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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; 4Center for Metabolic and Bariatric Surgery, Jen-Ai Hospital, Taichung, Taiwan; 5Psychopharmacology Unit for Mood Disorders, University Health Network, Department of Psychiatry, University of Toronto, ON, Canada; 6Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada; 7Departments of Psychiatry and Pharmacology, University of Toronto, Toronto, ON, Canada; 8School of Medicine, University of Queensland, Queensland, Brisbane, Australia; 9Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan
Goal: Obesity is a complex and multifactorial disease identified as a global epidemic. Converging evidence suggests that obesity influences patients with neuropsychiatric disorders differently, suggesting that obesity alters brain structure and function associated with brain propensity for mood and mood disorders. cognition. Here, we characterize the alterations of brain structures and networks in obese subjects (ie, body mass index). [BMI] ≥30 kg / m2) compared to non-obese controls.
Patients and methods: We obtained non-invasive diffusion tensor imaging and generalized q-imaging of 20 obese subjects (BMI = 37.9 ± 5.2 SD) and 30 non-obese controls (BMI = 22.6 ± 3). , 4 standard deviation). A graphical theoretical analysis and a network-based statistical analysis were performed to evaluate the structural and functional differences between the groups. We also evaluated correlations between diffusion indices, BMI and severity of symptoms of anxiety and depression (total score of the anxiety and depression scale of the disease). 39; hospital).
Results: Diffusion indices of the posterior limb of the internal capsule, corona radiata, and upper longitudinal fascicle were significantly lower in obese subjects than in controls. In addition, obese subjects were more likely to report symptoms of anxiety and depression. There were fewer structural network connections observed in obese subjects than in non-obese controls. Topological measures of the clustering coefficient (C), local efficiency (Elocal), overall efficiency (Eglobal), and transitivity were significantly lower in obese subjects. Similarly, three sub-networks were identified as having decreased structural connectivity between frontotemporal regions in obese subjects compared to non-obese controls.
Conclusion: We deepen knowledge by delineating alterations in structural interconnectivity within and between brain regions that are negatively affected in obese individuals.
Keywords: obesity, diffusion tensor imaging, DTI, generalized Q-sampling imaging, IQQ, theoretical graph analysis, IAG, network-based statistical analysis, NBS
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