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Brain structural networks and conjunctions: brain-obesity int



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Vincent Chin-Hung Chen,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

oneFaculty of Medicine, Chang Gung University, Taoyuan, Taiwan; 2ndDepartment of Psychiatry, Chang Gung Memorial Hospital, Chiayi, Taiwan; 3Department of Medical Imaging and Radiology, Chung Shan Medical University, Taichung, Taiwan; 4Metabolic and Bariatric Surgery Center, Jen-Ai Hospital, Taichung, Taiwan; 5Psychopharmacology Unit, University Health Network, Department of Psychiatry, University of Toronto, ON, Canada; 6Institute of Medical Sciences, 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 Radiology, Chang Gung University, Taoyuan, Taiwan

Goal: Obesity is a complex and multifactorial disease that is defined as a global epidemic. Convergent evidence suggests that obesity affects patients with neuropsychiatric disorders as a basis for the hypothesis that obesity changes the brain structure and function associated with brain tendency to mood and cognitive disorders. Here, we characterize changes in brain structures and networks between obese individuals (ie, body mass index [BMI] ≥30 kg / m2ndcompared with non-obese controls.
Patients and Methods: In our study, we obtained noninvasive diffusion tensor imaging and generalized q-sampling imaging and 30 non-obese control (BMI = 22.6 ± 3.4 SD) in 20 obese subjects (BMI = 37.9 ± 5.2 SD). Graphical analysis and network based statistical analyzes were performed to evaluate the structural and functional differences between the groups. In addition, we evaluated the correlation between diffusion indices, BMI, anxiety, and severity of depressive symptoms (ie Hospital Anxiety and Depression Scale total score).
Results: Diffusion indices of the posterior extremity of the inner capsule, corona radiata and superior longitudinal fascicle were significantly lower in obese patients compared to controls. In addition, obese individuals were more likely to report anxiety and depressive symptoms. There was less structural network connectivity in non-obese subjects compared to non-obese controls. Topological measurements of the clustering coefficient (C), local efficiency (E)localglobal efficiency (E)globaland obese people were significantly lower. Similarly, three subnets were found to have structural connectivity between frontal-temporal regions in obese individuals compared to nonobese controls.
Result: We further broaden the knowledge by identifying the structural interconnections between and within the brain regions that are negatively affected by obese individuals.

Keywords: obesity, diffusion tensor imaging, DTI, generalized q-sampling imaging, GQI, graphical analysis, GTA, network-based statistical analysis, NBS

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