Background

Increased inflammation, endothelial dysfunction, and structural brain abnormalities have been reported in both schizophrenia and bipolar disorder, but the relationships between these factors are unknown. We aimed to identify associations between markers of inflammatory and endothelial activation and structural brain variation in psychotic disorders.

Methods

We measured von Willebrand factor (vWf) as a marker of endothelial cell activation and six inflammatory markers (tumor necrosis factor-receptor 1, osteoprotegerin, interleukin-1-receptor antagonist, interleukin-6, C-reactive protein, CD40 ligand) in plasma and 16 brain structures obtained from MRI scans of 356 individuals (schizophrenia spectrum; n=121, affective spectrum; n=95, healthy control subjects; n=140). The relationship between the inflammatory and endothelial markers and brain measurements were investigated across groups.

Results

There was a positive association (p=2.5×10(-4)) between plasma levels of vWf and total volume of the basal ganglia which remained significant after correction for multiple testing. Treatment with first generation antipsychotics was associated with basal ganglia volume only (p=0.009). After adjusting for diagnosis and antipsychotic medication, vWf remained significantly associated with increased basal ganglia volume (p=0.008), in particular the right globus pallidus (p=3.7×10(-4)). The relationship between vWf and basal ganglia volume was linear in all groups, but the intercept was significantly higher in the schizophrenia group (df=2, F=8.2, p=3.4×10(-4)).

Conclusion

Our results show a strong positive correlation between vWf levels and basal ganglia volume, in particular globus pallidus, independent of diagnosis. vWf levels were significantly higher in schizophrenia, which could indicate a link between endothelial cell activation and basal ganglia morphology in schizophrenia patients.

Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.

Individuals at Clinical High Risk for Psychosis (CHR-P) demonstrate heterogeneity in clinical profiles and outcome features. However, the extent of neuroanatomical heterogeneity in the CHR-P state is largely undetermined. We aimed to quantify the neuroanatomical heterogeneity in structural magnetic resonance imaging measures of cortical surface area (SA), cortical thickness (CT), subcortical volume (SV), and intracranial volume (ICV) in CHR-P individuals compared with healthy controls (HC), and in relation to subsequent transition to a first episode of psychosis. The ENIGMA CHR-P consortium applied a harmonised analysis to neuroimaging data across 29 international sites, including 1579 CHR-P individuals and 1243 HC, offering the largest pooled CHR-P neuroimaging dataset to date. Regional heterogeneity was indexed with the Variability Ratio (VR) and Coefficient of Variation (CV) ratio applied at the group level. Personalised estimates of heterogeneity of SA, CT and SV brain profiles were indexed with the novel Person-Based Similarity Index (PBSI), with two complementary applications. First, to assess the extent of within-diagnosis similarity or divergence of neuroanatomical profiles between individuals. Second, using a normative modelling approach, to assess the 'normativeness' of neuroanatomical profiles in individuals at CHR-P. CHR-P individuals demonstrated no greater regional heterogeneity after applying FDR corrections. However, PBSI scores indicated significantly greater neuroanatomical divergence in global SA, CT and SV profiles in CHR-P individuals compared with HC. Normative PBSI analysis identified 11 CHR-P individuals (0.70%) with marked deviation (>1.5 SD) in SA, 118 (7.47%) in CT and 161 (10.20%) in SV. Psychosis transition was not significantly associated with any measure of heterogeneity. Overall, our examination of neuroanatomical heterogeneity within the CHR-P state indicated greater divergence in neuroanatomical profiles at an individual level, irrespective of psychosis conversion. Further large-scale investigations are required of those who demonstrate marked deviation.