Tuesday , June 28 2022

Structural networks and brain connectors: the question of brain obesity



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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

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

Scope: Obesity is a complex and multifactorial disease identified as a global epidemic. Converging evidence indicates that obesity influences differentiated patients with neuropsychiatric disorders providing a basis for the hypothesis that obesity alters brain structure and function associated with brain propensity to mood and cognition disorders. In this case, we characterize changes in cerebral structures and networks among obese subjects (ie the body mass index [BMI] ≥ 30 kg / m2) compared to non-obese controls.
Patients and methods: We obtained imaging images of non-invasive tensor diffusion and generalized imaging scans from 20 obese subjects (IMC = 37.9 ± 5.2 SD) and 30 non-obese controls (IMC = 22.6 ± 3.4 SD). Graphical theoretical analyzes and network based statistical analyzes were performed to assess the structural and functional differences between groups. We further evaluated the correlations between diffusion indices, BMI and anxiety and the severity of depressive symptoms (ie the overall score of hospital anxiety and depression).
Results: The diffusion index of the posterior limbs of the internal capsule, the radius corona and the upper longitudinal beam were significantly lower among obese subjects compared to the controls. In addition, obese subjects were more likely to report anxiety and depressive symptoms. There were fewer structural network connections observed in obese subjects compared to non-obese controls. Topological measures of grouping coefficient (C), local efficiency (Elocal), overall efficiency (Eoverall), and transitivity was significantly lower among obese subjects. Similarly, three sub-networks have been identified that reduced structural connectivity between the frontal-temporal regions in obese subjects compared to non-obese controls.
Conclusion: We extend our knowledge further by delimiting structural changes in interconnectivity within and between brain regions that are adversely affected in individuals who are obese.

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

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