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Kuang Y, Wu Z, Xia R, Li X, Liu J, Dai Y, Wang D, Chen S. Phase Lag Index of Resting-State EEG for Identification of Mild Cognitive Impairment Patients with Type 2 Diabetes. Brain Sci 2022; 12:brainsci12101399. [PMID: 36291332 PMCID: PMC9599801 DOI: 10.3390/brainsci12101399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 10/02/2022] [Accepted: 10/07/2022] [Indexed: 11/30/2022] Open
Abstract
Mild cognitive impairment (MCI) is one of the important comorbidities of type 2 diabetes mellitus (T2DM). It is critical to find appropriate methods for early diagnosis and objective assessment of mild cognitive impairment patients with type 2 diabetes (T2DM-MCI). Our study aimed to investigate potential early alterations in phase lag index (PLI) and determine whether it can distinguish between T2DM-MCI and normal controls with T2DM (T2DM-NC). EEG was recorded in 30 T2DM-MCI patients and 30 T2DM-NC patients. The phase lag index was computed and used in a logistic regression model to discriminate between groups. The correlation between the phase lag index and Montreal Cognitive Assessment (MoCA) score was assessed. The α-band phase lag index was significantly decreased in the T2DM-MCI group compared with the T2DM-NC group and showed a moderate degree of classification accuracy. The MoCA score was positively correlated with the α-band phase lag index (r = 0.4812, moderate association, p = 0.015). This work shows that the functional connectivity analysis of EEG may offer an effective way to track the cortical dysfunction linked to the cognitive deterioration of T2DM patients, and the α-band phase lag index may have a role in guiding the diagnosis of T2DM-MCI.
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Affiliation(s)
- Yuxing Kuang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
- Department of Rehabilitation, Affiliated Baoan Hospital of Shenzhen, Southern Medical University (The People’s Hospital of Baoan Shenzhen), Shenzhen 518101, China
| | - Ziyi Wu
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
- Department of Rehabilitation, Affiliated Baoan Hospital of Shenzhen, Southern Medical University (The People’s Hospital of Baoan Shenzhen), Shenzhen 518101, China
| | - Rui Xia
- Department of Rehabilitation, Affiliated Baoan Hospital of Shenzhen, Southern Medical University (The People’s Hospital of Baoan Shenzhen), Shenzhen 518101, China
| | - Xingjie Li
- Department of Rehabilitation, Affiliated Baoan Hospital of Shenzhen, Southern Medical University (The People’s Hospital of Baoan Shenzhen), Shenzhen 518101, China
| | - Jun Liu
- Department of Rehabilitation, Affiliated Baoan Hospital of Shenzhen, Southern Medical University (The People’s Hospital of Baoan Shenzhen), Shenzhen 518101, China
| | - Yalan Dai
- Department of Rehabilitation, Affiliated Baoan Hospital of Shenzhen, Southern Medical University (The People’s Hospital of Baoan Shenzhen), Shenzhen 518101, China
| | - Dan Wang
- Department of Rehabilitation, Affiliated Baoan Hospital of Shenzhen, Southern Medical University (The People’s Hospital of Baoan Shenzhen), Shenzhen 518101, China
| | - Shangjie Chen
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
- Department of Rehabilitation, Affiliated Baoan Hospital of Shenzhen, Southern Medical University (The People’s Hospital of Baoan Shenzhen), Shenzhen 518101, China
- Correspondence: ; Tel.: +86-0755-27788311
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Zhang J, Liu Y, Guo X, Guo J, Du Z, He M, Liu Q, Xu D, Liu T, Zhang J, Yuan H, Wang M, Li S. Causal Structural Covariance Network Suggesting Structural Alterations Progression in Type 2 Diabetes Patients. Front Hum Neurosci 2022; 16:936943. [PMID: 35911591 PMCID: PMC9336220 DOI: 10.3389/fnhum.2022.936943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 06/16/2022] [Indexed: 12/02/2022] Open
Abstract
Background and Purpose According to reports, type 2 diabetes (T2D) is a progressive disease. However, no known research has examined the progressive brain structural changes associated with T2D. The purpose of this study was to determine whether T2D patients exhibit progressive brain structural alterations and, if so, how the alterations progress. Materials and Methods Structural magnetic resonance imaging scans were collected for 81 T2D patients and 48 sex-and age-matched healthy controls (HCs). Voxel-based morphometry (VBM) and causal structural covariance network (CaSCN) analyses were applied to investigate gray matter volume (GMV) alterations and the likely chronological processes underlying them in T2D. Two sample t-tests were performed to compare group differences, and the differences were corrected using Gaussian random field (GRF) correction (voxel-level p < 0.001, cluster-level p < 0.01). Results Our findings demonstrated that GMV alterations progressed in T2D patients as disease duration increased. In the early stages of the disease, the right temporal pole of T2D patients had GMV atrophy. As the diseases duration prolonged, the limbic system, cerebellum, subcortical structures, parietal cortex, frontal cortex, and occipital cortex progressively exhibited GMV alterations. The patients also exhibited a GMV alterations sequence exerting from the right temporal pole to the limbic-cerebellum-striatal-cortical network areas. Conclusion Our results indicate that the progressive GMV alterations of T2D patients manifested a limbic-cerebellum-striatal-cortical sequence. These findings may contribute to a better understanding of the progression and an improvement of current diagnosis and intervention strategies for T2D.
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Affiliation(s)
- Jiang Zhang
- College of Electrical Engineering, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yuyan Liu
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Xiaonan Guo
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Jing Guo
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhengcong Du
- School of Information Science and Technology, Xichang University, Xichang, China
| | - Muyuan He
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Qihong Liu
- College of Biomedical Engineering, Sichuan University, Chengdu, China
| | - Dundi Xu
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Taiyuan Liu
- Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, China
| | - Junran Zhang
- College of Electrical Engineering, Sichuan University, Chengdu, China
- *Correspondence: Junran Zhang
| | - Huijuan Yuan
- Department of Endocrinology, Henan Provincial People's Hospital, Zhengzhou, China
- Huijuan Yuan
| | - Meiyun Wang
- Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, China
- Meiyun Wang
| | - Shasha Li
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
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