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.

Highlights

  • Schizophrenia is a debilitating illness that affects 1% of the global population [1, 2], shortens the lifespan of those afflicted [3], and imposes a substantial financial burden on patients, their families, and society [4, 5]

  • Prior to a first episode of psychosis (FEP), patients experience a prodromal period of 1–2 years, during which symptoms of psychosis first appear in an attenuated form and progress

  • Clinical follow up of the first cohort revealed that Clinical High Risk (CHR) subjects with the highest level of striatal dopamine synthesis converted to psychosis [105], and that progression towards psychosis was associated with increasing levels of dopamine [106]

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Summary

INTRODUCTION

Schizophrenia is a debilitating illness that affects 1% of the global population [1, 2], shortens the lifespan of those afflicted [3], and imposes a substantial financial burden on patients, their families, and society [4, 5]. It is characterized by positive symptoms, such as hallucinations and delusions, negative symptoms, such as anhedonia and amotivation, and cognitive symptoms, such

Neuroimaging in Clinical High Risk
Enlarged Ventricles
Decreased Grey Matter Volume
White Matter Deficits
Regional Abnormalities
Network Abnormalities
INFLAMMATORY PHENOTYPES
NEUROTRANSMITTER SPECIFIC PHENOTYPES
MACHINE LEARNING AND PREDICTION ALGORITHMS
Machine Learning and Clinical Phenotypes
Machine Learning and Neuroimaging Phenotypes
Findings
DISCUSSION AND CONCLUSION
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