Many of the major neuropsychiatric illnesses, including schizophrenia, have a typical age of onset in late adolescence. Late adolescence may reflect a critical period in brain development making it particularly vulnerable for the onset of psychopathology. Neuroimaging studies that focus on this age range may provide unique insights into the onset and course of psychosis. In this review, we examine the evidence from 2 unique longitudinal cohorts that span the ages from early childhood through young adulthood; a study of childhood-onset schizophrenia where patients and siblings are followed from ages 6 through to their early twenties, and an ultra-high risk study where subjects (mean age of 19 years) are studied before and after the onset of psychosis. From the available evidence, we make an argument that subtle, regionally specific, and genetically influenced alterations during developmental age windows influence the course of psychosis and the resultant brain phenotype. The importance of examining trajectories of development and the need for future combined approaches, using multimodal imaging together with molecular studies is discussed.

Autism spectrum disorder (ASD) depends on a clinical interview with no biomarkers to aid diagnosis. The current investigation interrogated single-nucleotide polymorphisms (SNPs) of individuals with ASD from the Autism Genetic Resource Exchange (AGRE) database. SNPs were mapped to Kyoto Encyclopedia of Genes and Genomes (KEGG)-derived pathways to identify affected cellular processes and develop a diagnostic test. This test was then applied to two independent samples from the Simons Foundation Autism Research Initiative (SFARI) and Wellcome Trust 1958 normal birth cohort (WTBC) for validation. Using AGRE SNP data from a Central European (CEU) cohort, we created a genetic diagnostic classifier consisting of 237 SNPs in 146 genes that correctly predicted ASD diagnosis in 85.6% of CEU cases. This classifier also predicted 84.3% of cases in an ethnically related Tuscan cohort; however, prediction was less accurate (56.4%) in a genetically dissimilar Han Chinese cohort (HAN). Eight SNPs in three genes (KCNMB4, GNAO1, GRM5) had the largest effect in the classifier with some acting as vulnerability SNPs, whereas others were protective. Prediction accuracy diminished as the number of SNPs analyzed in the model was decreased. Our diagnostic classifier correctly predicted ASD diagnosis with an accuracy of 71.7% in CEU individuals from the SFARI (ASD) and WTBC (controls) validation data sets. In conclusion, we have developed an accurate diagnostic test for a genetically homogeneous group to aid in early detection of ASD. While SNPs differ across ethnic groups, our pathway approach identified cellular processes common to ASD across ethnicities. Our results have wide implications for detection, intervention and prevention of ASD.