authors
- Cao, Zhipeng; Cupertino, Renata B; Ottino-Gonzalez, Jonatan; Murphy, Alistair; Pancholi, Devarshi; Juliano, Anthony; Chaarani, Bader; Albaugh, Matthew; Yuan, Dekang; Schwab, Nathan; Stafford, James; Goudriaan, Anna E; Hutchison, Kent; Li, Chiang-Shan R; Luijten, Maartje; Groefsema, Martine; Momenan, Reza; Schmaal, Lianne; Sinha, Rajita; van Holst, Ruth J; Veltman, Dick J; Wiers, Reinout W; Porjesz, Bernice; Lett, Tristram; Banaschewski, Tobias; Bokde, Arun L W; Desrivières, Sylvane; Flor, Herta; Grigis, Antoine; Gowland, Penny; Heinz, Andreas; Brühl, Rüdiger; Martinot, Jean-Luc; Martinot, Marie-Laure Paillère; Artiges, Eric; Nees, Frauke; Orfanos, Dimitri Papadopoulos; Paus, Tomáš; Poustka, Luise; Hohmann, Sarah; Millenet, Sabina; Fröhner, Juliane H; Robinson, Lauren; Smolka, Michael N; Walter, Henrik; Winterer, Jeanne; Schumann, Gunter; Whelan, Robert; Bhatt, Ravi R; Zhu, Alyssa; Conrod, Patricia; Jahanshad, Neda; Thompson, Paul M; Mackey, Scott; Garavan, Hugh; IMAGEN Consortium; ENIGMA Addiction Working Group
- Year
- 2023
- Journal
- Molecular psychiatry
- PMID
- 36380235
- DOI
- 10.1038/s41380-022-01855-6
- PMCID
- PMC12659924
The neurobiological bases of the association between development and psychopathology remain poorly understood. Here, we identify a shared spatial pattern of cortical thickness (CT) in normative development and several psychiatric and neurological disorders. Principal component analysis (PCA) was applied to CT of 68 regions in the Desikan-Killiany atlas derived from three large-scale datasets comprising a total of 41,075 neurotypical participants. PCA produced a spatially broad first principal component (PC1) that was reproducible across datasets. Then PC1 derived from healthy adult participants was compared to the pattern of CT differences associated with psychiatric and neurological disorders comprising a total of 14,886 cases and 20,962 controls from seven ENIGMA disease-related working groups, normative maturation and aging comprising a total of 17,697 scans from the ABCD Study® and the IMAGEN developmental study, and 17,075 participants from the ENIGMA Lifespan working group, as well as gene expression maps from the Allen Human Brain Atlas. Results revealed substantial spatial correspondences between PC1 and widespread lower CT observed in numerous psychiatric disorders. Moreover, the PC1 pattern was also correlated with the spatial pattern of normative maturation and aging. The transcriptional analysis identified a set of genes including KCNA2, KCNS1 and KCNS2 with expression patterns closely related to the spatial pattern of PC1. The gene category enrichment analysis indicated that the transcriptional correlations of PC1 were enriched to multiple gene ontology categories and were specifically over-represented starting at late childhood, coinciding with the onset of significant cortical maturation and emergence of psychopathology during the prepubertal-to-pubertal transition. Collectively, the present study reports a reproducible latent pattern of CT that captures interregional profiles of cortical changes in both normative brain maturation and a spectrum of psychiatric disorders. The pubertal timing of the expression of PC1-related genes implicates disrupted neurodevelopment in the pathogenesis of the spectrum of psychiatric diseases emerging during adolescence.
The spatial pattern of standard loadings of PC1 for various datasets and their correlations. A. The spatial pattern of standard loadings of PC1 for adult participants in UKB-CTRL, ENIGMA-CTRL and IMAGEN-T2. A Combined-PC1 is derived from all participants in these datasets and used for further analysis. B. The spatial pattern of standard loadings of PC1 for peri-pubescent participants in ABCD-T1 (10 years), ABCD-T2 (12 years) and IMAGEN-T1 (14 years). C. Pairwise Pearson’s correlation coefficients among standard loadings of PC1 across datasets, with significant correlations (p-spin <0.05) colored.
Normative aging effects on regional CT and their correlation with Combined-PC1. A. Age effects on regional CT quantified as effect sizes of sampling time (T2 vs. T1 with T1 as reference) from the longitudinal IMAGEN and ABCD data. B. Pearson’s correlation between age and CT during the early (3–29 years), middle (30–59 years) and late (60–90 years) developmental stages, and proportion of variance in the data explained by age and its polynomial combination across the lifespan (3–90 years) taken from published results from the ENIGMA Lifespan working group. C. Pairwise Pearson’s correlation coefficients among spatial patterns of normative aging effects and Combined-PC1, with significant correlations (p-spin <0.05) colored. The negative signs in the last column reflect the fact that the standard loadings of the Combined-PC1 are positive and most regional effect sizes of normative aging are negative.
Effect sizes of case-control comparisons on regional CT and their correlation with Combined-PC1. A. Effect sizes of case-control comparisons for alcohol dependence (ALCgc) calculated using the ENIGMA and UKB datasets. B. Effect sizes of case-control comparisons for bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), schizophrenia (SCZ), epilepsy (EPI) and clinical high risk for psychosis (CHR) taken from published results from other ENIGMA disease-related working groups. C. Pairwise Pearson’s correlation coefficients among the spatial patterns of case-control effect sizes and Combined-PC1, with significant correlations (p-spin<0.05) colored. The negative signs in the last column reflect the fact that the standard loadings of the Combined-PC1 are positive and most regional effect sizes from case-control comparisons are negative.
Results of group comparison between participants with alcohol dependence and controls in ENIGMA-Addiction and UKB with age, sex, ICV and site adjusted (left) and with PC1 removed (right). The 2nd and 4th rows show the effect sizes of regions with FDR-corrected p<0.05.
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