Brain structural networks and connectomes: the brain–obesity interface and its impact on mental health

Brain structural networks and connectomes: the brain–obesity interface and its impact on mental health

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 Ima...

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Autores principales: Chen VCH, Liu YC, Chao SH, McIntyre RS, Cha DS, Lee Y, Weng JC
Formato: article
Lenguaje:EN
Publicado: Dove Medical Press 2018
Materias:
obesity
diffusion tensor imaging (DTI)
generalized q-sampling imaging (GQI)
graph theoretical analysis (GTA)
network-based statistical (NBS) analysis
BMI and HADS scores
Neurosciences. Biological psychiatry. Neuropsychiatry
RC321-571
Neurology. Diseases of the nervous system
RC346-429
Acceso en línea:https://doaj.org/article/d445b587df344019b9d0a0723e4f6ce4
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spelling oai:doaj.org-article:d445b587df344019b9d0a0723e4f6ce42021-12-02T03:24:19ZBrain structural networks and connectomes: the brain–obesity interface and its impact on mental health1178-2021https://doaj.org/article/d445b587df344019b9d0a0723e4f6ce42018-11-01T00:00:00Zhttps://www.dovepress.com/brain-structural-networks-and-connectomes-the-brain-obesity-interface--peer-reviewed-article-NDThttps://doaj.org/toc/1178-2021Vincent 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 of Metabolic and Bariatric Surgery, Jen-Ai Hospital, Taichung, Taiwan; 5Mood Disorder Psychopharmacology Unit, University Health Network, Department of Psychiatry, University of Toronto, ON, Canada; 6Institute of Medical Science, 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 Purpose: Obesity is a complex and multifactorial disease identified as a global epidemic. Convergent evidence indicates that obesity differentially influences patients with neuropsychiatric disorders providing a basis for hypothesizing that obesity alters brain structure and function associated with the brain’s propensity toward disturbances in mood and cognition. Herein, we characterize alterations in brain structures and networks among obese subjects (ie, body mass index [BMI] ≥30 kg/m2) when compared with non-obese controls.Patients and methods: We obtained noninvasive diffusion tensor imaging and generalized q-sampling imaging scans of 20 obese subjects (BMI=37.9±5.2 SD) and 30 non-obese controls (BMI=22.6±3.4 SD). Graph theoretical analysis and network-based statistical analysis were performed to assess structural and functional differences between groups. We additionally assessed for correlations between diffusion indices, BMI, and anxiety and depressive symptom severity (ie, Hospital Anxiety and Depression Scale total score).Results: The diffusion indices of the posterior limb of the internal capsule, corona radiata, and superior longitudinal fasciculus were significantly lower among obese subjects when compared with controls. Moreover, obese subjects were more likely to report anxiety and depressive symptoms. There were fewer structural network connections observed in obese subjects compared with non-obese controls. Topological measures of clustering coefficient (C), local efficiency (Elocal), global efficiency (Eglobal), and transitivity were significantly lower among obese subjects. Similarly, three sub-networks were identified to have decreased structural connectivity among frontal–temporal regions in obese subjects compared with non-obese controls.Conclusion: We extend knowledge further by delineating structural interconnectivity alterations within and across brain regions that are adversely affected in individuals who are obese. Keywords: obesity, diffusion tensor imaging, DTI, generalized q-sampling imaging, GQI, graph theoretical analysis, GTA, network-based statistical analysis, NBSChen VCHLiu YCChao SHMcIntyre RSCha DSLee YWeng JCDove Medical Pressarticleobesitydiffusion tensor imaging (DTI)generalized q-sampling imaging (GQI)graph theoretical analysis (GTA)network-based statistical (NBS) analysisBMI and HADS scoresNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571Neurology. Diseases of the nervous systemRC346-429ENNeuropsychiatric Disease and Treatment, Vol Volume 14, Pp 3199-3208 (2018)
institution DOAJ
collection DOAJ
language EN
topic obesity
diffusion tensor imaging (DTI)
generalized q-sampling imaging (GQI)
graph theoretical analysis (GTA)
network-based statistical (NBS) analysis
BMI and HADS scores
Neurosciences. Biological psychiatry. Neuropsychiatry
RC321-571
Neurology. Diseases of the nervous system
RC346-429
spellingShingle obesity
diffusion tensor imaging (DTI)
generalized q-sampling imaging (GQI)
graph theoretical analysis (GTA)
network-based statistical (NBS) analysis
BMI and HADS scores
Neurosciences. Biological psychiatry. Neuropsychiatry
RC321-571
Neurology. Diseases of the nervous system
RC346-429
Chen VCH
Liu YC
Chao SH
McIntyre RS
Cha DS
Lee Y
Weng JC
Brain structural networks and connectomes: the brain–obesity interface and its impact on mental health
description 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 of Metabolic and Bariatric Surgery, Jen-Ai Hospital, Taichung, Taiwan; 5Mood Disorder Psychopharmacology Unit, University Health Network, Department of Psychiatry, University of Toronto, ON, Canada; 6Institute of Medical Science, 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 Purpose: Obesity is a complex and multifactorial disease identified as a global epidemic. Convergent evidence indicates that obesity differentially influences patients with neuropsychiatric disorders providing a basis for hypothesizing that obesity alters brain structure and function associated with the brain’s propensity toward disturbances in mood and cognition. Herein, we characterize alterations in brain structures and networks among obese subjects (ie, body mass index [BMI] ≥30 kg/m2) when compared with non-obese controls.Patients and methods: We obtained noninvasive diffusion tensor imaging and generalized q-sampling imaging scans of 20 obese subjects (BMI=37.9±5.2 SD) and 30 non-obese controls (BMI=22.6±3.4 SD). Graph theoretical analysis and network-based statistical analysis were performed to assess structural and functional differences between groups. We additionally assessed for correlations between diffusion indices, BMI, and anxiety and depressive symptom severity (ie, Hospital Anxiety and Depression Scale total score).Results: The diffusion indices of the posterior limb of the internal capsule, corona radiata, and superior longitudinal fasciculus were significantly lower among obese subjects when compared with controls. Moreover, obese subjects were more likely to report anxiety and depressive symptoms. There were fewer structural network connections observed in obese subjects compared with non-obese controls. Topological measures of clustering coefficient (C), local efficiency (Elocal), global efficiency (Eglobal), and transitivity were significantly lower among obese subjects. Similarly, three sub-networks were identified to have decreased structural connectivity among frontal–temporal regions in obese subjects compared with non-obese controls.Conclusion: We extend knowledge further by delineating structural interconnectivity alterations within and across brain regions that are adversely affected in individuals who are obese. Keywords: obesity, diffusion tensor imaging, DTI, generalized q-sampling imaging, GQI, graph theoretical analysis, GTA, network-based statistical analysis, NBS
format article
author Chen VCH
Liu YC
Chao SH
McIntyre RS
Cha DS
Lee Y
Weng JC
author_facet Chen VCH
Liu YC
Chao SH
McIntyre RS
Cha DS
Lee Y
Weng JC
author_sort Chen VCH
title Brain structural networks and connectomes: the brain–obesity interface and its impact on mental health
title_short Brain structural networks and connectomes: the brain–obesity interface and its impact on mental health
title_full Brain structural networks and connectomes: the brain–obesity interface and its impact on mental health
title_fullStr Brain structural networks and connectomes: the brain–obesity interface and its impact on mental health
title_full_unstemmed Brain structural networks and connectomes: the brain–obesity interface and its impact on mental health
title_sort brain structural networks and connectomes: the brain–obesity interface and its impact on mental health
publisher Dove Medical Press
publishDate 2018
url https://doaj.org/article/d445b587df344019b9d0a0723e4f6ce4
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