Selective Review of Neuroimaging Findings in Youth at Clinical High Risk for Psychosis: On the Path to Biomarkers for Conversion

Abstract

First episode psychosis (FEP), and subsequent diagnosis of schizophrenia or schizoaffective disorder, predominantly occurs during late adolescence, is accompanied by a significant decline in function and represents a traumatic experience for patients and families alike. Prior to first episode psychosis, most patients experience a prodromal period of 1–2 years, during which symptoms first appear and then progress. During that time period, subjects are referred to as being at Clinical High Risk (CHR), as a prodromal period can only be designated in hindsight in those who convert. The clinical high-risk period represents a critical window during which interventions may be targeted to slow or prevent conversion to psychosis. However, only one third of subjects at clinical high risk will convert to psychosis and receive a formal diagnosis of a primary psychotic disorder. Therefore, in order for targeted interventions to be developed and applied, predicting who among this population will convert is of critical importance. To date, a variety of neuroimaging modalities have identified numerous differences between CHR subjects and healthy controls. However, complicating attempts at predicting conversion are increasingly recognized co-morbidities, such as major depressive disorder, in a significant number of CHR subjects. The result of this is that phenotypes discovered between CHR subjects and healthy controls are likely non-specific to psychosis and generalized for major mental illness. In this paper, we selectively review evidence for neuroimaging phenotypes in CHR subjects who later converted to psychosis. We then evaluate the recent landscape of machine learning as it relates to neuroimaging phenotypes in predicting conversion to psychosis.

Insula volumes in first-episode and chronic psychosis: A longitudinal MRI study

“…Indeed, the identification of such diseases or trait related markers will greatly help a) the development of personalized strategies (Sverdlov et al, 2021), b) the improvement of prevention and early identification approaches (Andreou and Borgwardt, 2020;Ellis et al, 2020), and c) to pave the way for new drug development (Loiodice et al, 2021).…”

Functional ultrasound imaging reveals an aberrant developmental trajectory of functional connectivity in a mouse model of the 15q13.3 copy number variant microdeletion

“…Identification of individuals that are at a high clinical risk for conversion to psychosis in addition to predicting which of these patients will convert, and when that change might occur, could help to thwart illness and improve outcomes. In fact, only a third of subjects deemed clinical high risk eventually receive a formal diagnosis of psychosis (Ellis et al, 2020). Our results show that these same analyses can be applied to a preclinical model, providing a translational tool that may further enhance drug development.…”

Global fractional anisotropy predicts transition to psychosis after 12 months in individuals at ultra‐high risk for psychosis

“…The joint initiatives to coordinate multicentre studies and methodology, such as the enigma‐DTI workgroup, 85 the PRONIA study (https://www.pronia.eu) and PSYSCAN consortia, 86 are an important development. Such multisite studies, examining multimodal data sets with large sample sizes, appear to increase accuracy of prediction models 87 . In particular, if these studies apply pattern recognition or machine‐learning methods allowing for individual classifications, 26 the neurobiological refinement of current clinical prediction models could be amplified and ultimately facilitate tailored preventive interventions at the individual level.…”