Schizophrenia is a neuropsychiatric illness with substantial individual variability. The heterogeneity spans most aspects of the illness: genetics, environmental risk factors, age at onset, symptoms, treatment response, and long-term prognosis. The causative mechanisms of these heterogeneities have remained elusive from Bleuler’s definition of the disorder1 to modern clinical and imaging studies. While variability may be because of distinct clinical or neurobiological subtypes, efforts to confirm this hypothesis have not yet succeeded. In fact, heterogeneity has prevented reproducible research on the effects of candidate genes and clinical, neuroanatomical, and functional findings. As a result, some call to retire the terms schizophrenia or schizoaffective disorder in favor of psychosis spectrum or psychosis syndrome, arguing that the former diagnoses incorrectly imply discrete illnesses.2,3 Recent big data studies, such as those conducted by Alnæs et al4 in this issue of JAMA Psychiatry, provide a new way of examining individual heterogeneity and reproducibility of observations associated with this illness.
Kochunov P, Thompson PM, Hong LE. Toward High Reproducibility and Accountable Heterogeneity in Schizophrenia Research. JAMA Psychiatry. 2019;76(7):680–681. doi:10.1001/jamapsychiatry.2019.0208
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