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. 2021 Apr 8;52(16):1-12.
doi: 10.1017/S0033291721001082. Online ahead of print.

Effects of polygenic risk for major mental disorders and cross-disorder on cortical complexity

Simon Schmitt  1   2   3 , Tina Meller  1   2 , Frederike Stein  1   2 , Katharina Brosch  1   2   3 , Kai Ringwald  1   2 , Julia-Katharina Pfarr  1   2 , Clemens Bordin  1 , Nina Peusch  1 , Olaf Steinsträter  1 , Dominik Grotegerd  4 , Katharina Dohm  4 , Susanne Meinert  4 , Katharina Förster  4 , Ronny Redlich  4   5 , Nils Opel  4 , Tim Hahn  4 , Andreas Jansen  1   2   6 , Andreas J Forstner  7   8   9 , Fabian Streit  10 , Stephanie H Witt  10 , Marcella Rietschel  10 , Bertram Müller-Myhsok  11   12   13 , Markus M Nöthen  8 , Udo Dannlowski  4 , Axel Krug  1   2   14 , Tilo Kircher  1   2   3 , Igor Nenadić  1   2   3
Affiliations

Effects of polygenic risk for major mental disorders and cross-disorder on cortical complexity

Simon Schmitt et al. Psychol Med. .

Abstract

Background: MRI-derived cortical folding measures are an indicator of largely genetically driven early developmental processes. However, the effects of genetic risk for major mental disorders on early brain development are not well understood.

Methods: We extracted cortical complexity values from structural MRI data of 580 healthy participants using the CAT12 toolbox. Polygenic risk scores (PRS) for schizophrenia, bipolar disorder, major depression, and cross-disorder (incorporating cumulative genetic risk for depression, schizophrenia, bipolar disorder, autism spectrum disorder, and attention-deficit hyperactivity disorder) were computed and used in separate general linear models with cortical complexity as the regressand. In brain regions that showed a significant association between polygenic risk for mental disorders and cortical complexity, volume of interest (VOI)/region of interest (ROI) analyses were conducted to investigate additional changes in their volume and cortical thickness.

Results: The PRS for depression was associated with cortical complexity in the right orbitofrontal cortex (right hemisphere: p = 0.006). A subsequent VOI/ROI analysis showed no association between polygenic risk for depression and either grey matter volume or cortical thickness. We found no associations between cortical complexity and polygenic risk for either schizophrenia, bipolar disorder or psychiatric cross-disorder when correcting for multiple testing.

Conclusions: Changes in cortical complexity associated with polygenic risk for depression might facilitate well-established volume changes in orbitofrontal cortices in depression. Despite the absence of psychopathology, changed cortical complexity that parallels polygenic risk for depression might also change reward systems, which are also structurally affected in patients with depressive syndrome.

Keywords: Bipolar disorder; brain development; cortical complexity; magnetic resonance imaging (MRI); major depressive disorder; polygenic risk; schizophrenia; surface-based morphometry.

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Figures

Fig. 1.
Fig. 1.
Associations between the polygenic risk for MDD and CC. Orbitofrontal cortical folding complexity is significantly associated with polygenic risk for major depression (for the purpose of display, images are shown at p < 0.001, uncorrected threshold). The cluster in 24/33/-12 withstood correction for multiple comparisons (p = 0.006, FWE cluster-level correction).
Fig. 2.
Fig. 2.
Scatter plot showing the association between the polygenic risk score for MDD and adjusted averaged cortical complexity in a significant cluster in the right orbitofrontal cortex. Note. Adjusted cortical complexity values were cluster-wise extracted for every participant using the CAT12 function cat_surf_results. Cluster values were calculated as β-values based on the used contrast of the corresponding multiple regression, including its covariates and residuals. A non-parametric correlation yielded also a significant association: Spearman's ρ = −0.189 (p < 0.0001).
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