Pathophysiology Informed Biomarkers of Treatment Response in Early Psychosis (PIB)

The introduction of Coordinated Specialty Care (CSC) has transformed the standard of care and
elevated treatment outcome goals for young individuals experiencing the initial stages of a
psychotic illness (EP). The response to treatment for EP individuals receiving CSC, however,
remains highly variable. A substantial proportion show minimal symptom reduction despite
receiving the full range of evidence-based practices comprising this treatment model. Currently,
clinicians have no way to predict which EP individuals entering CSC will respond to treatment
and published data show that expert clinicians perform no better than chance. Early
identification of treatment non-responders has very high clinical significance and would inform
and enhance clinical decision making during the first few months of care. Surprisingly, little
research has been conducted on baseline predictors of treatment outcomes in EP individuals
entering CSC. During the past two decades, considerable progress has been made using
neuroimaging to investigate pathophysiological processes during the early phases of illness.
Furthermore, limited data suggest that fMRI measures of brain activity and PET measures of
increased dopamine synthesis are related to treatment outcomes in EP. We have recently
demonstrated in a moderately large sample of EP patients entering CSC that the ability to
activate the frontal parietal (FP) cognitive control network (measured using fMRI during the AX-
CPT task) is a significant predictor of who will meet responder criterion after one year of CSC.
We propose to replicate and extend this result by examining the predictive ability of this and two
other promising MRI based measures linked to pathophysiological processes related to
psychosis: 1) free water diffusion tensor imaging (FW) - a putative biomarker of
neuroinflammation that is increased in EP individuals, and 2) midbrain neuromelanin (NM)
scans, which index midbrain dopamine, shown to be decreased in Parkinson's disease and
increased in schizophrenia. Each of these measures will be used individually to predict
responder status for EP participants entering CSC. In addition to these analyses we will use
novel deep learning methods to optimize the prediction of treatment response in EP individuals
entering CSC and to obtain new insights into the mechanisms underlying these effects. Our goal
is to leverage recent progress in the development of MRI based imaging biomarkers to develop
a precision medicine tool that can identify early psychosis patients entering CSC who are at
high risk for non-response and thereby inform treatment decision making for all patients in order
to optimize the recovery of young individuals following the onset of psychotic illness.

Grant Number: 5R01MH122139-06
NIH Institute/Center: NIH
Principal Investigator: Cameron Carter