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
1School of Medicine, Chang Gung University, Taoyuan, Taiwan; 2Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi, Taiwan; 3Department of Medical Imaging and Radiology, Chung Shan Medical University, Taichung, Taiwan; 4Center for Metabolic and Bariatric Surgery, Jen-Ai Hospital, Taichung, Taiwan; 5Mood Disorder Psychopharmacology Unit, University Health Network, Department of Psychiatry, University of Toronto, ON, Canada; 6Medical Institute, University of Toronto, Toronto, ON, Canada; 7Department of Psychiatry and Pharmacology at the University of Turin, Toronto, ON, Canada; 8Bachelor of Medicine, Queensland University, Queensland, Brisbane, Australia; 9Department of Medical Imaging and Radiology, Chang Gung University, Taoyuan, Taiwan
Purpose: Obesity is a complex and complex disease known as a global epidemic. Convergent evidence suggests that obesity has a different effect on patients with neuropsychiatric disorders that form the basis for hypothesis that obesity changes the brain's structure and activity associated with brain tendency to mood disorders and cognition. Here, we characterize changes in brain structures and networks between obesity (ie body mass index) [BMI] ≥30 kg / m2) when comparing non-obese controls.
Patients and Methods: A non-invasive diffusion oscillation imaging and general q-sample analysis of 20-bit (BMI = 37.9 ± 5.2 SD) and 30 non-fat controls (BMI = 22.6 ± 3.4 SD) were obtained. Graphical theoretical analysis and web-based statistical analysis were performed to assess the structural and functional differences of the groups. In addition, we evaluated the correlations between diffusion indices, BMI and anxiety and the severity of the symptoms of depression (eg, total illness and depression scale).
score: The diffusion indexes of the posterior branch of the inner capsule, the Korona radiata and the excellent longitudinal fasciculus were significantly lower than the obese subjects compared to the controls. In addition, overweight is more likely to cause anxiety and depressive symptoms. Less structured network connections were found in obesity products than obese controls. The clustering factor (C), the local efficiency (Elocal), global efficiency (Eglobal) and the throughput was considerably lower in obesity. Similarly, three subnets were found to reduce the structural links between relieved individuals in the front and the temporal areas compared to non-obese controls.
conclusion: We are expanding knowledge by increasing structural interconnectivity within and across brain regions that are harmful to individuals who are obese.
Keywords: obesity, diffusion-tensorimaging, DTI, general q sample sampling, GQI, graph theoretical analysis, GTA, web-based statistical analysis, NBS
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