Predicting conversion to psychosis in clinical high risk patients using resting-state functional MRI features

Jolie McDonnell; William Hord; Jenna Reinen; Pablo Polosecki; Irina Rish; Guillermo Cecchi

doi:10.1117/12.2525341

15 March 2019 Predicting conversion to psychosis in clinical high risk patients using resting-state functional MRI features

Jolie McDonnell, William Hord, Jenna Reinen, Pablo Polosecki, Irina Rish, Guillermo Cecchi

Author Affiliations +

Proceedings Volume 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging; 109532A (2019) https://doi.org/10.1117/12.2525341
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Recent progress in artificial intelligence provides researchers with a powerful set of machine learning tools for analyzing brain imaging data. In this work, we explore a variety of classification algorithms and functional network features derived from resting-state fMRI data collected from clinical high-risk (prodromal schizophrenia) patients and controls, trying to identify features predictive of conversion to psychosis among a subset of CHR patients. While there are many existing studies suggesting that functional network features can be highly discriminative of schizophrenia when analyzing fMRI of patients suffering from the disease vs controls, few studies attempt to explore a similar approach to actual prediction of future psychosis development ahead of time, in the prodromal stage. Our preliminary results demonstrate the potential of fMRI functional network features to predict the conversion to psychosis in CHR patients. However, given the high variance of our results across different classifiers and subsets of data, a more extensive empirical investigation is required to reach more robust conclusions.

Citation Download Citation

Jolie McDonnell, William Hord, Jenna Reinen, Pablo Polosecki, Irina Rish, and Guillermo Cecchi "Predicting conversion to psychosis in clinical high risk patients using resting-state functional MRI features", Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 109532A (15 March 2019); https://doi.org/10.1117/12.2525341

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