Current research typically examines single neuroimaging modalities to establish normative values, development related differences, and abnormalities in neuropsychiatric disorders. Little is known about how these complementary parameters of brain structure and function interrelate and how combined processes reflected in these parameters lead to a mature, healthy brain. Behavioral functioning, manifested in mental health and neurocognitive performance, shows marked developmental effects. While such measures have been related to specific neuroimaging modalities, there is limited knowledge on developmental effects of multimodal brain parameters related to psychopathology and neurocognition. The path from biological processes to behavior is through genomics, which can elucidate mechanistic neurobiological processes thereby offering hope for early identification, prevention and intervention in aberrant development. Finally, to understand how brain changes relate to behavioral changes it is essential to have longitudinal data. We propose to capitalize on our efforts to establish the Philadelphia Neurodevelopmental Cohort (PNC), which was designed to obtain data on neuropsychiatric features, neurocognitive performance, multimodal neuroimaging and genomics. In addition to analyzing the data on the initial assessment of the PNC sample that we share in dbGaP, we have been following a subsample of PNC participants that includes both typically developing and those at clinical high-risk (CHR) for psychosis. Therefore, we will be able to establish dimensionally and longitudinally which combination of clinical, neurocognitive, neuroimaging and genomic parameters best predicts progression to psychosis. PNC data analysis will identify biotypes based on development related differences in regional multimodal characterization of major brain structures and systems related to dimensions of psychopathology and neurocognitive domains. We will apply advanced anatomic parcellation and voxelwise connectome-wide association studies to delineate multi-modal development effects on structural and functional connectivity, and identify aberrations associated with psychopathology and neurocognitive deficits. Networks will be examined using hypergraphs and parameters such as segregation and modularity defined by multi- scale community detection methods. These efforts will establish candidate parameters for genomic analysis and will be used to examine the GWAS- findings from the PGC and associated polygene scores and their effects on patterns of development and emerging biotypes. We will test the ability of developmental biotypes derived from the current dataset to predict brain health and clinical status in a subsample of 500 participants with follow-up data at 24 and 36 months intervals after the PNC data were collected. Since the follow-up is on 200 typically developing, 200 psychosis prone and 100 individuals with other disorders, we will focus on the subgroup with psychosis risk while exploring associations with other clinical factor scores. The repeated- measures data will establish how changes in these parameters inform about developmental trajectories.
The PNC data can help address a substantial gap in our knowledge on how different neuroimaging parameters relate to each other, the respective developmental staging of these relations, and how this staging relates to behavioral performance and psychopathology. We will identify candidate parameters for genomic analysis with a battery of network diagnostics including segregation and modularity as defined by multi-scale community detection methods, establish normative development effects and aberrations associated with psychopathology, examine the GWAS- findings from the PGC and associated polygene scores and their effects on patterns of development and emerging biotypes, and validate these findings in a sample with 24 and 36 months follow-up.
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