Big neuroimaging datasets comprising hundreds or even thousands of subjects are becoming widely available, thanks to major collaborative efforts across multiple imaging centers and groups. Mining and analyzing Big-Data is also becoming feasible, owing to increased computational power and new implementations of machine learning algorithms, which can learn from data and generate predictions. Big-Data studies bear exceptional promise in disentangling complex psychiatric illness, including psychosis, where imaging correlates are often subtle and difficult to reproduce. Large datasets, combined with novel machine learning algorithms have opened avenues for delineating subtypes, as well as for predicting biological and clinical outcomes, including psychosis conversion and drug response.
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