Vincent Chin-Hoon Chen,1.2 Yi-Chun Liu,3 Cech-Huang Chao,4 Roger With McIntyre,5-7 Daniel C Cha,5.8 Jena Lee,5,6 Jun Cheng Weng2.9
1Medical High School, Chan Gong University, Taoyuan, Taiwan; 2Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi, Taiwan; 3Department of Medical Imaging and Radiological Sciences, Chun Shan Medical University, Taichun, Taiwan; 4Center for Metabolic and Barrictive Surgery, Jen-Aye Hospital, Taichun, Taiwan; 5Psychopharmacological Mood Disorder, University Health Network, Department of Psychiatry, University of Toronto, ON, Canada; 6Institute of Medicine, University of Toronto, Toronto, ON, Canada; 7Department of Psychiatry and Pharmacology, University of Toronto, Toronto, ON, Canada; 8High School of Medicine, University of Queensland, Queensland, Brisbane, Australia; 9Department of Medical Imaging and Radiology, Chong Gong University, Taoyuan, Taiwan
Purpose: Obesity is a complex and multifactorial disease, defined as a global epidemic. Convergence data show that obesity has a differential effect on patients with neuropsychiatric disorders, providing the basis for the hypothesis that obesity alters the brain structure and function associated with brain tendency to mood and cognitive impairment. Here we characterize changes in brain structures and networks among obese subjects (ie, body mass index [BMI] ≥ 30 kg / m2) compared to non-obese controls.
Patients and methods: Non-invasive diffusion tones and aggregated scan images of 20 obese (BMI = 37.9 ± 5.2 SD) and 30 non-obese controls (BMI = 22.6 ± 3.4 SD) were obtained. Chart theoretical analysis and network based statistical analysis were performed to assess the structural and functional differences between the groups. In addition, we evaluated the correlations between diffusion indices, body weight and anxiety, and the severity of depressive symptoms (ie a general anxiety score and depression).
Results: The diffusion indices of the back end of the inner capsule, the crown of the radius and the better longitudinal fascicle were significantly lower among the obese subjects than the controls. In addition, obesity was more likely to report anxiety and depressive symptoms. There are fewer structural network connections seen in obese patients than non-obese controls. Topological measures for clustering factor (C), local efficiency (E.local), global efficiency (E.worldwide) and transitivity are significantly lower among obese subjects. Similarly, three sub-networks have been identified that have reduced structural connectivity between frontal-temporal areas in obese individuals compared to non-obese controls.
Conclusion: We extend our knowledge further by outlining structural changes in interconnection in and in brain regions that are adversely affected in obese individuals.
Keywords: obesity, diffusion tint image, DTI, generalized imaging of samples q, GQI, graphic theoretical analysis, GTA, network statistical analysis, NBS
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