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Aberrant brain functional networks in type 2 diabetes mellitus: A graph theoretical and support-vector machine approach
OBJECTIVE: Type 2 diabetes mellitus (T2DM) is a high risk of cognitive decline and dementia, but the underlying mechanisms are not yet clearly understood. This study aimed to explore the functional connectivity (FC) and topological properties among whole brain networks and correlations with impaired...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9597867/ https://www.ncbi.nlm.nih.gov/pubmed/36310847 http://dx.doi.org/10.3389/fnhum.2022.974094 |
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author | Lin, Lin Zhang, Jindi Liu, Yutong Hao, Xinyu Shen, Jing Yu, Yang Xu, Huashuai Cong, Fengyu Li, Huanjie Wu, Jianlin |
author_facet | Lin, Lin Zhang, Jindi Liu, Yutong Hao, Xinyu Shen, Jing Yu, Yang Xu, Huashuai Cong, Fengyu Li, Huanjie Wu, Jianlin |
author_sort | Lin, Lin |
collection | PubMed |
description | OBJECTIVE: Type 2 diabetes mellitus (T2DM) is a high risk of cognitive decline and dementia, but the underlying mechanisms are not yet clearly understood. This study aimed to explore the functional connectivity (FC) and topological properties among whole brain networks and correlations with impaired cognition and distinguish T2DM from healthy controls (HC) to identify potential biomarkers for cognition abnormalities. METHODS: A total of 80 T2DM and 55 well-matched HC were recruited in this study. Subjects’ clinical data, neuropsychological tests and resting-state functional magnetic resonance imaging data were acquired. Whole-brain network FC were mapped, the topological characteristics were analyzed using a graph-theoretic approach, the FC and topological characteristics of the network were compared between T2DM and HC using a general linear model, and correlations between networks and clinical and cognitive characteristics were identified. The support vector machine (SVM) model was used to identify differences between T2DM and HC. RESULTS: In patients with T2DM, FC was higher in two core regions [precuneus/posterior cingulated cortex (PCC)_1 and later prefrontal cortex_1] in the default mode network and lower in bilateral superior parietal lobes (within dorsal attention network), and decreased between the right medial frontal cortex and left auditory cortex. The FC of the right frontal medial-left auditory cortex was positively correlated with the Montreal Cognitive Assessment scales and negatively correlated with the blood glucose levels. Long-range connectivity between bilateral auditory cortex was missing in the T2DM. The nodal degree centrality and efficiency of PCC were higher in T2DM than in HC (P < 0.005). The nodal degree centrality in the PCC in the SVM model was 97.56% accurate in distinguishing T2DM patients from HC, demonstrating the reliability of the prediction model. CONCLUSION: Functional abnormalities in the auditory cortex in T2DM may be related to cognitive impairment, such as memory and attention, and nodal degree centrality in the PCC might serve as a potential neuroimaging biomarker to predict and identify T2DM. |
format | Online Article Text |
id | pubmed-9597867 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95978672022-10-27 Aberrant brain functional networks in type 2 diabetes mellitus: A graph theoretical and support-vector machine approach Lin, Lin Zhang, Jindi Liu, Yutong Hao, Xinyu Shen, Jing Yu, Yang Xu, Huashuai Cong, Fengyu Li, Huanjie Wu, Jianlin Front Hum Neurosci Human Neuroscience OBJECTIVE: Type 2 diabetes mellitus (T2DM) is a high risk of cognitive decline and dementia, but the underlying mechanisms are not yet clearly understood. This study aimed to explore the functional connectivity (FC) and topological properties among whole brain networks and correlations with impaired cognition and distinguish T2DM from healthy controls (HC) to identify potential biomarkers for cognition abnormalities. METHODS: A total of 80 T2DM and 55 well-matched HC were recruited in this study. Subjects’ clinical data, neuropsychological tests and resting-state functional magnetic resonance imaging data were acquired. Whole-brain network FC were mapped, the topological characteristics were analyzed using a graph-theoretic approach, the FC and topological characteristics of the network were compared between T2DM and HC using a general linear model, and correlations between networks and clinical and cognitive characteristics were identified. The support vector machine (SVM) model was used to identify differences between T2DM and HC. RESULTS: In patients with T2DM, FC was higher in two core regions [precuneus/posterior cingulated cortex (PCC)_1 and later prefrontal cortex_1] in the default mode network and lower in bilateral superior parietal lobes (within dorsal attention network), and decreased between the right medial frontal cortex and left auditory cortex. The FC of the right frontal medial-left auditory cortex was positively correlated with the Montreal Cognitive Assessment scales and negatively correlated with the blood glucose levels. Long-range connectivity between bilateral auditory cortex was missing in the T2DM. The nodal degree centrality and efficiency of PCC were higher in T2DM than in HC (P < 0.005). The nodal degree centrality in the PCC in the SVM model was 97.56% accurate in distinguishing T2DM patients from HC, demonstrating the reliability of the prediction model. CONCLUSION: Functional abnormalities in the auditory cortex in T2DM may be related to cognitive impairment, such as memory and attention, and nodal degree centrality in the PCC might serve as a potential neuroimaging biomarker to predict and identify T2DM. Frontiers Media S.A. 2022-10-12 /pmc/articles/PMC9597867/ /pubmed/36310847 http://dx.doi.org/10.3389/fnhum.2022.974094 Text en Copyright © 2022 Lin, Zhang, Liu, Hao, Shen, Yu, Xu, Cong, Li and Wu. https://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 | Human Neuroscience Lin, Lin Zhang, Jindi Liu, Yutong Hao, Xinyu Shen, Jing Yu, Yang Xu, Huashuai Cong, Fengyu Li, Huanjie Wu, Jianlin Aberrant brain functional networks in type 2 diabetes mellitus: A graph theoretical and support-vector machine approach |
title | Aberrant brain functional networks in type 2 diabetes mellitus: A graph theoretical and support-vector machine approach |
title_full | Aberrant brain functional networks in type 2 diabetes mellitus: A graph theoretical and support-vector machine approach |
title_fullStr | Aberrant brain functional networks in type 2 diabetes mellitus: A graph theoretical and support-vector machine approach |
title_full_unstemmed | Aberrant brain functional networks in type 2 diabetes mellitus: A graph theoretical and support-vector machine approach |
title_short | Aberrant brain functional networks in type 2 diabetes mellitus: A graph theoretical and support-vector machine approach |
title_sort | aberrant brain functional networks in type 2 diabetes mellitus: a graph theoretical and support-vector machine approach |
topic | Human Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9597867/ https://www.ncbi.nlm.nih.gov/pubmed/36310847 http://dx.doi.org/10.3389/fnhum.2022.974094 |
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