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Original Investigation
April 2018

Multivariate Associations Among Behavioral, Clinical, and Multimodal Imaging Phenotypes in Patients With Psychosis

Author Affiliations
  • 1Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
  • 2Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
  • 3Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, California
  • 4Center for Neuroscience in Women’s Health, Stanford University, Palo Alto, California
JAMA Psychiatry. 2018;75(4):386-395. doi:10.1001/jamapsychiatry.2017.4741
Key Points

Question  What is the contribution of nonimaging clinical, behavioral, lifestyle, and cardiometabolic factors to multimodal neuroimaging phenotypes in psychosis?

Findings  In this imaging study of 92 patients with schizophrenia, 37 patients with bipolar disorder, and 48 healthy volunteers, nonimaging and imaging data have substantial covariation. General intelligence, body mass index, positive psychotic symptoms, substance use, and antipsychotic medication were major contributors to variation in brain structure and function.

Meaning  The results of this study underscore the importance of including these key factors in future research to derive a more accurate characterization of brain alterations associated with psychosis.


Importance  Alterations in multiple neuroimaging phenotypes have been reported in psychotic disorders. However, neuroimaging measures can be influenced by factors that are not directly related to psychosis and may confound the interpretation of case-control differences. Therefore, a detailed characterization of the contribution of these factors to neuroimaging phenotypes in psychosis is warranted.

Objective  To quantify the association between neuroimaging measures and behavioral, health, and demographic variables in psychosis using an integrated multivariate approach.

Design, Setting, and Participants  This imaging study was conducted at a university research hospital from June 26, 2014, to March 9, 2017. High-resolution multimodal magnetic resonance imaging data were obtained from 100 patients with schizophrenia, 40 patients with bipolar disorder, and 50 healthy volunteers; computed were cortical thickness, subcortical volumes, white matter fractional anisotropy, task-related brain activation (during working memory and emotional recognition), and resting-state functional connectivity. Ascertained in all participants were nonimaging measures pertaining to clinical features, cognition, substance use, psychological trauma, physical activity, and body mass index. The association between imaging and nonimaging measures was modeled using sparse canonical correlation analysis with robust reliability testing.

Main Outcomes and Measures  Multivariate patterns of the association between nonimaging and neuroimaging measures in patients with psychosis and healthy volunteers.

Results  The analyses were performed in 92 patients with schizophrenia (23 female [25.0%]; mean [SD] age, 27.0 [7.6] years), 37 patients with bipolar disorder (12 female [32.4%]; mean [SD] age, 27.5 [8.1] years), and 48 healthy volunteers (20 female [41.7%]; mean [SD] age, 29.8 [8.5] years). The imaging and nonimaging data sets showed significant covariation (r = 0.63, P < .001), which was independent of diagnosis. Among the nonimaging variables examined, age (r = −0.53), IQ (r = 0.36), and body mass index (r = −0.25) were associated with multiple imaging phenotypes; cannabis use (r = 0.23) and other substance use (r = 0.33) were associated with subcortical volumes, and alcohol use was associated with white matter integrity (r = −0.15). Within the multivariate models, positive symptoms retained associations with the global neuroimaging (r = −0.13), the cortical thickness (r = −0.22), and the task-related activation variates (r = −0.18); negative symptoms were mostly associated with measures of subcortical volume (r = 0.23), and depression/anxiety was associated with measures of white matter integrity (r = 0.12).

Conclusions and Relevance  Multivariate analyses provide a more accurate characterization of the association between brain alterations and psychosis because they enable the modeling of other key factors that influence neuroimaging phenotypes.