Proc. SPIE. 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging
KEYWORDS: Data modeling, Magnetic resonance imaging, Control systems, Feature extraction, Machine learning, Data conversion, Statistical modeling, Functional magnetic resonance imaging, Brain imaging, Brain
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.