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Association of Inter-individual Differences in Imaging Markers with Schizophrenia Phenotypes

Published online by Cambridge University Press:  23 March 2020

G. Pergola
Affiliation:
Institute of Psychiatry, Basic Medical Science- Neuroscience and Sense Organs, Bari, Italy
T. Quarto
Affiliation:
Institute of Psychiatry, Basic Medical Science- Neuroscience and Sense Organs, Bari, Italy
M. Papalino
Affiliation:
Institute of Psychiatry, Basic Medical Science- Neuroscience and Sense Organs, Bari, Italy
P. Di Carlo
Affiliation:
Institute of Psychiatry, Basic Medical Science- Neuroscience and Sense Organs, Bari, Italy
P. Selvaggi
Affiliation:
Institute of Psychiatry, Basic Medical Science- Neuroscience and Sense Organs, Bari, Italy Institute of Psychiatry- Psychology- and Neuroscience, Department of Neuroimaging, London, United Kingdom
B. Gelao
Affiliation:
Institute of Psychiatry, Basic Medical Science- Neuroscience and Sense Organs, Bari, Italy
G. Blasi
Affiliation:
Institute of Psychiatry, Basic Medical Science- Neuroscience and Sense Organs, Bari, Italy Institute of Psychiatry, Department of Neuroscience- Sense Organs- and Locomotive System, Bari, Italy
A. Bertolino
Affiliation:
Institute of Psychiatry, Basic Medical Science- Neuroscience and Sense Organs, Bari, Italy Institute of Psychiatry, Department of Neuroscience- Sense Organs- and Locomotive System, Bari, Italy

Abstract

Introduction

Neuroimaging studies have identified several candidate biomarkers of schizophrenia. However, it is unclear whether the considerable variability in these neurobiological correlates between patients can be translated into the clinical setting.

Objectives

We aimed to identify neuroimaging predictors of clinical course in patients with schizophrenia. Combined with the identification of genetically determined markers of schizophrenia risk, our studies aimed to elucidate the biological basis and the clinical relevance of inter-individual variability between patients.

Methods

We included over 150 patients with schizophrenia and 279 healthy volunteers across five neuroimaging centers in the framework of the IMAGEMEND project [1]. We performed multiple studies on MRI scans using random forests and ROC curves to predict clinical course. Data from healthy controls served to normalize the data from the clinical population and to provide a benchmark for the findings.

Results

We identified ensembles of neuroimaging markers and of genetic variants predictive of clinical course. Results highlight that (i) brain imaging carries significant clinical information, (ii) clinical information at baseline can considerably increase prediction accuracy.

Conclusion

The methodological challenges and the results will be discussed in the context of recent findings from other multi-site studies. We conclude that brain imaging data on their own right are relevant to stratify patients in terms of clinical course; however, complementing these data with other modalities such as genetics and clinical information is necessary to further develop the field towards clinical application of the predictions.

Disclosure of interest

Giulio Pergola is the academic supervisor of a Hoffmann-La Roche Collaboration grant that partially funds his salary.

Type
Symposium: Dissecting heterogeneity in psychiatric disorders using imaging and genetic markers
Copyright
Copyright © European Psychiatric Association 2017

References

Frangou, S., Schwarz, E., et al. World Psychiatry 2016 doi: 10.1002/wps.20334CrossRefGoogle Scholar
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