Monday , June 21 2021

Brain structural networks and connectors: brain obesity int



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

1Medical School, Chang Gung University, Taoyuan, Taiwan; 2Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi, Taiwan; 3Medical Picture and Radiological Sciences Department, Chung Shan Medical University, Taichung, Taiwan; 4Metabolic Surgery and Bariatric Center, Jen-Ai Hospital, Taichung, Taiwan; 5Mood Disorder Psychopharmacology Unit, University of Health Networks, Psychiatry Department, Toronto University, ON, Canada; 6Institute of Scientific Medicine, University of Toronto, Toronto, ON, Canada; 7Department of Psychiatry and Pharmacology, Toronto University, Toronto, ON, Canada; 8Medical School, Queensland University, Queensland, Brisbane, Australia; 9Medical Image and Radiological Sciences Department, Chang Gung University, Taoyuan, Taiwan

goal: Obesity is a complex and multifaceted disease in the global epidemic. Convergence evidence causes obesity in people with neuropsychiatric disorders inequality, because obesity mixes the brain's structures and brain disorders in mood and cognition. In this sense, brain structures and networks are altered between obese patients (ie body mass index [BMI] ≥30 kg / m2), not compared to obesity controls.
Patients and Methods: We have obtained an image of non-invasive tensile size and we have analyzed 20 obesity issues (IME = 37.9 ± 5.2 SD) and 30 obesity controls (IME = 22.6 ± 3.4 SD). Theoretical graphical analysis and network-based statistical analysis were carried out to evaluate the structural and functional differences between groups. On the other hand, we evaluate the diffusion indices, BMI and anxiety and depression symptom severity (that is, Hospital Anxiety and General Depression).
Results: The body index indexes of the internal capsule, crown radiata, and long-term fiction significantly decreased in obesity compared to control. In addition, obesity issues were more severe to denounce anxiety and depression symptoms. There was less connection to the structure network observed in obesity compared to non-obesity controls. Topological measurements of coefficients classification (C), local efficiency (Elocal), global efficiency (Eglobal), and transitivity were significantly reduced in obesity. Similarly, structural subscriptions identified in three sub-networks declined in obesity in pedestrian-related areas, not compared to obesity controls.
Conclusion: It gives us greater insight into determining the interoperability of the brain regions in which the affected individuals with excessive obesity are affected.

Keywords: Obesity, diffusion voltage image, DTI, general sampling images, GQI, graphic theoretical analysis, GTA, network-based statistical analysis, NBS

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