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

Abstract

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.

Figure 1.. Global Analysis of the Imaging and Nonimaging Data Sets

A, Scatterplot of the imaging and nonimaging variates. The overall correlation was r = 0.63 (P < .001). Correlations were similar across groups (r = 0.54 for BD, r = 0.64 for SCZ, and r = 0.66 for HV). B, Correlation of nonimaging variables with the imaging variate. The y-axis shows the r value of each imaging variable with the global imaging variate. BD indicates patients with bipolar disorder; HV, healthy volunteers; and SCZ, patients with schizophrenia.

Figure 2.. Results of the Modular Sparse Canonical Correlation Analyses

A, Top: Regional cortical thickness measures correlated most highly with nonimaging variate. Bottom: Correlations between nonimaging variables and cortical thickness variate. B, Top: Subcortical volumetric measures correlated most highly with nonimaging variate. Bottom: Correlations between nonimaging variables and subcortical volumes variate. C, Top: Regional task-related brain activation correlated most highly with nonimaging variate. Bottom: Correlations between nonimaging variables and task-related brain activation variate. D, Top: Regional fractional anisotropy measures correlated most highly with nonimaging variate. Bottom: Correlations between nonimaging variables and fractional anisotropy variate.