Brain Heterogeneity in Schizophrenia and Its Association With Polygenic Risk

Abstract

for the Karolinska Schizophrenia Project Consortium IMPORTANCE Between-individual variability in brain structure is determined by gene-environment interactions, possibly reflecting differential sensitivity to environmental and genetic perturbations. Magnetic resonance imaging (MRI) studies have revealed thinner cortices and smaller subcortical volumes in patients with schizophrenia. However, group-level comparisons may mask considerable within-group heterogeneity, which has largely remained unnoticed in the literature.OBJECTIVES To compare brain structural variability between individuals with schizophrenia and healthy controls and to test whether respective variability reflects the polygenic risk score (PRS) for schizophrenia in an independent sample of healthy controls. DESIGN, SETTING, AND PARTICIPANTSThis case-control and polygenic risk analysis compared MRI-derived cortical thickness and subcortical volumes between healthy controls and patients with schizophrenia across 16 cohorts and tested for associations between PRS and MRI features in a control cohort from the UK Biobank.

Predicting cognitive and mental health traits and their polygenic architecture using large-scale brain connectomics

“…Relatedly, the current findings illustrate that the correlation traits and the brain is stronger for other imaging modalities. Indeed, some studies have demonstrated an association between cortical structure and polygenic scores for schizophrenia (52,53), which are supported by a recent large-scale UKB study reporting thinner fronto-temporal cortex with higher polygenic risk (36), suggesting shared mechanisms. However, the variance explained by polygenic scores, such as those for schizophrenia, in brain measures like gray matter volume, tend to be low, around a few percent (54).…”

“…Cross-validated results were very similar using a lower polygenic score threshold (p ≤ 0.05:Fig. S10), and after performing principal component analysis (PCA) across p-value thresholds on each of the above individual polygenic scores(36) (Fig. S11).…”

Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis

“…Pursuant to this, there has been considerable interest in identifying clinically relevant subgroups based on brain imaging, with initially encouraging results (Drysdale et al, ). However, the robustness and generalizability of such studies have been brought into question (Dinga et al, ), which may be partly due to substantial brain heterogeneity within groups, which has been illustrated in terms of morphometry in schizophrenia (Alnæs et al, ). Alternatively, dimensional measures such as brain age prediction (Kaufmann et al, In press) and normative modeling (Marquand et al, ; Marquand, Rezek, Buitelaar, & Beckmann, ) have shown promising results in elucidating brain heterogeneity in mental disorders such as schizophrenia (Wolfers et al, ) and attention deficit/hyperactivity disorder (Wolfers et al, ).…”

Brain age prediction reveals aberrant brain white matter in schizophrenia and bipolar disorder: A multi-sample diffusion tensor imaging study

“…While the current results support the existence of a common set of mechanisms across disorders, future studies utilizing a broader range of imaging modalities in combination with specific genetic, clinical, cognitive, sociodemographic and biological phenotypes may allow for the identification of specific diagnostic signatures and sub-groups. However, inherent limitations associated with the classical case-control design in mental health research have recently been emphasized using neuroimaging data (24,25). In particular, the current lack of biologically informed diagnostic criteria should motivate future studies to consider alternative approaches to promote a novel clinical nosology based both on symptomatology and data-driven clustering (56), as well as brain-based and biological phenotypes cutting across diagnostic boundaries.…”

“…Despite converging evidence of case-control differences both preceding and following disease onset, recent brain imaging studies have documented substantial heterogeneity within patient groups (24,25). In contrast to conventional group-level analyses, brain age prediction using machine learning on imaging features allows for brain-based phenotyping at the individual level, and enables an efficient dimensionality reduction of the neuroimaging data into one or more biologically informative summary measures (26,27).…”