Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders

The deviation between chronological age and age predicted using brain MRI is a putative marker of overall brain health. Age prediction based on structural MRI data shows high accuracy in common brain disorders. However, brain aging is complex and heterogenous, both in terms of individual differences and the underlying biological processes. Here, we implemented a multimodal model to estimate brain age using different combinations of cortical area, thickness and sub‐cortical volumes, cortical and subcortical T1/T2‐weighted ratios, and cerebral blood flow (CBF) based on arterial spin labeling. For each of the 11 models we assessed the age prediction accuracy in healthy controls (HC, n = 750) and compared the obtained brain age gaps (BAGs) between age‐matched subsets of HC and patients with Alzheimer's disease (AD, n = 54), mild (MCI, n = 90) and subjective (SCI, n = 56) cognitive impairment, schizophrenia spectrum (SZ, n = 159) and bipolar disorder (BD, n = 135). We found highest age prediction accuracy in HC when integrating all modalities. Furthermore, two‐group case–control classifications revealed highest accuracy for AD using global T1‐weighted BAG, while MCI, SCI, BD and SZ showed strongest effects in CBF‐based BAGs. Combining multiple MRI modalities improves brain age prediction and reveals distinct deviations in patients with psychiatric and neurological disorders. The multimodal BAG was most accurate in predicting age in HC, while group differences between patients and HC were often larger for BAGs based on single modalities. These findings indicate that multidimensional neuroimaging of patients may provide a brain‐based mapping of overlapping and distinct pathophysiology in common disorders.

Vitamin D levels, brain volume, and genetic architecture in patients with psychosis

BACKGROUND

Lower vitamin D levels are found in people with schizophrenia and depressive disorders, and also associated with neuroimaging abnormalities such as reduced brain volume in both animals and humans. Reduced whole brain and increased ventricular volume are also systematically reported in schizophrenia. Even though vitamin D deficiency has been proposed as a risk mechanism for schizophrenia there exist no studies to date of the association between vitamin D levels and brain volume in this population. Therefore, we investigated the relationship between vitamin D levels and brain phenotypes in psychotic disorders, and assessed possible interactions with genetic variants in vitamin D receptor (VDR) and other genetic variants that play a role in vitamin D levels in the body.

METHODS

Our sample consisted of 83 psychosis patients and 101 healthy controls. We measured vitamin D levels as serum 25-hydroxyvitamin D. All participants were genotyped and neuroimaging conducted by structural magnetic resonance imaging.

RESULTS

Vitamin D levels were significantly positively associated with peripheral grey matter volume in patients (β 860.6; 95% confidence interval (CI) 333.4-1466, p < .003). A significant interaction effect of BSML marker (rs1544410) was observed to mediate the association between patient status and both white matter volume (β 23603.3; 95% CI 2732.8-48708.6, p < .05) and whole brain volume (β 46670.6, 95% CI 8817.8-93888.3, p < .04). Vitamin D did not predict ventricular volume, which rather was associated with patient status (β 4423.3, 95% CI 1583.2-7267.8p < .002) and CYP24A1 marker (rs6013897) (β 2491.5, 95% CI 269.7-4978.5, p < .04).

CONCLUSIONS

This is the first study of the association between vitamin D levels and brain volume in patients with psychotic disorders that takes into account possible interaction with genetic polymorphisms. The present findings warrant replication in independent samples.