Identifying predictors and elucidating mechanisms underlying psychosis onset are critical for the
development of targeted interventions. The current symptom-based clinical high risk (CHR)
syndrome has been validated as an indicator of future psychosis risk but does not provide
accurate predictions regarding individual clinical trajectories or a stratification model that informs
outcome or treatment response. The CHR phase is diverse in terms of risk factors and outcomes,
with a significant proportion of CHR converters (<30%) developing non-affective psychosis
(~73%) and a smaller group developing affective psychoses (~11%). In addition, CHR non-
converters have variable outcomes, ranging from full remission to ongoing multifaceted sequelae.
Furthermore, substantial neurobiological heterogeneity among CHR individuals is well-
documented. Despite this, little research attempted to harness the rich multimodal data collected
in large-scale CHR studies to form and validate neurobiologically driven CHR subgroups.
Successful parsing of the underlying heterogeneity of CHR may yield more defined subgroups
with distinct clinical trajectories and outcomes. Our group’s recent studies examining
neurobiological heterogeneity in psychosis spectrum using the Bipolar-Schizophrenia Network for
Intermediate Phenotypes dataset, adopted a data-driven cluster analytic method to define
biological clusters (or biotypes) using cognitive, and electrophysiological biomarkers agnostic to
traditional symptom-based diagnostic categories. The goal was to identify neurobiologically
homogeneous biotypes with presumed distinct underlying pathophysiology. However, the sample
consisted of chronic psychosis subjects and thus was unable to inform the development of early
intervention and prevention applications, and lacked clinically relevant longitudinal outcome data.
An approach focusing on CHR to define biotypes and assess them using longitudinal clinical, and
functional outcome data has yet to be attempted. The availability of prospectively characterized
and deeply phenotyped CHR samples from the North American Prodrome Longitudinal Study 2
and 3 is a unique opportunity to address this question using an objective approach. In the current
proposal, we will identify distinct subgroups or ‘biotypes’ for CHR using cluster analysis and will
compare, evaluate and validate the resulting biotypes. This proposal will have an important impact
on our understanding of how biological heterogeneity contributes to clinical outcomes in CHR and
elucidates a way to characterize biological heterogeneity in this population, providing biological
targets for more effective diagnosis, and early therapeutic intervention.
