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Brain Functional Networks in Type 2 Diabetes Mellitus Patients: A Resting-State Functional MRI Study

BACKGROUND: Previous diabetes mellitus studies of cognitive impairments in the early stages have focused on changes in brain structure and function, and more recently the focus has shifted to the relationships between encephalic regions and diversification of network topology. However, studies exami...

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Autores principales: Xu, Jian, Chen, Fuqin, Liu, Taiyuan, Wang, Ting, Zhang, Junran, Yuan, Huijuan, Wang, Meiyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6433793/
https://www.ncbi.nlm.nih.gov/pubmed/30941007
http://dx.doi.org/10.3389/fnins.2019.00239
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author Xu, Jian
Chen, Fuqin
Liu, Taiyuan
Wang, Ting
Zhang, Junran
Yuan, Huijuan
Wang, Meiyun
author_facet Xu, Jian
Chen, Fuqin
Liu, Taiyuan
Wang, Ting
Zhang, Junran
Yuan, Huijuan
Wang, Meiyun
author_sort Xu, Jian
collection PubMed
description BACKGROUND: Previous diabetes mellitus studies of cognitive impairments in the early stages have focused on changes in brain structure and function, and more recently the focus has shifted to the relationships between encephalic regions and diversification of network topology. However, studies examining network topology in diabetic brain function are still limited. METHODS: The study included 102 subjects; 55 type 2 diabetes mellitus (T2DM) patients plus 47 healthy controls. All subjects were examined by resting-state functional magnetic resonance imaging (rs-fMRI) scan. According to Automated Anatomical Labeling, the brain was divided into 90 anatomical regions, and every region corresponds to a brain network analysis node. The whole brain functional network was constructed by thresholding the correlation matrices of the 90 brain regions, and the topological properties of the network were computed based on graph theory. Then, the topological properties of the network were compared between different groups by using a non-parametric test. Finally, the associations between differences in topological properties and the clinical indicators were analyzed. RESULTS: The brain functional networks of both T2DM patients and healthy controls were found to possess small-world characteristics, i.e., normalized clustering coefficient (γ) > 1, and normalized characteristic path length (λ) close to 1. No significant differences were found in the small-world characteristics (σ). Second, the T2DM patient group displayed significant differences in node properties in certain brain regions. Correlative analytic results showed that the node degree of the right inferior temporal gyrus (ITG) and the node efficiencies of the right ITG and superior temporal gyrus of T2DM patients were positively correlated with body mass index. CONCLUSION: The brain network of T2DM patients has the same small-world characteristics as normal people, but the normalized clustering coefficient is higher and the normalized characteristic path length is lower than that of the normal control group, indicating that the brain function network of the T2DM patients has changed. The changes of node properties were mostly concentrated in frontal lobe, temporal lobe and posterior cingulate gyrus. The abnormal changes in these indices in T2DM patients might be explained as a compensatory behavior to reduce cognitive impairments, which is achieved by mobilizing additional neural resources, such as the excessive activation of the network and the efficient networking of multiple brain regions.
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spelling pubmed-64337932019-04-02 Brain Functional Networks in Type 2 Diabetes Mellitus Patients: A Resting-State Functional MRI Study Xu, Jian Chen, Fuqin Liu, Taiyuan Wang, Ting Zhang, Junran Yuan, Huijuan Wang, Meiyun Front Neurosci Neuroscience BACKGROUND: Previous diabetes mellitus studies of cognitive impairments in the early stages have focused on changes in brain structure and function, and more recently the focus has shifted to the relationships between encephalic regions and diversification of network topology. However, studies examining network topology in diabetic brain function are still limited. METHODS: The study included 102 subjects; 55 type 2 diabetes mellitus (T2DM) patients plus 47 healthy controls. All subjects were examined by resting-state functional magnetic resonance imaging (rs-fMRI) scan. According to Automated Anatomical Labeling, the brain was divided into 90 anatomical regions, and every region corresponds to a brain network analysis node. The whole brain functional network was constructed by thresholding the correlation matrices of the 90 brain regions, and the topological properties of the network were computed based on graph theory. Then, the topological properties of the network were compared between different groups by using a non-parametric test. Finally, the associations between differences in topological properties and the clinical indicators were analyzed. RESULTS: The brain functional networks of both T2DM patients and healthy controls were found to possess small-world characteristics, i.e., normalized clustering coefficient (γ) > 1, and normalized characteristic path length (λ) close to 1. No significant differences were found in the small-world characteristics (σ). Second, the T2DM patient group displayed significant differences in node properties in certain brain regions. Correlative analytic results showed that the node degree of the right inferior temporal gyrus (ITG) and the node efficiencies of the right ITG and superior temporal gyrus of T2DM patients were positively correlated with body mass index. CONCLUSION: The brain network of T2DM patients has the same small-world characteristics as normal people, but the normalized clustering coefficient is higher and the normalized characteristic path length is lower than that of the normal control group, indicating that the brain function network of the T2DM patients has changed. The changes of node properties were mostly concentrated in frontal lobe, temporal lobe and posterior cingulate gyrus. The abnormal changes in these indices in T2DM patients might be explained as a compensatory behavior to reduce cognitive impairments, which is achieved by mobilizing additional neural resources, such as the excessive activation of the network and the efficient networking of multiple brain regions. Frontiers Media S.A. 2019-03-19 /pmc/articles/PMC6433793/ /pubmed/30941007 http://dx.doi.org/10.3389/fnins.2019.00239 Text en Copyright © 2019 Xu, Chen, Liu, Wang, Zhang, Yuan and Wang. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Xu, Jian
Chen, Fuqin
Liu, Taiyuan
Wang, Ting
Zhang, Junran
Yuan, Huijuan
Wang, Meiyun
Brain Functional Networks in Type 2 Diabetes Mellitus Patients: A Resting-State Functional MRI Study
title Brain Functional Networks in Type 2 Diabetes Mellitus Patients: A Resting-State Functional MRI Study
title_full Brain Functional Networks in Type 2 Diabetes Mellitus Patients: A Resting-State Functional MRI Study
title_fullStr Brain Functional Networks in Type 2 Diabetes Mellitus Patients: A Resting-State Functional MRI Study
title_full_unstemmed Brain Functional Networks in Type 2 Diabetes Mellitus Patients: A Resting-State Functional MRI Study
title_short Brain Functional Networks in Type 2 Diabetes Mellitus Patients: A Resting-State Functional MRI Study
title_sort brain functional networks in type 2 diabetes mellitus patients: a resting-state functional mri study
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6433793/
https://www.ncbi.nlm.nih.gov/pubmed/30941007
http://dx.doi.org/10.3389/fnins.2019.00239
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