Neuroimaging Heterogeneity in Psychosis: Neurobiological Underpinnings and Opportunities for Prognostic and Therapeutic Innovation

• Published in: Biological psychiatry (1969) Vol. 88; no. 1; pp. 95 - 102
• Main Authors: Voineskos, Aristotle N., Jacobs, Grace R., Ameis, Stephanie H.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.07.2020
• Subjects: Bipolar Disorder; Brain–Behavior; Computation; Data Driven; Heterogeneity; Humans; Multivariate; Neuroimaging; Prognosis; Psychosis; Psychotic Disorders - diagnostic imaging; Psychotic Disorders - therapy; Schizophrenia; Schizophrenia - diagnostic imaging; Schizophrenia - therapy; Brain–Behavior; Neuroimaging; Psychosis; Heterogeneity; Computation; Schizophrenia; Data Driven; Multivariate
• ISSN: 0006-3223; 1873-2402
• DOI: 10.1016/j.biopsych.2019.09.004

Heterogeneity in symptom presentation, outcomes, and treatment response has long been problematic for researchers aiming to identify biological markers of schizophrenia or psychosis. However, there is increasing recognition that there may likely be no such general illness markers, which is consistent with the notion of a group of schizophrenia(s) that may have both shared and unique neurobiological pathways. Instead, strategies aiming to capitalize on or leverage such heterogeneity may help uncover neurobiological pathways that may then be used to stratify groups of patients for prognostic purposes or for therapeutic trials. A shift toward larger sample sizes with adequate statistical power to overcome small effect sizes and disentangle the shared variance among different brain-imaging or behavioral variables has become a priority for the field. In addition, recognition that two individuals with the same clinical diagnosis may be more different from each other (at brain, genetic, and behavioral levels) than from another individual in a different disorder or nonclinical control group—coupled with computational advances—has catapulted data-driven efforts forward. Emerging challenges for this new approach include longitudinal stability of new subgroups, demonstration of validity, and replicability. The “litmus test” will be whether computational approaches that are successfully identifying groups of patients who share features in common, more than current DSM diagnostic constructs, also provide better prognostic accuracy over time and in addition lead to enhancements in treatment response and outcomes. These are the factors that matter most to patients, families, providers, and payers.