The association between gene variants and longitudinal structural brain changes in psychosis: a systematic review of longitudinal neuroimaging genetics studies

Combining fMRI and DISC1 gene haplotypes to understand working memory-related brain activity in schizophrenia

The DISC1 gene is one of the most relevant susceptibility genes for psychosis. However, the complex genetic landscape of this locus, which includes protective and risk variants in interaction, may have hindered consistent conclusions on how DISC1 contributes to schizophrenia (SZ) liability. Analysis from haplotype approaches and brain-based phenotypes can contribute to understanding DISC1 role in the neurobiology of this disorder. We assessed the brain correlates of DISC1 haplotypes associated with SZ through a functional neuroimaging genetics approach. First, we tested the association of two DISC1 haplotypes, the HEP1 (rs6675281-1000731-rs999710) and the HEP3 (rs151229-rs3738401), with the risk for SZ in a sample of 138 healthy subjects (HS) and 238 patients. This approach allowed the identification of three haplotypes associated with SZ (HEP1-CTG, HEP3-GA and HEP3-AA). Second, we explored whether these haplotypes exerted differential effects on n-back associated brain activity in a subsample of 70 HS compared to 70 patients (diagnosis × haplotype interaction effect). These analyses evidenced that HEP3-GA and HEP3-AA modulated working memory functional response conditional to the health/disease status in the cuneus, precuneus, middle cingulate cortex and the ventrolateral and dorsolateral prefrontal cortices. Our results are the first to show a diagnosis-based effect of DISC1 haplotypes on working memory-related brain activity, emphasising its role in SZ.

Dysmaturational Longitudinal Epigenetic Aging During Transition to Psychosis

Psychosis frequently occurs during adolescence and young adulthood, possibly as a result of gene-environment interactions, mediated by epigenetic mechanisms such as DNA methylation. Methylation patterns can be leveraged to predict epigenetic age in order to identify anomalies in aging trajectories that may be associated with the emergence of psychosis. Thus, epigenetic age may provide a measurable surrogate of psychotic risk or psychosis' emergence, and shed light on the neurodevelopmental model of psychosis. In this study, we present the first longitudinal analysis of epigenetic age trajectory during conversion to psychosis in a population at ultra-high-risk, with available genome-wide methylation DNA at two time points, at baseline and after one year of follow-up (N = 38 × 2). After predicting epigenetic age, we computed epigenetic age gap as the cross-sectional difference between real age and predicted age, and (longitudinal) epigenetic age acceleration as the derivative of predicted age with respect to time. At baseline, future converters were 2.7 years younger than nonconverters and this difference disappeared at follow-up, when some converted to psychosis. This is because during conversion to psychosis, the epigenetic age of converters accelerated by 2.8 years/year compared to nonconverters. This acceleration was robust with a strictly positive 95% confidence interval, and held its significance after adjustment for age, sex, and cannabis intake. The methylation sites most associated with aging were on genes also linked with schizophrenia and neurodevelopmental disorders. This accelerated age trajectory, following a previous deceleration, may therefore reflect dysmaturational processes.

Multivariate Analysis and Modelling of multiple Brain endOphenotypes: Let's MAMBO!

Imaging genetic studies aim to test how genetic information influences brain structure and function by combining neuroimaging-based brain features and genetic data from the same individual. Most studies focus on individual correlation and association tests between genetic variants and a single measurement of the brain. Despite the great success of univariate approaches, given the capacity of neuroimaging methods to provide a multiplicity of cerebral phenotypes, the development and application of multivariate methods become crucial. In this article, we review novel methods and strategies focused on the analysis of multiple phenotypes and genetic data. We also discuss relevant aspects of multi-trait modelling in the context of neuroimaging data.

Reward-related brain structures are smaller in patients with schizophrenia and comorbid metabolic syndrome

OBJECTIVE

Metabolic syndrome (MS) is highly prevalent in schizophrenia and often a consequence of unhealthy behaviour. Reward-related brain areas might be associated with MS, since they play a major role in regulating health behaviour. This study examined the relationship between MS and brain volumes related to the reward system in schizophrenia.

METHOD

We included patients with schizophrenia, with MS (MS+; n = 23), patients with schizophrenia, without MS (MS-; n = 48), and healthy controls (n = 54). Global brain volumes and volumes of (sub)cortical areas, part of the reward circuit, were compared between patients and controls. In case of a significant brain volume difference between patients and controls, the impact of MS in schizophrenia was examined.

RESULTS

Patients had smaller total brain (TB; P = 0.001), GM (P = 0.010), larger ventricles (P = 0.026), and smaller reward circuit volume (P < 0.001) than controls. MS+ had smaller TB (P = 0.017), GM (P = 0.008), larger ventricles (P = 0.015), and smaller reward circuit volume (P = 0.002) than MS-. MS+ had smaller orbitofrontal cortex (OFC; P = 0.002) and insula volumes (P = 0.005) and smaller OFC (P = 0.008) and insula cortical surface area (P = 0.025) compared to MS-.

CONCLUSION

In schizophrenia, structural brain volume reductions in areas of the reward circuitry appear to be related to comorbid MS.