Towards Precision Medicine in Psychosis: Benefits and Challenges of Multimodal Multicenter Studies—PSYSCAN: Translating Neuroimaging Findings From Research into Clinical Practice

Abstract

In the last 2 decades, several neuroimaging studies investigated brain abnormalities associated with the early stages of psychosis in the hope that these could aid the prediction of onset and clinical outcome. Despite advancements in the field, neuroimaging has yet to deliver. This is in part explained by the use of univariate analytical techniques, small samples and lack of statistical power, lack of external validation of potential biomarkers, and lack of integration of nonimaging measures (eg, genetic, clinical, cognitive data). PSYSCAN is an international, longitudinal, multicenter study on the early stages of psychosis which uses machine learning techniques to analyze imaging, clinical, cognitive, and biological data with the aim of facilitating the prediction of psychosis onset and outcome. In this article, we provide an overview of the PSYSCAN protocol and we discuss benefits and methodological challenges of large multicenter studies that employ neuroimaging measures.

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

“…It has been suggested that limiting the number of predictors compared to the number of converters may assist in solving this problem ( 119 ). One example of a large multi-site consortium trying to overcome these issues is the PSYSCAN Consortium ( 140 ). They have developed a protocol which aims to use multimodal methodologies (clinical, cognitive, genetics, blood, and imaging) and machine learning to create algorithms that predict conversion.…”

From models to tools: clinical translation of machine learning studies in psychosis

“…In contrast, to develop machine learning models capable of providing clinically useful information, we need access to longitudinal data (for example, whether a patient did or did not respond to a full cycle of conventional antipsychotic medication). In the near future, a number of ongoing large-scale studies using a longitudinal design are expected to come to completion (e.g., PSYSCAN, 21 PRONIAwww.pronia.eu). It is hoped that the data resulting from these studies will provide our research community with opportunities to bridge the existing gap between models and tools.…”

“…Here the critical distinction is between "models", which tend to be developed and validated using a limited number of well characterised datasets with the aim of maximising accuracy, sensitivity and specificity, and "tools", which must be feasible, acceptable and safe, and provide information that will guide clinical decision-making in real-world settings. This is a timely discussion, as a new generation of multi-centre studies aiming to develop machine learning tools to manage patients with psychosis is emerging (e.g., PSYSCAN, 21 PRONIA-www.pronia.eu).…”

Study Protocol for a Prospective Longitudinal Cohort Study to Identify Proteomic Predictors of Pluripotent Risk for Mental Illness: The Seoul Pluripotent Risk for Mental Illness Study

“…This epidemiologic inadequacy is also supported by the fact that psychosis still develops, although at a low rate, in clinical high-risk for non-psychotic mental disorders (13). Lack of the pragmatic transdiagnostic ability of the CHR-P designation and modest statistical power due to the recruitment difficulties and low conversion rate do not given much latitude in highrisk research (14,15). As a way to overcome the limitation of statistical power, a method of expanding to a broad range of disorders has been proposed instead of limiting the outcome to schizophrenia.…”