174. Global Research Efforts to Characterize the Neurobiology of the Psychosis Risk Syndrome and to Optimize Clinical Prediction

Abstract

Overall Abstract: Since the development and validation of clinical criteria for prospectively defning a psychosis risk syndrome associated with increased risk of transition to full psychosis, research programs worldwide have been engaged in efforts to prospectively study the prodromal period prior to psychosis onset. Goals of these programs have been to extend the initial predictive relationships between subthreshold psychotic symptoms and transition to psychosis by characterizing the neurobiological and neu-rocognitive abnormalities associated with the psychosis risk syndrome and by considering whether consideration of these nonclinical domains can enhance our ability to predict who among clinical high risk (CHR) individuals are at greatest risk for transition to psychosis. Findings from these mature CHR research programs are yielding not only promising neurocog-nitive and neurobiological biomarkers of psychosis risk but are producing an increasingly detailed understanding of the structural and functional brain abnormalities that precede psychosis onset and their pathogenic con-tributions to the transition to psychosis. In the current symposium, progress in these efforts is presented from research programs in North America, Asia, and Europe. These results converge in documenting (1) neurocogni-tive defcits, as presented by Dr. Larry Seidman (USA) from the NAPLS consortium, (2) neurophysiological abnormalities refective of abnormal information processing (e.g., the P300 event-related potential), as presented by Dr. Dorien Nieman (the Netherlands) from the Dutch Prediction of Psychosis Study, and underlying neuroanatomical and microstructural compromise of the nodes and connections comprising thalamocortical networks in the brain, as presented by Dr. Jun Soo Kwon (South Korea) from Seoul National University. Ultimately, the potential clinical utility of these fndings depends on the implementation of quantitative methods, such as machine learning, that optimally combine data from multiple domains of measurement to achieve valid and robust psychosis prediction algorithms, a topic presented by Dr. Nikolaos Koutsouleris (Germany) from the PRONIA consortium. Dr. Scott Woods will serve as discussant, commenting on integrative links among the data presented, their implications for clinical prediction, and, ultimately, their potential to facilitate development of early interventions that prevent, or improve the clinical outcomes of, psychotic disorders such as schizophrenia.