Vincent Chin-Hung Chen,1,2 Yi-Chun Liu,3 Seh-Huang Chao,4 Roger S MacIntyre,5-7 Danielle S Cha,5.8 Yena Lee,5.6 Jun-Cheng Weng2.9
1Medicine School, Chang Gung University, Taoyuan, Taiwan; 2Department of Illness, Chang Gung Memorial Hospital, Chiayi, Taiwan; 3Department of Medical Instruments and Radiological Sciences, Medical University of Chung Shan, Taichung, Taiwan; 4Metabolic and Bariatric Surgery Center, Jen-Ai Hospital, Taichung, Taiwan; 5Mood Disorder Psychology Unit, University Health Network, Department of Psychiatry, University of Toronto, ON, Canada; 6Institute of Medical Science, University of Toronto, Toronto, ON, Canada; 7Department of Psychotherapy and the Cure, University of Toronto, Toronto, ON, Canada; 8Medicine School, Queensland University, Queensland, Brisbane, Australia; 9Department of Medical Instruments and Radio Sciences, Chang Gung University, Taoyuan, Taiwan
Reason: Obesity is a complex and multi-functional illness that is known as a global problem. Comprehensive evidence shows that obesity is evident. Differently affecting patients with neuropsychiatric disorders that provide a base for the & # 39; To think that obesity changes the brain structure and work associated with the brain despite worrying in enjoyment and knowledge. Here, we identify changes in brain structures and networks among rough topics (ie, large body indexes [BMI] ≥30 kg / m2) compared to inaccurate controls.
Patients and ways: We received non-invasive separation tensor imaging and extensive q-sampling imagery scans of 20 obesumes (BMI = 37.9 ± 5.2 SD) and 30 non-working controls (BMI = 22.6 ± 3.4 SD). Graph theory and network-based statistical analysis was carried out to evaluate structural and organizational differences between groups. In addition, we evaluated equalities between explosives, BMI, and anxiety and depression of epilepsy symptoms (eg, full time of Hospital Time and Depreciation Scale).
Results: India was different from the posterior capillary members inside, radiated corona, and better broader, much lower in fatty subjects compared to controls. In addition, obesity subjects were more likely to mention concerns and bad signs. Less structural network connections were inspected in obesity compared to inverse controls. Basic measurements of probability (C) efficiency, local efficiency (Elocal), global efficiency (Eworld), and there was a much lower diversity among fatty subjects. In the same way, three sub-networks were identified to reduce structural connectivity among premature-time departments in obesity compared to non-working controls.
Decision: We will expand additional knowledge by & # 39; Designing structural changes in interconnections inside and across brain divisions that adversely affect individuals who are overweight.
Keywords: horrors, translation tensor images, DTI, general Q-sampling images, GQI, graph theory analysis, GTA, network-based statistical analysis, NBS
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