Brain functional connectivity patterns associated with adiposity in schizophrenia.
Authors
Affiliations (6)
Affiliations (6)
- Department of Psychiatry, University of Colorado School of Medicine, Anschutz Medical Campus, Anschutz Health Sciences Building, 1890 N Revere Court, Aurora, CO 80045, USA; Department of Bioengineering, University of Colorado Denver, 12705 E Montview Blvd Suite 100, Aurora, CO 80045, USA.
- Department of Psychiatry, University of Colorado School of Medicine, Anschutz Medical Campus, Anschutz Health Sciences Building, 1890 N Revere Court, Aurora, CO 80045, USA; Research Service, Rocky Mountain Regional VA Medical Center, 1700 N Wheeling St, Aurora, CO 80045, USA.
- Division of Endocrinology, Diabetes and Metabolic Diseases, Department of Medicine, Medical University of South Carolina, Clinical Sciences Building, CSB 96 Jonathan Lucas Street, Charleston, SC 29425, USA.
- Department of Psychiatry, University of Colorado School of Medicine, Anschutz Medical Campus, Anschutz Health Sciences Building, 1890 N Revere Court, Aurora, CO 80045, USA.
- Department of Neurology, Virginia Commonwealth University, 1101 E Marshall Street, Richmond, VA 23298, USA.
- Department of Psychiatry, University of Colorado School of Medicine, Anschutz Medical Campus, Anschutz Health Sciences Building, 1890 N Revere Court, Aurora, CO 80045, USA; Research Service, Rocky Mountain Regional VA Medical Center, 1700 N Wheeling St, Aurora, CO 80045, USA. Electronic address: [email protected].
Abstract
Individuals with schizophrenia (SZ) experience significantly greater obesity rates compared to the general population. The underlying biological mechanisms, however, remain poorly understood. Although brain functional connectivity (FC) has been associated with obesity in the general population, its role in obesity in SZ is largely unexplored. As such, this study examined FC contributions to adiposity in participants with SZ (n = 46) and an adiposity-matched comparison group without SZ (n = 46) as they completed resting-state functional magnetic resonance imaging in fasted and fed states. A machine learning approach identified FC patterns associated with adiposity (percent body fat), with model performance evaluated on an independent test set. In both fasted and fed states, brain regions involved in reward and eating behaviors contributed to adiposity prediction in both groups. While fasted, predictive connections in SZ largely involved limbic and sensorimotor networks, whereas comparison group networks were more varied. In the fed state, SZ predictive features included visual, default mode, and executive control networks, while comparison group connections involved limbic, sensorimotor, salience, and executive control networks. Findings may suggest shared, as well as diagnosis- and state-specific, FC patterns associated with adiposity in SZ, which may help inform future development of obesity-related interventions in this high-risk population.