Brain structural networks and connectomes: the brain–obesity interface and its impact on mental health

Abstract

Purpose

Obesity is a complex and multifactorial disease identified as a global epidemic. Convergent evidence indicates that obesity differentially influences patients with neuropsychiatric disorders providing a basis for hypothesizing that obesity alters brain structure and function associated with the brain’s propensity toward disturbances in mood and cognition. Herein, we characterize alterations in brain structures and networks among obese subjects (ie, body mass index [BMI] ≥30 kg/m²) when compared with non-obese controls.

Patients and methods

We obtained noninvasive diffusion tensor imaging and generalized q-sampling imaging scans of 20 obese subjects (BMI=37.9±5.2 SD) and 30 non-obese controls (BMI=22.6±3.4 SD). Graph theoretical analysis and network-based statistical analysis were performed to assess structural and functional differences between groups. We additionally assessed for correlations between diffusion indices, BMI, and anxiety and depressive symptom severity (ie, Hospital Anxiety and Depression Scale total score).

Results

The diffusion indices of the posterior limb of the internal capsule, corona radiata, and superior longitudinal fasciculus were significantly lower among obese subjects when compared with controls. Moreover, obese subjects were more likely to report anxiety and depressive symptoms. There were fewer structural network connections observed in obese subjects compared with non-obese controls. Topological measures of clustering coefficient (C), local efficiency (E_local), global efficiency (E_global), and transitivity were significantly lower among obese subjects. Similarly, three sub-networks were identified to have decreased structural connectivity among frontal–temporal regions in obese subjects compared with non-obese controls.

Conclusion

We extend knowledge further by delineating structural interconnectivity alterations within and across brain regions that are adversely affected in individuals who are obese.

Keywords:

• obesity
• diffusion tensor imaging
• DTI
• generalized q-sampling imaging
• GQI
• graph theoretical analysis
• GTA
• network-based statistical analysis
• NBS

Acknowledgments

This study was supported by the research programs NSC103-2420-H-182A-001 and MOST106-2221-E-182-079, which were sponsored by the Ministry of Science and Technology, Taipei, Taiwan. The authors appreciate the full support from the Department of Radiology, Jen-Ai Hospital, Taichung, Taiwan, and the Health Information and Epidemiology Laboratory (CLRPG6G0041), Chang Gung Memorial Hospital, Chiayi, Taiwan. The authors would like to thank Hsiang-Ya Huang and I Ieng Chao for their assistance in experimental preparation.

Author contributions

VCHC: project idea, study design, manuscript revision; YCL: data analysis, writing article; SHC: data collection; RSM: manuscript revision; DSC: manuscript revision; YL: manuscript revision; JCW: project idea, study design, software development, data analysis, writing article, manuscript revision. All authors contributed to data analysis, drafting and revising the article, gave final approval of the version to be published, and agree to be accountable for all aspects of the work.

Disclosure

The authors report no conflicts of interest in this work.