Schizophrenia is widely recognized as a neurodevelopmental disorder, but determining neurodevelopmental features of schizophrenia requires a departure from classic case-control designs. Polygenic risk scoring for schizophrenia (PRS-SCZ) enables investigation of the influence of genetic risk for schizophrenia on cortical anatomy during neurodevelopment and prior to disease onset. PRS-SCZ and cortical morphometry were assessed in typically developing children (3 – 21 years) using T1-weighted MRI and whole genome genotyping (n=390) from the Pediatric Imaging, Neurocognition and Genetics (PING) cohort. Then, we sought to contextualise the findings using (i) age-matched transcriptomics, (ii) gradients of cortical differentiation and (iii) case-control differences of major psychiatric disorders. Higher PRS-SCZ was associated with greater cortical thickness in typically developing children, while surface area and cortical volume showed only subtle associations. Greater cortical thickness was most prominent in areas with heightened gene expression for dendrites and synapses. The pattern of PRS-SCZ associations with cortical thickness reflected functional specialisation in the cortex and was spatially related to cortical abnormalities of patient populations of schizophrenia, bipolar disorder, and major depression. Finally, age interaction models indicated PRS-SCZ effects on cortical thickness were most pronounced between ages 3 and 6, suggesting an influence of PRS-SCZ on cortical maturation early in life. Integrating imaging-genetics with multi-scale mapping of cortical organization, our work contributes to an emerging understanding of how risk for schizophrenia and related disorders manifest in early life.

Diffusion-weighted magnetic resonance imaging (dMRI) allows for the in-vivo assessment of anatomical white matter in the brain, thus allowing the depiction of structural connectivity. Using structural processing techniques and related methods, a growing body of literature has illustrated that connectomics is a crucial aspect to assessing the brain in health and disease. The Pediatric Imaging Neurocognition and Genetics (PING) dataset was collected and released openly to contribute to the assessment of typical brain development in a pediatric sample. This current work details the processing of diffusion-weighted images from the PING dataset, including rigorous quality assessment and fine-tuning of parameters at every step, to increase the accessibility of these data for connectomic analysis. This processing provides state-of-the-art diffusion measures, both classical diffusion tensor imaging (DTI) and more advanced HARDI-based metrics, enabling the evaluation not only of structural white matter but also of integrated multimodal analyses, i.e. combining structural information from dMRI with functional or gray matter analyses.