Healthy adolescent brain maturation involves several highly coordinated processes including extensive and dynamic reorganization of structural and functional networks and neuropil contraction. However, excessive neuropil contraction, and impaired network reorganization may result in dysconnectivity in structural, diffusion, and functional networks that may explain why more than 50% of psychiatric disorders begin during adolescence. This is particularly notable in first-degree relatives of persons with schizophrenia/schizoaffective disorder (familial high risk for schizophrenia (FHR)) possibly due to additional familial risk. Prior studies on adolescents have examined gray matter (GM) metrics (e.g., volumes) to index neuropil changes and have studied one modality of network, e.g., structural, or functional to elucidate network pathology. However, GM metrics do not specifically index neuropil (cortical regions rich in synapses, dendrites, axonal endings, interneurons, glia, and unmyelinated axons with few cell bodies). Similarly, examining individual network modality (monoplex network) does not reflect concomitant changes in other network modalities. Therefore, we propose to examine neuropil growth/contraction using 3D whole-brain phosphorus magnetic resonance spectroscopy (31P MRS). To elucidate alterations in multiple modalities of networks, we will use a novel multiplex network analysis that provides composite metrics of dysconnectivity in multiple network layers that may be closer to the ‘ground-truth’ of network pathology since networks in the brain tissue are essentially multiplex in nature. Our goal is to longitudinally examine neuropil growth/contraction and multiplex network dysconnectivity in relation to categorical (axis I disorders) and dimensional (severity of cognitive/social deficits, and psychopathology) outcomes using an accelerated cohort design on 210 subjects between the ages encompassing onset of puberty and 18 years equally distributed for the FHR status. Our proposal is strongly supported by convergent preliminary data that show greater psychiatric morbidity, GM loss in the fronto-temporal network (FTN) regions and altered structural, diffusion, and functional networks among adolescent FHR persons, and impairments in selected multiplex network metrics suggesting multiplex network dysconnectivity. We will use ultra-high field neuroimaging at 7 Tesla to obtain high resolution MP2RAGE, 3D whole-brain 31P MRS with superior spectral resolution, diffusion weighted imaging with Neurite Orientation Dispersion Density Imaging (NODDI) modeling, and resting fMRI, and collect psychopathological, cognitive, and social function data at baseline, 1, and 2-years to characterize longitudinal trajectory of neuropil growth/contraction (Aim 1), its relationship to multiplex network dysconnectivity (Aim 2) and with categorical and dimensional outcomes. We will build an integrated model of multiplex network features and neuropil growth/ contraction to predict categorical and dimensional outcomes to improve predictive accuracy of categorical and dimensional outcomes (aim 3). We will recapitulate our results on ABCD data with available phenotypes. Thus, our proposal may help better understand adolescent brain maturation in relation to the clinical outcomes.

Public Health Significance Understanding the alterations in neuropil and connectivity pattern in multiple layers of networks involving structural covariance, white matter and functional connectomes is critical to elucidate adolescent brain maturation. Alterations in these processe may elucidate potential mechanisms underlying nearly 50% of psychiatric illnesses beginning during adolescence which are increasing in the last 2 decades especially among adolescents from lower socioeconomic status (SES) with its adverse consequences on school performance, family relationships, substance use and criminal justice system. This is more onerous for persons who have a first-degree relative with schizophrenia where our proposal could highlight neural pathways as targets for developing new treatment approaches and develop better predictive models to potentially mitigate the risk and develop preventative efforts.