Irregular structural networks of gray matter in patients with type 2 diabetes mellitus

Cao, Chuanlong; W, Liu; Zhang, Qing; Wu, Jianlin; Sun, Yuan; Li, Danyang; Fan, Hongyu; Wang, Feifei

doi:10.1007/s11682-019-00070-2

Cited by 10 publications

(8 citation statements)

References 58 publications

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“…Neuroimaging is one of the most important techniques for exploring T2DM-CD. Gray matter (GM) and white matter (WM) are two sensitive markers in neuroimaging studies, which indicate the changes in brain structure caused by T2DM [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Gray and white matter abnormality in patients with T2DM-related cognitive dysfunction: a systemic review and meta-analysis

et al. 2022

Nutr. Diabetes

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Aims/hypothesis Brain structure abnormality in patients with type 2 diabetes mellitus (T2DM)-related cognitive dysfunction (T2DM-CD) has been reported for decades in magnetic resonance imaging (MRI) studies. However, the reliable results were still unclear. This study aimed to make a systemic review and meta-analysis to find the significant and consistent gray matter (GM) and white matter (WM) alterations in patients with T2DM-CD by comparing with the healthy controls (HCs). Methods Published studies were systemically searched from PubMed, MEDLINE, Cochrane Library and Web of Science databases updated to November 14, 2021. Studies reporting abnormal GM or WM between patients with T2DM-CD and HCs were selected, and their significant peak coordinates (x, y, z) and effect sizes (z-score or t-value) were extracted to perform a voxel-based meta-analysis by anisotropic effect size-signed differential mapping (AES-SDM) 5.15 software. Results Total 15 studies and 16 datasets (1550 participants) from 7531 results were involved in this study. Compared to HCs, patients with T2DM-CD showed significant and consistent decreased GM in right superior frontal gyrus, medial orbital (PFCventmed. R, BA 11), left superior temporal gyrus (STG. L, BA 48), and right calcarine fissure / surrounding cortex (CAL. R, BA 17), as well as decreased fractional anisotropy (FA) in right inferior network, inferior fronto-occipital fasciculus (IFOF. R), right inferior network, longitudinal fasciculus (ILF. R), and undefined area (32, −60, −42) of cerebellum. Meta-regression showed the positive relationship between decreased GM in PFCventmed.R and MoCA score, the positive relationship between decreased GM in STG.L and BMI, as well as the positive relationship between the decreased FA in IFOF.R and age or BMI. Conclusions/interpretation T2DM impairs the cognitive function by affecting the specific brain structures. GM atrophy in PFCventmed. R (BA 11), STG. L (BA 48), and CAL. R (BA 17), as well as WM injury in IFOF. R, ILF. R, and undefined area (32, −60, −42) of cerebellum. And those brain regions may be valuable targets for future researches. Age, BMI, and MoCA score have a potential influence on the altered GM or WM in T2DM-CD.

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Section: Introductionmentioning

confidence: 99%

Gray and white matter abnormality in patients with T2DM-related cognitive dysfunction: a systemic review and meta-analysis

et al. 2022

Nutr. Diabetes

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“…The existing studies either focused on the voxel‐by‐voxel statistical comparisons (Chen et al, 2014; Cui et al, 2014; Cui et al, 2015) or limited the scope in certain predefined brain regions/subnetworks, despite the structural studies suggesting unspecific and widespread alterations (Moran et al, 2013; Schmidt et al, 2004). While network neuroscience studies with complex network analysis indicated that T2DM patients had reduced efficiency in the whole‐brain structural connectivity network (Zhang et al, 2014) and the gray matter co‐variation network (Cao et al, 2019) compared to healthy controls (HCs), none of them reported if such a pattern also looks alike in the whole‐brain functional networks in T2DM and whether the affected connections are associated with T2DM‐induced cognitive impairment. In this study, we assumed that, due to the widespread and systemic effect caused by T2DM, T2DM‐CI could cause widely distributed brain functional dis‐concordance.…”

Section: Introductionmentioning

confidence: 99%

Classification of type 2 diabetes mellitus with or without cognitive impairment from healthy controls using high‐order functional connectivity

Chen

Zhou

Liang

et al. 2021

Human Brain Mapping

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Type 2 diabetes mellitus (T2DM) is associated with cognitive impairment and may progress to dementia. However, the brain functional mechanism of T2DM‐related dementia is still less understood. Recent resting‐state functional magnetic resonance imaging functional connectivity (FC) studies have proved its potential value in the study of T2DM with cognitive impairment (T2DM‐CI). However, they mainly used a mass‐univariate statistical analysis that was not suitable to reveal the altered FC “pattern” in T2DM‐CI, due to lower sensitivity. In this study, we proposed to use high‐order FC to reveal the abnormal connectomics pattern in T2DM‐CI with a multivariate, machine learning‐based strategy. We also investigated whether such patterns were different between T2DM‐CI and T2DM without cognitive impairment (T2DM‐noCI) to better understand T2DM‐induced cognitive impairment, on 23 T2DM‐CI and 27 T2DM‐noCI patients, as well as 50 healthy controls (HCs). We first built the large‐scale high‐order brain networks based on temporal synchronization of the dynamic FC time series among multiple brain region pairs and then used this information to classify the T2DM‐CI (as well as T2DM‐noCI) from the matched HC based on support vector machine. Our model achieved an accuracy of 79.17% in T2DM‐CI versus HC differentiation, but only 59.62% in T2DM‐noCI versus HC classification. We found abnormal high‐order FC patterns in T2DM‐CI compared to HC, which was different from that in T2DM‐noCI. Our study indicates that there could be widespread connectivity alterations underlying the T2DM‐induced cognitive impairment. The results help to better understand the changes in the central neural system due to T2DM.

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“…According to Cao et al, 31 each brain was divided into 90 cortical and subcortical regions of interest (ROI), excluding the cerebellum, using the automatic anatomical labeling (AAL) template. The average volume of each ROI was extracted from the GM map generated by VBM.…”

Section: Construction Of the Structural Covariance Networkmentioning

confidence: 99%

<p>Topological Regularization of Networks in Adult Patients with Moderate-to-Severe Obstructive Sleep Apnea-Hypopnea Syndrome: A Structural MRI Study</p>

Cao

et al. 2020

NSS

Self Cite

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Objective: Patients with obstructive sleep apnea-hypopnea syndrome (OSAHS) exhibit neurocognitive impairments; however, the neuroimaging mechanism of neurocognitive impairments remains unclear. The aim of this study was to understand the neuroimaging mechanism in adult patients with moderate-to-severe OSAHS, from the perspective of the connectome.Patients and Methods: Thirty-one untreated patients with moderate-to-severe OSAHS (mean age: 41.23±8.22) were compared with 26 good sleepers (GS) (mean age: 39.50±7.92) matched according to age, gender, handedness, and education level. All subjects underwent thin-slice T1WI scanning of the skull using a 3.0T MRI. Then, a large-scale structural covariance network was constructed based on the gray matter volume extracted from the structural MRI. Graph theory was then used to determine the topological changes in the structural covariance network of OSAHS patients. Results: Although small-world networks were retained,the structural covariance network exhibited topological irregularities in regular architecture as evidenced by an increase in the clustering coefficient (p=0.009), transfer coefficient (p=0.029) and local efficiency (p=0.031), and a local increase in the shortest path length (p<0.05) compared with the GS group. Locally, OSAHS patients showed a decrease in nodal betweenness and degree in the left inferior parietal gyrus, left angular gyrus and right anterior cingulate cortex compared with the GS group (p<0.05, uncorrected). In addition, the resistance of structural covariance networks in OSAHS patients to random fault is significantly lower than that of the GS group (p=0.044). Conclusion: Structural covariance networks are abnormal in terms of multiple network parameters, which provide network-level insight into the neuroimaging mechanism of cognitive impairments in adult OSAHS patients.

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Irregular structural networks of gray matter in patients with type 2 diabetes mellitus

Cited by 10 publications

References 58 publications

Gray and white matter abnormality in patients with T2DM-related cognitive dysfunction: a systemic review and meta-analysis

Gray and white matter abnormality in patients with T2DM-related cognitive dysfunction: a systemic review and meta-analysis

Classification of type 2 diabetes mellitus with or without cognitive impairment from healthy controls using high‐order functional connectivity

<p>Topological Regularization of Networks in Adult Patients with Moderate-to-Severe Obstructive Sleep Apnea-Hypopnea Syndrome: A Structural MRI Study</p>

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