Vincent Chin-Hung Chen,1.2 1.2 Yi-Chun Liu,3 Seh-Huang Chao,4 Roger S McIntyre,5-7 Danielle S Cha,5.8 Yena Lee,5.6 Jun-Cheng Weng2.9
1School of Medicine, Chang Gung University, Taoyuan, Taiwan; 2Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi, Taiwan; 3Department of Medical Imaging and Radiological Sciences, Chung Shan Medical University, Taichung, Taiwan; 4Center for Bariatric and Metabolic Surgery, Jen-Ai Hospital, Taichung, Taiwan; 5Unit of Psychopharmacology of Mood Disorder, University Health Network, Department of Psychiatry, University of Toronto, ON, Canada; 6Institute of Medical Science, University of Toronto, Toronto, ON, Canada; 7Departments of Psychiatry and Pharmacology, University of Toronto, Toronto, ON, Canada; 8School of Medicine, University of Queensland, Queensland, Brisbane, Australia; 9Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan
Goal: 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 the hypothesis that obesity alters the structure and function of the brain associated with brain propensity to mood and cognitive disorders. Here, we characterize changes in brain structures and networks among obese individuals (ie, body mass index [BMI] ≥30 kg / m2) when compared to non-obese controls.
Patients and methods: We obtained image by non-invasive diffusion tensor and generalized images of q sample of 20 obese individuals (BMI = 37.9 ± 5.2 SD) and 30 non-obese controls (BMI = 22.6 ± 3.4 SD). Theoretical analysis of graphs and network-based statistical analysis were performed to evaluate structural and functional differences between groups. In addition, we assessed the correlations between diffusion rates, BMI, and severity of depressive and anxiety symptoms (ie, Hospital Anxiety and Depression Scale score).
Results: The diffusion rates of the posterior limb of the internal capsule, radiata corona, and upper longitudinal fasciculus were significantly lower among obese individuals when compared to controls. In addition, obese individuals were more likely to report anxiety and depressive symptoms. There were fewer structural network connections observed in obese individuals compared to non-obese controls. Topological measures of aggregation coefficient (C), local efficiency (Elocal), overall efficiency (Eglobaland transitivity were significantly lower among obese subjects. Similarly, three subnets were identified to decrease structural connectivity between the frontotemporal regions in obese individuals compared to non-obese controls.
Conclusion: We extend the knowledge even further by outlining the structural changes in interconnectivity within and across regions of the brain that are adversely affected in individuals who are obese.
Key words: obesity, diffusion tensor image, DTI, generalized q-sample image, GQI, theoretical chart analysis, GTA, network-based statistical analysis, NBS
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