The field of schizophrenia research is at a crossroads. On the one hand, relatively little progress has been made in elucidating its fundamental nature or in developing more effective treatments, leading to increasing calls for the death of this construct1 and its immediate replacement by one of several proposed alternatives. On the other hand, there is no consensus about which of these alternatives should replace it as none of them have been found to better explain the set of facts associated with schizophrenia.2 Can we make transformational advances in our comprehension of human brain function and apply that understanding into a more accurate concept of schizophrenia? Multibillion-dollar research initiatives such as the US-based Brain Research through Advancing Innovative Neurotechnologies and the Europe-based Human Brain Project hope to revolutionize our appreciation of how the human brain works. A prerequisite to these efforts is the ability to integrate and analyze “big data,” enabled by the exponential increase in the capacity of computer systems to store and process data. This enhanced capability has spawned great excitement in the overlapping fields of computational psychiatry and network neuroscience,3 exemplified by the steep growth of scientific publications in the area. This trend is also reflected in the field of schizophrenia research;4 for example, the number of publications on machine learning (ML) in this Journal has increased from an average of 1/year between 2004 and 2013 to 9 in 2017 and 15 this year. The 5 articles on the topic in this issue of the Journal illustrate both the promise and challenges in the application of ML methods to the study of schizophrenia.5–9