Altered Structural and Functional MRI Connectivity in Type 2 Diabetes Mellitus Related Cognitive Impairment: A Review

Lei, Hao; Hu, Rong; Luo, Guanghua; Yang, Tingqian; Shen, Hui; Deng, Hao; Chen, Chun-Yu; Zhao, Heng; Liu, Jincai

doi:10.3389/fnhum.2021.755017

Cited by 9 publications

(6 citation statements)

References 176 publications

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“…In addition, pED, owing predominantly to psychological factors including anxiety, depression, and introversion, was found to be more vulnerable to structural and functional changes in the prefrontal regions ( 26 , 27 , 38 ). Therefore, our findings were in agreement with the central neural mechanisms of pED and DM in previous neuroimaging studies ( 11 , 38 , 50 ).…”

Section: Discussionsupporting

confidence: 93%

“…An epidemiological study suggested that the prevalence of diabetes was increasing rapidly, and the prevalence of erectile dysfunction (ED) among diabetic patients varied from 35 to 90% ( 10 ). Compared with the general population, patients with T2DM have a higher risk for cognitive decline, which is one of the central nervous system complications associated with abnormalities of brain function and structure ( 11 – 13 ). In addition, T2DM patients are at higher risk of developing male sexual dysfunction, including ED and retrograde ejaculation, which are two common peripheral microvascular and neurological complications associated with oxidative stress-induced penile vascular endothelial cell injury and peripheral neuropathy ( 14 – 17 ).…”

Section: Introductionmentioning

confidence: 99%

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Differences in structural connectivity between diabetic and psychological erectile dysfunction revealed by network-based statistic: A diffusion tensor imaging study

Chen

Xiao-hong

et al. 2022

Front. Endocrinol.

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IntroductionType 2 diabetes mellitus (T2DM) has been found to be associated with abnormalities of the central and peripheral vascular nervous system, which were considered to be involved in the development of cognitive impairments and erectile dysfunction (ED). In addition, altered brain function and structure were identified in patients with ED, especially psychological ED (pED). However, the similarities and the differences of the central neural mechanisms underlying pED and T2DM with ED (DM-ED) remained unclear.MethodsDiffusion tensor imaging data were acquired from 30 T2DM, 32 ED, and 31 DM-ED patients and 47 healthy controls (HCs). Then, whole-brain structural networks were constructed, which were mapped by connectivity matrices (90 × 90) representing the white matter between 90 brain regions parcellated by the anatomical automatic labeling template. Finally, the method of network-based statistic (NBS) was applied to assess the group differences of the structural connectivity.ResultsOur NBS analysis demonstrated three subnetworks with reduced structural connectivity in DM, pED, and DM-ED patients when compared to HCs, which were predominantly located in the prefrontal and subcortical areas. Compared with DM patients, DM-ED patients had an impaired subnetwork with increased structural connectivity, which were primarily located in the parietal regions. Compared with pED patients, an altered subnetwork with increased structural connectivity was identified in DM-ED patients, which were mainly located in the prefrontal and cingulate areas.ConclusionThese findings highlighted that the reduced structural connections in the prefrontal and subcortical areas were similar mechanisms to those associated with pED and DM-ED. However, different connectivity patterns were found between pED and DM-ED, and the increased connectivity in the frontal–parietal network might be due to the compensation mechanisms that were devoted to improving erectile function.

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Section: Discussionsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Differences in structural connectivity between diabetic and psychological erectile dysfunction revealed by network-based statistic: A diffusion tensor imaging study

Chen

Xiao-hong

et al. 2022

Front. Endocrinol.

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“…Neuropsychological screening tests are easily performed in most clinics or hospitals, but the fact that the neuropsychological data were difficult to be interpreted has increased the necessity of medical images, artificial intelligence, and even a combination of them. In the field of medical image, several Cognitive dysfunction biomarkers have been studied including the brain metabolic change derived from fluorodeoxyglucose positron emission tomography (FDG-PET) ( Yuan et al, 2009 ; Pardo et al, 2010 ; Newberg et al, 2022 ), and the structural or functional change in the brain measured by MRI ( van Harten et al, 2007 ; Belfort-Deaguiar et al, 2014 ; Lei et al, 2021 ). In recent years, with the development of MRI scanner and its increasingly enhanced data processing function, the level of analysis has been moving from assessment of brain structure and morphology such as volume and general atrophy to more detailed and in-depth analysis of white matter tracts, using diffusion tensor imaging (DTI), a newer method.…”

Section: Discussionmentioning

confidence: 99%

MRI-based machine learning model: A potential modality for predicting cognitive dysfunction in patients with type 2 diabetes mellitus

Xu¹,

Zhao²,

et al. 2022

Front. Bioeng. Biotechnol.

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Background: Type 2 diabetes mellitus (T2DM) is a crucial risk factor for cognitive impairment. Accurate assessment of patients’ cognitive function and early intervention is helpful to improve patient’s quality of life. At present, neuropsychiatric screening tests is often used to perform this task in clinical practice. However, it may have poor repeatability. Moreover, several studies revealed that machine learning (ML) models can effectively assess cognitive impairment in Alzheimer’s disease (AD) patients. We investigated whether we could develop an MRI-based ML model to evaluate the cognitive state of patients with T2DM.Objective: To propose MRI-based ML models and assess their performance to predict cognitive dysfunction in patients with type 2 diabetes mellitus (T2DM).Methods: Fluid Attenuated Inversion Recovery (FLAIR) of magnetic resonance images (MRI) were derived from 122 patients with T2DM. Cognitive function was assessed using the Chinese version of the Montréal Cognitive Assessment Scale-B (MoCA-B). Patients with T2DM were separated into the Dementia (DM) group (n = 40), MCI group (n = 52), and normal cognitive state (N) group (n = 30), according to the MoCA scores. Radiomics features were extracted from MR images with the Radcloud platform. The variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) were used for the feature selection. Based on the selected features, the ML models were constructed with three classifiers, k-NearestNeighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR), and the validation method was used to improve the effectiveness of the model. The area under the receiver operating characteristic curve (ROC) determined the appearance of the classification. The optimal classifier was determined by the principle of maximizing the Youden index.Results: 1,409 features were extracted and reduced to 13 features as the optimal discriminators to build the radiomics model. In the validation set, ROC curves revealed that the LR classifier had the best predictive performance, with an area under the curve (AUC) of 0.831 in DM, 0.883 in MIC, and 0.904 in the N group, compared with the SVM and KNN classifiers.Conclusion: MRI-based ML models have the potential to predict cognitive dysfunction in patients with T2DM. Compared with the SVM and KNN, the LR algorithm showed the best performance.

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“…Resting-state functional magnetic resonance imaging (rs-fMRI) is a promising approach with which to studying the brain-related pathophysiology of T2DM ( Chau et al, 2021 ; Lei et al, 2022 ). Functional connectivity (FC) is an effective method to explore impaired frontostriatal circuits ( Di Martino et al, 2008 ; Kelly et al, 2009 ).…”

Section: Introductionmentioning

confidence: 99%

Abnormal functional connectivity of the frontostriatal circuits in type 2 diabetes mellitus

Mei-ling

Wang

et al. 2023

Front. Aging Neurosci.

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BackgroundType 2 diabetes mellitus (T2DM) is a metabolic disorder associated with an increased incidence of cognitive and emotional disorders. Previous studies have indicated that the frontostriatal circuits play a significant role in brain disorders. However, few studies have investigated functional connectivity (FC) abnormalities in the frontostriatal circuits in T2DM.ObjectiveWe aimed to investigate the abnormal functional connectivity (FC) of the frontostriatal circuits in patients with T2DM and to explore the relationship between abnormal FC and diabetes-related variables.MethodsTwenty-seven patients with T2DM were selected as the patient group, and 27 healthy peoples were selected as the healthy controls (HCs). The two groups were matched for age and sex. In addition, all subjects underwent resting-state functional magnetic resonance imaging (rs-fMRI) and neuropsychological evaluation. Seed-based FC analyses were performed by placing six bilateral pairs of seeds within a priori defined subdivisions of the striatum. The functional connection strength of subdivisions of the striatum was compared between the two groups and correlated with each clinical variable.ResultsPatients with T2DM showed abnormalities in the FC of the frontostriatal circuits. Our findings show significantly reduced FC between the right caudate nucleus and left precentral gyrus (LPCG) in the patients with T2DM compared to the HCs. The FC between the prefrontal cortex (left inferior frontal gyrus, left frontal pole, right frontal pole, and right middle frontal gyrus) and the right caudate nucleus has a significant positive correlation with fasting blood glucose (FBG).ConclusionThe results showed abnormal FC of the frontostriatal circuits in T2DM patients, which might provide a new direction to investigate the neuropathological mechanisms of T2DM.

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Altered Structural and Functional MRI Connectivity in Type 2 Diabetes Mellitus Related Cognitive Impairment: A Review

Cited by 9 publications

References 176 publications

Differences in structural connectivity between diabetic and psychological erectile dysfunction revealed by network-based statistic: A diffusion tensor imaging study

Differences in structural connectivity between diabetic and psychological erectile dysfunction revealed by network-based statistic: A diffusion tensor imaging study

MRI-based machine learning model: A potential modality for predicting cognitive dysfunction in patients with type 2 diabetes mellitus

Abnormal functional connectivity of the frontostriatal circuits in type 2 diabetes mellitus

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