The dark side of the mean: brain structural heterogeneity in schizophrenia and its polygenic risk

Abstract

Telephone: +47 23 02 73 50, Fax: +47 23 02 73 33 32 33 34 3 Key Points 35Question: Is schizophrenia and its polygenic risk associated with brain structural 36 heterogeneity in addition to mean changes? 37Findings: In a sample of 1151 patients and 2010 controls, schizophrenia was associated with 38 increased heterogeneity in fronto-temporal thickness, cortical, ventricle, and hippocampal 39 volumes, besides robust reductions in mean estimates. In an independent sample of 12,490 40 controls, polygenic risk for schizophrenia was associated with thinner fronto-temporal 41 cortices and smaller CA2/3 of the left hippocampus, but not with heterogeneity. 42Meaning: Schizophrenia is associated with increased inter-individual differences in brain 43 structure, possibly reflecting clinical heterogeneity, gene-environment interactions, or 44 secondary disease factors. 45 46 4 Abstract 47 Importance: Between-subject variability in brain structure is determined by gene-48 environment interactions, possibly reflecting differential sensitivity to environmental and 49 genetic perturbations. Magnetic resonance imaging (MRI) studies have revealed thinner 50 cortices and smaller subcortical volumes in patients. However, such group-level comparisons 51 may mask considerable within-group heterogeneity, which has largely remained unnoticed in 52 the literature 53Objective: To compare brain structural variability between individuals with SZ and healthy 54 controls (HC) and to test if respective variability reflects the polygenic risk for SZ (PRS) in 55 HC. 56Design, Setting, and Participants: We compared MRI derived cortical thickness and 57 subcortical volumes between 2,010 healthy controls and 1,151 patients with SZ across 16 58cohorts. Secondly, we tested for associations between PRS and MRI features in 12,490 59 participants from UK Biobank. 60 Main Outcomes and Measures:We modeled mean and dispersion effects of SZ and PRS 61 using double generalized linear models. We performed vertex-wise analyses for thickness, 62 and region-of-interest analysis for cortical, subcortical and hippocampal subfield volumes. 63Follow-up analyses included within-sample analysis, controlling for intracranial volume and 64 population covariates, test of robustness of PRS threshold, and outlier removal. 65Results: Compared to controls, patients with SZ showed higher heterogeneity in cortical 66 thickness, cortical and ventricle volumes, and hippocampal subfields. Higher PRS was 67 associated with thinner frontal and temporal cortices, as well as smaller left CA2/3, but was 68 not significantly associated with dispersion. 69Conclusion and relevance: SZ is associated with substantial brain structural heterogeneity 70 beyond the mean differences. These findings possibly reflect higher differential sensitivity to 71 5 environmental and genetic perturbations in patients, supporting the heterogeneous nature of 72 SZ. Higher PRS for SZ was associated with thinner fronto-temporal cortices and smaller 73 subcortical volumes, but there were no significant associati...

Genetic influences on structural and functional brain maturation

“…5.4.1 Brain aging and its association to polygenic risk for schizophrenia Diagnosis status (schizophrenia vs. healthy) had a highly significant effect on brain aging. The age gap, i.e., the difference between estimated age and chronological age, was 4 years in patients (and zero in healthy subjects), and the brain age acceleration rate was 2 year/year (and 1 in healthy subjects), consistent with the previously reported results for the broader sample (Schnack et al, 2016) and with other studies reporting accelerated aging of the brain in schizophrenia patients (Koutsouleris et al, 2014;Nenadić et al, 2017;Kaufmann et al, 2019;Jonsson et al, 2019) and in subjects at clinically high-risk for psychosis and first-episode patients (Kolenic et al, 2018;Chung et al, 2018;Hajek et al, 2019;Shahab et al, 2019). Polygenic risk for schizophrenia (PRS), as expected, was significantly higher in the patients as compared to the control subjects (p < 0.0001).…”

“…A classification algorithm can be trained on selected features from the brain, such as grey matter density, to predict a person's "biological" brain age (Cole and Franke, 2017;Franke and Gaser, 2019). Patients with schizophrenia show advanced brain age that is accelerated around the time of onset (Koutsouleris et al, 2014;Schnack et al, 2016;Nenadić et al, 2017;Kaufmann et al, 2019). Accelerated brain age predicts all-cause mortality (Cole et al, 2018), is highly heritable and has a genetic overlap with common brain disorders, including schizophrenia Kaufmann et al, 2019).…”

“…Patients with schizophrenia show advanced brain age that is accelerated around the time of onset (Koutsouleris et al, 2014;Schnack et al, 2016;Nenadić et al, 2017;Kaufmann et al, 2019). Accelerated brain age predicts all-cause mortality (Cole et al, 2018), is highly heritable and has a genetic overlap with common brain disorders, including schizophrenia Kaufmann et al, 2019). Similar to a person's brain age, the biological age of a person can be predicted from DNA methylation (Horvath, 2013;Hannum et al, 2013;Levine et al, 2018).…”

“…Using neuroimaging, the biological age of the brain can be predicted from gray matter distributions (Cole and Franke, 2017;Valizadeh et al, 2017), white matter properties (Mwangi et al, 2013), or brain-activity related properties (Dosenbach et al, 2010). For patients with schizophrenia, accelerated aging of the brain occurs around the onset of psychosis (Koutsouleris et al, 2014;Schnack et al, 2016;Nenadić et al, 2017;Kaufmann et al, 2019;Jonsson et al, 2019;Kolenic et al, 2018;Chung et al, 2018;Hajek et al, 2019;Shahab et al, 2019) before stabilizing several years after onset (Schnack et al, 2016). Accelerated brain age predicts all-cause mortality (Cole et al, 2018), is highly heritable and has a genetic overlap with common brain disorders, including schizophrenia Kaufmann et al, 2019).…”

“…For patients with schizophrenia, accelerated aging of the brain occurs around the onset of psychosis (Koutsouleris et al, 2014;Schnack et al, 2016;Nenadić et al, 2017;Kaufmann et al, 2019;Jonsson et al, 2019;Kolenic et al, 2018;Chung et al, 2018;Hajek et al, 2019;Shahab et al, 2019) before stabilizing several years after onset (Schnack et al, 2016). Accelerated brain age predicts all-cause mortality (Cole et al, 2018), is highly heritable and has a genetic overlap with common brain disorders, including schizophrenia Kaufmann et al, 2019). For biological tissue samples, several molecular and phenotypic biomarkers of aging have been reported for research into proteomic, transcriptomics, metabolomics, telomere length, and DNA methylation levels (Jylhävä et al, 2017;Nguyen et al, 2017).…”