The inhibitory gamma-aminobutyric acid (GABA) system is involved in the etiology of most psychiatric disorders, including schizophrenia, autism spectrum disorder (ASD) and major depressive disorder (MDD). It is therefore not surprising that proton magnetic resonance spectroscopy ((1) H-MRS) is increasingly used to investigate in vivo brain GABA levels. However, integration of the evidence for altered in vivo GABA levels across psychiatric disorders is lacking. We therefore systematically searched the clinical (1) H-MRS literature and performed a meta-analysis. A total of 40 studies (N = 1,591) in seven different psychiatric disorders were included in the meta-analysis: MDD (N = 437), schizophrenia (N = 517), ASD (N = 150), bipolar disorder (N = 129), panic disorder (N = 81), posttraumatic stress disorder (PTSD) (N = 104), and attention deficit/hyperactivity disorder (ADHD) (N = 173). Brain GABA levels were lower in ASD (standardized mean difference [SMD] = -0.74, P = 0.001) and in depressed MDD patients (SMD = -0.52, P = 0.005), but not in remitted MDD patients (SMD = -0.24, P = 0.310) compared with controls. In schizophrenia this finding did not reach statistical significance (SMD = -0.23, P = 0.089). No significant differences in GABA levels were found in bipolar disorder, panic disorder, PTSD, and ADHD compared with controls. In conclusion, this meta-analysis provided evidence for lower brain GABA levels in ASD and in depressed (but not remitted) MDD patients compared with healthy controls. Findings in schizophrenia were more equivocal. Even though future (1) H-MRS studies could greatly benefit from a longitudinal design and consensus on the preferred analytical approach, it is apparent that (1) H-MRS studies have great potential in advancing our understanding of the role of the GABA system in the pathogenesis of psychiatric disorders. Hum Brain Mapp 37:3337-3352, 2016. © 2016 Wiley Periodicals, Inc.

Under the influence of genes and a varying environment, human brain structure changes throughout the lifespan. Even in adulthood, when the brain seems relatively stable, individuals differ in the profile and rate of brain changes 1 . Longitudinal studies are crucial to identify genetic and environmental factors that influence the rate of these brain changes throughout development 2 and aging 3 . Inter-individual differences in brain development are associated with general cognitive function 4,5 and risk for psychiatric disorders 6,7 and neurological diseases 8,9 . Genetic factors involved in brain development and aging overlap with those for cognition 10 and risk for neuropsychiatric disorders 11 . A recent cross-sectional study showed brain age to be advanced in several brain disorders. Brain age is an estimate of biological age based on brain structure, which can deviate from chronological age. Several shared loci were found between the genome-wide association study (GWAS) summary statistics for advanced brain age and psychiatric disorders 12 . However, information is still lacking on which genetic variants influence an individual's brain changes throughout life, because this requires longitudinal data. Discovering genetic factors that explain variation between individuals in brain structural changes may reveal key biological pathways that drive normal development and aging and may contribute to identifying disease risk and resilience-a crucial goal given the urgent need for new treatments for aberrant brain development and aging worldwide.As part of the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium 13 , the ENIGMA Plasticity Working Group quantified the overall genetic contribution to longitudinal brain changes by combining evidence from multiple twin cohorts across the world 14 . Most global and subcortical brain measures showed genetic influences on change over time, with a higher genetic contribution in the elderly (heritability, 16-42%). Genetic factors that influence longitudinal changes were partially independent of those that influence baseline volumes of brain structures, suggesting that there might be genetic variants that specifically affect the rate of development or aging. However, the genes involved in these processes are still not known, with only a single, small-scale GWAS performed for longitudinal volume change in gray and white matter of the cerebrum, basal ganglia and cerebellum 15 . In this study, we set out to find genetic variants that may influence rates of brain changes over time, using genome-wide analysis in individuals scanned with magnetic resonance imaging (MRI) on more than one occasion. We also aimed to identify references

Alcohol consumption is commonly initiated during adolescence, but the effects on human brain development remain unknown. In this multisite study, we investigated the longitudinal associations of adolescent alcohol use and brain morphology. Three longitudinal cohorts in the Netherlands (BrainScale n = 200, BrainTime n = 239 and a subsample of the Generation R study n = 318) of typically developing participants aged between 8 and 29 years were included. Adolescent alcohol use was self‐reported. Longitudinal neuroimaging data were collected for at least two time points. Processing pipelines and statistical analyses were harmonized across cohorts. Main outcomes were global and regional brain volumes, which were a priori selected. Linear mixed effect models were used to test main effects of alcohol use and interaction effects of alcohol use with age in each cohort separately. Alcohol use was associated with adolescent's brain morphology showing accelerated decrease in grey matter volumes, in particular in the frontal and cingulate cortex volumes, and decelerated increase in white matter volumes. No dose–response association was observed. The findings were most prominent and consistent in the older cohorts (BrainScale and BrainTime). In summary, this longitudinal study demonstrated differences in neurodevelopmental trajectories of grey and white matter volume in adolescents who consume alcohol compared with non‐users. These findings highlight the importance to further understand underlying neurobiological mechanisms when adolescents initiate alcohol consumption. Therefore, further studies need to determine to what extent this reflects the causal nature of this association, as this longitudinal observational study does not allow for causal inference.