Altered brain functional network connectivity and topology in type 2 diabetes mellitus
IntroductionType 2 diabetes mellitus (T2DM) accelerates brain aging and disrupts brain functional network connectivity, though the specific mechanisms remain unclear. This study aimed to investigate T2DM-driven alterations in brain functional network connectivity and topology.MethodsEighty-five T2DM...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2025.1472010/full |
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| author | Weiwei Ni Weiyin Vivian Liu Mingrui Li Shouchao Wei Xuanzi Xu Shutong Huang Lanhui Zhu Jieru Wang Fengling Wen Hailing Zhou |
| author_facet | Weiwei Ni Weiyin Vivian Liu Mingrui Li Shouchao Wei Xuanzi Xu Shutong Huang Lanhui Zhu Jieru Wang Fengling Wen Hailing Zhou |
| author_sort | Weiwei Ni |
| collection | DOAJ |
| description | IntroductionType 2 diabetes mellitus (T2DM) accelerates brain aging and disrupts brain functional network connectivity, though the specific mechanisms remain unclear. This study aimed to investigate T2DM-driven alterations in brain functional network connectivity and topology.MethodsEighty-five T2DM patients and 67 healthy controls (HCs) were included. All participants underwent clinical, neuropsychological, and laboratory tests, followed by MRI examinations, including resting-state functional magnetic resonance imaging (rs-fMRI) and three-dimensional high-resolution T1-weighted imaging (3D-T1WI) on a 3.0 T MRI scanner. Post-image preprocessing, brain functional networks were constructed using the Dosenbach atlas and analyzed with the DPABI-NET toolkit through graph theory.ResultsIn T2DM patients, functional connectivity within and between the default mode network (DMN), frontal parietal network (FPN), subcortical network (SCN), ventral attention network (VAN), somatosensory network (SMN), and visual network (VN) was significantly reduced compared to HCs. Conversely, two functional connections within the VN and between the DMN and SMN were significantly increased. Global network topology analysis showed an increased shortest path length and decreased clustering coefficient, global efficiency, and local efficiency in the T2DM group. MoCA scores were negatively correlated with the shortest path length and positively correlated with global and local efficiency in the T2DM group. Node network topology analysis indicated reduced clustering coefficient, degree centrality, eigenvector centrality, and nodal efficiency in multiple nodes in the T2DM group. MoCA scores positively correlated with clustering coefficient and nodal efficiency in the bilateral precentral gyrus in the T2DM group.DiscussionThis study demonstrated significant abnormalities in connectivity and topology of large-scale brain functional networks in T2DM patients. These findings suggest that brain functional network connectivity and topology could serve as imaging biomarkers, providing insights into the underlying neuropathological processes associated with T2DM-related cognitive impairment. |
| format | Article |
| id | doaj-art-b1be68636fa7431ba0c55b144b8be4bb |
| institution | Kabale University |
| issn | 1662-453X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj-art-b1be68636fa7431ba0c55b144b8be4bb2025-01-28T06:41:32ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2025-01-011910.3389/fnins.2025.14720101472010Altered brain functional network connectivity and topology in type 2 diabetes mellitusWeiwei Ni0Weiyin Vivian Liu1Mingrui Li2Shouchao Wei3Xuanzi Xu4Shutong Huang5Lanhui Zhu6Jieru Wang7Fengling Wen8Hailing Zhou9Physical Examination Centre, Central People's Hospital of Zhanjiang, Zhanjiang, ChinaMR Research, GE Healthcare, Beijing, ChinaDepartment of Magnetic Resonance Imaging, Zhanjiang First Hospital of Traditional Chinese Medicine, Zhanjiang, ChinaCentral People's Hospital of Zhanjiang, Zhanjiang Institute of Clinical Medicine, Zhanjiang, ChinaDepartment of Teaching and Training, Central People's Hospital of Zhanjiang, Zhanjiang, ChinaDepartment of Clinical Laboratory, Central People's Hospital of Zhanjiang, Zhanjiang, ChinaPhysical Examination Centre, Central People's Hospital of Zhanjiang, Zhanjiang, ChinaDepartment of Radiology, Central People's Hospital of Zhanjiang, Zhanjiang, ChinaDepartment of Radiology, Central People's Hospital of Zhanjiang, Zhanjiang, ChinaDepartment of Radiology, Central People's Hospital of Zhanjiang, Zhanjiang, ChinaIntroductionType 2 diabetes mellitus (T2DM) accelerates brain aging and disrupts brain functional network connectivity, though the specific mechanisms remain unclear. This study aimed to investigate T2DM-driven alterations in brain functional network connectivity and topology.MethodsEighty-five T2DM patients and 67 healthy controls (HCs) were included. All participants underwent clinical, neuropsychological, and laboratory tests, followed by MRI examinations, including resting-state functional magnetic resonance imaging (rs-fMRI) and three-dimensional high-resolution T1-weighted imaging (3D-T1WI) on a 3.0 T MRI scanner. Post-image preprocessing, brain functional networks were constructed using the Dosenbach atlas and analyzed with the DPABI-NET toolkit through graph theory.ResultsIn T2DM patients, functional connectivity within and between the default mode network (DMN), frontal parietal network (FPN), subcortical network (SCN), ventral attention network (VAN), somatosensory network (SMN), and visual network (VN) was significantly reduced compared to HCs. Conversely, two functional connections within the VN and between the DMN and SMN were significantly increased. Global network topology analysis showed an increased shortest path length and decreased clustering coefficient, global efficiency, and local efficiency in the T2DM group. MoCA scores were negatively correlated with the shortest path length and positively correlated with global and local efficiency in the T2DM group. Node network topology analysis indicated reduced clustering coefficient, degree centrality, eigenvector centrality, and nodal efficiency in multiple nodes in the T2DM group. MoCA scores positively correlated with clustering coefficient and nodal efficiency in the bilateral precentral gyrus in the T2DM group.DiscussionThis study demonstrated significant abnormalities in connectivity and topology of large-scale brain functional networks in T2DM patients. These findings suggest that brain functional network connectivity and topology could serve as imaging biomarkers, providing insights into the underlying neuropathological processes associated with T2DM-related cognitive impairment.https://www.frontiersin.org/articles/10.3389/fnins.2025.1472010/fulltype 2 diabetes mellitusfunctional magnetic resonance imagingblood oxygenation level-dependent imagingbrain functional network connectivitygraph theory |
| spellingShingle | Weiwei Ni Weiyin Vivian Liu Mingrui Li Shouchao Wei Xuanzi Xu Shutong Huang Lanhui Zhu Jieru Wang Fengling Wen Hailing Zhou Altered brain functional network connectivity and topology in type 2 diabetes mellitus Frontiers in Neuroscience type 2 diabetes mellitus functional magnetic resonance imaging blood oxygenation level-dependent imaging brain functional network connectivity graph theory |
| title | Altered brain functional network connectivity and topology in type 2 diabetes mellitus |
| title_full | Altered brain functional network connectivity and topology in type 2 diabetes mellitus |
| title_fullStr | Altered brain functional network connectivity and topology in type 2 diabetes mellitus |
| title_full_unstemmed | Altered brain functional network connectivity and topology in type 2 diabetes mellitus |
| title_short | Altered brain functional network connectivity and topology in type 2 diabetes mellitus |
| title_sort | altered brain functional network connectivity and topology in type 2 diabetes mellitus |
| topic | type 2 diabetes mellitus functional magnetic resonance imaging blood oxygenation level-dependent imaging brain functional network connectivity graph theory |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2025.1472010/full |
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