Altered Whole-Brain Functional Topological Organization and Cognitive Function in Type 2 Diabetes Mellitus Patients

Structural characteristics of amygdala subregions in type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) patients often suffer from depressive symptoms, which seriously affect cooperation in treatment and nursing. The amygdala plays a significant role in depression. This study aims to explore the microstructural alterations of the amygdala in T2DM and to investigate the relationship between the alterations and depressive symptoms. Fifty T2DM and 50 healthy controls were included. Firstly, the volumes of subcortical regions and subregions of amygdala were calculated by FreeSurfer. Covariance analysis (ANCOVA) was conducted between the two groups with covariates of age, sex, and estimated total intracranial volume to explore the differences in volume of subcortical regions and subregions of amygdala. Furthermore, the structural covariance within the amygdala subregions was performed. Moreover, we investigate the correlation between depressive symptoms and the volume of subcortical regions and amygdala subregions in T2DM. We observed a reduction in the volume of the bilateral cortico-amygdaloid transition area, left basal nucleus, bilateral accessory basal nucleus, left anterior amygdaloid area of amygdala, the left thalamus and left hippocampus in T2DM. T2DM patients showed decreased structural covariance connectivity between left paralaminar nucleus and the right central nucleus. Moreover, there was a negative correlation between self-rating depression scale scores and the volume of the bilateral cortico-amygdaloid transition area in T2DM. This study reveals extensive structural alterations in the amygdala subregions of T2DM patients. The reduction in the volume of the bilateral cortico-amygdaloid transition area may be a promising imaging marker for early recognition of depressive symptoms in T2DM.

Altered cortical thickness-based structural covariance networks in type 2 diabetes mellitus

Cognitive impairment is a common complication of type 2 diabetes mellitus (T2DM), and early cognitive dysfunction may be associated with abnormal changes in the cerebral cortex. This retrospective study aimed to investigate the cortical thickness-based structural topological network changes in T2DM patients without mild cognitive impairment (MCI). Fifty-six T2DM patients and 59 healthy controls underwent neuropsychological assessments and sagittal 3-dimensional T1-weighted structural magnetic resonance imaging. Then, we combined cortical thickness-based assessments with graph theoretical analysis to explore the abnormalities in structural covariance networks in T2DM patients. Correlation analyses were performed to investigate the relationship between the altered topological parameters and cognitive/clinical variables. T2DM patients exhibited significantly lower clustering coefficient (C) and local efficiency (Elocal) values and showed nodal property disorders in the occipital cortical, inferior temporal, and inferior frontal regions, the precuneus, and the precentral and insular gyri. Moreover, the structural topological network changes in multiple nodes were correlated with the findings of neuropsychological tests in T2DM patients. Thus, while T2DM patients without MCI showed a relatively normal global network, the local topological organization of the structural network was disordered. Moreover, the impaired ventral visual pathway may be involved in the neural mechanism of visual cognitive impairment in T2DM patients. This study enriched the characteristics of gray matter structure changes in early cognitive dysfunction in T2DM patients.

Effects of individual metabolic brain network changes co-affected by T2DM and aging on the probabilities of T2DM: protective and risk factors

Neuroimaging markers for risk and protective factors related to type 2 diabetes mellitus are critical for clinical prevention and intervention. In this work, the individual metabolic brain networks were constructed with Jensen-Shannon divergence for 4 groups (elderly type 2 diabetes mellitus and healthy controls, and middle-aged type 2 diabetes mellitus and healthy controls). Regional network properties were used to identify hub regions. Rich-club, feeder, and local connections were subsequently obtained, intergroup differences in connections and correlations between them and age (or fasting plasma glucose) were analyzed. Multinomial logistic regression was performed to explore effects of network changes on the probability of type 2 diabetes mellitus. The elderly had increased rich-club and feeder connections, and decreased local connection than the middle-aged among type 2 diabetes mellitus; type 2 diabetes mellitus had decreased rich-club and feeder connections than healthy controls. Protective factors including glucose metabolism in triangle part of inferior frontal gyrus, metabolic connectivity between triangle of the inferior frontal gyrus and anterior cingulate cortex, degree centrality of putamen, and risk factors including metabolic connectivities between triangle of the inferior frontal gyrus and Heschl's gyri were identified for the probability of type 2 diabetes mellitus. Metabolic interactions among critical brain regions increased in type 2 diabetes mellitus with aging. Individual metabolic network changes co-affected by type 2 diabetes mellitus and aging were identified as protective and risk factors for the likelihood of type 2 diabetes mellitus, providing guiding evidence for clinical interventions.

Cognition and motion dysfunction-associated brain functional network disruption in diabetic peripheral neuropathy

Neuroimaging studies have demonstrated extensive brain functional alterations in cognitive and motor functional areas in Type 2 diabetes mellitus (T2DM) with diabetic peripheral neuropathy (DPN), suggesting potential alterations in large-scale brain networks related to DPN and associated cognition and motor dysfunction. In this study, using resting-state functional connectivity (FC) and graph theory computational approaches, we investigated the topological disruptions of brain functional networks in 28 DPN, 43 T2DM without DPN (NDPN), and 32 healthy controls (HCs) and examined the correlations between altered network topological metrics and cognitive/motor function parameters in T2DM. For global topology, NDPN exhibited a significantly decreased shortest path length compared with HCs, suggesting increased efficient global integration. For regional topology, DPN and NDPN had separated topological reorganization of functional hubs compared with HCs. In addition, DPN showed significantly decreased nodal efficiency (Enodal ), mainly in the bilateral superior occipital gyrus (SOG), right cuneus, middle temporal gyrus (MTG), and left inferior parietal gyrus (IPL), compared with NDPN, whereas NDPN showed significantly increased Enodal compared with HCs. Intriguingly, in T2DM patients, the Enodal of the right SOG was significantly negatively correlated with Toronto Clinical Scoring System scores, while the Enodal of the right postcentral gyrus (PoCG) and MTG were significantly positively correlated with Montreal Cognitive Assessment scores. Conclusively, DPN and NDPN patients had segregated disruptions in the brain functional network, which were related to cognition and motion dysfunctions. Our findings provide a theoretical basis for understanding the neurophysiological mechanism of DPN and its effective prevention and treatment in T2DM.

Altered brain activity in diabetic patients with erectile dysfunction revealed by fractional amplitude of low-frequency fluctuation: A resting-state fMRI study

INTRODUCTION

Diabetes mellitus is one of the major chronic diseases, which enhances the risk of erectile dysfunction. However, the central pathological mechanisms of erectile dysfunction in diabetes mellitus patients are still unclear.

METHODS

Resting-state functional magnetic resonance imaging data were acquired in 30 type-2 diabetes mellitus, 31 type-2 diabetes mellitus with erectile dysfunction patients, and 31 healthy controls. The measure of fractional amplitude of low-frequency fluctuation was calculated and compared among groups.

RESULTS

Differences of fractional amplitude of low-frequency fluctuation values were found in the left superior frontal gyrus (medial) and middle temporal gyrus among three groups. Compared with healthy controls group, type-2 diabetes mellitus group exhibited lower fractional amplitude of low-frequency fluctuation values in the left superior frontal gyrus (dorsolateral), anterior cingulate gyrus, calcarine fissure, and increased fractional amplitude of low-frequency fluctuation values in the left post-central gyrus. Compared with healthy controls group, erectile dysfunction with type-2 diabetes mellitus group exhibited lower fractional amplitude of low-frequency fluctuation values in the left superior frontal gyrus (medial), middle temporal gyrus, temporal middle (pole), and increased fractional amplitude of low-frequency fluctuation values in the right post-central gyrus. Compared with type-2 diabetes mellitus group, erectile dysfunction with type-2 diabetes mellitus group exhibited increased fractional amplitude of low-frequency fluctuation values in the right median cingulum gyrus and left calcarine fissure.

CONCLUSION

Erectile dysfunction with type-2 diabetes mellitus patients showed functional changes in brain regions that were closely correlated with sexual dysfunction, which suggested that altered regional brain activity might be related to the pathophysiology of erectile dysfunction with type-2 diabetes mellitus.

The Interaction of Glycemia with Anxiety and Depression Is Related to Altered Cerebellar and Cerebral Functional Correlations

Depression, type 2 diabetes (T2D), and obesity are comorbid, and prevention and treatment of all three diseases are needed. We hypothesized an inverse relationship between the connectivity of the cingulo-opercular task control network with the somatosensory mouth network and the interaction between HbA1c and depression. Three-hundred and twenty-five participants (BMI: 26.11 ± 0.29; Achenbach adult self-report (ASR) DSM depressive problems T-score (depression): 54.60 ± 6.77; Age: 28.26 ± 3.90 y; adult self-report anxiety and depression scale (anxiety and depression): 54.69 ± 7.27; HbA1c: 5.26 ± 0.29; 68% white) were sampled from the Human Connectome Project 1200 subjects PTN release. Inclusion criteria were: four (15 min) resting state fMRI scans; BMI; hemoglobin A1c (HbA1c); and complete adult self-report data. The following models were run to assess the connectivity between 15 independent fMRI components: the interaction of depression with HbA1c; anxiety and depression with HbA1c; depression with BMI; and anxiety and depression with BMI. All models were corrected for a reported number of depressive symptoms, head motion in the scanner, age, and race. Functional connectivity was modeled in FSLNets. Corrected significance was set at pFWE < 0.05. The interaction HbA1c and anxiety and depression was positively related to the connectivity of the cerebellum with the visual network (t = 3.76, pFWE = 0.008), frontoparietal network (t = 3.45, pFWE = 0.02), and somatosensory mouth network (t = 4.29, pFWE = 0.0004). Although our hypotheses were not supported, similar increases in cerebellar connectivity are seen in patients with T2D and overall suggest that the increased cerebellar connectivity may be compensatory for an increasingly poor glycemic control.

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

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.

Neurovascular decoupling measured with quantitative susceptibility mapping is associated with cognitive decline in patients with type 2 diabetes

Disturbance of neurovascular coupling (NVC) is suggested to be one potential mechanism in type 2 diabetes mellitus (T2DM) associated mild cognitive impairment (MCI). However, NVC evidence derived from functional magnetic resonance imaging ignores the relationship of neuronal activity with vascular injury. Twenty-seven T2DM patients without MCI and thirty healthy controls were prospectively enrolled. Brain regions with changed susceptibility detected by quantitative susceptibility mapping (QSM) were used as seeds for functional connectivity (FC) analysis. NVC coefficients were estimated using combined degree centrality (DC) with susceptibility or cerebral blood flow (CBF). Partial correlations between neuroimaging indicators and cognitive decline were investigated. In T2DM group, higher susceptibility values in right hippocampal gyrus (R.PHG) were found and were negatively correlated with Naming Ability of Montreal Cognitive Assessment. FC increased remarkably between R.PHG and right middle temporal gyrus (R.MTG), right calcarine gyrus (R.CAL). Both NVC coefficients (DC-QSM and DC-CBF) reduced in R.PHG and increased in R.MTG and R.CAL. Both NVC coefficients in R.PHG and R.MTG increased with the improvement of cognitive ability, especially for executive function. These demonstrated that QSM and DC-QSM coefficients can be promising biomarkers for early evaluation of cognitive decline in T2DM patients and help to better understand the mechanism of NVC.

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 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.

Prediction value of the genetic risk of type 2 diabetes on the amnestic mild cognitive impairment conversion to Alzheimer’s disease

Amnestic mild cognitive impairment (aMCI) and Type 2 diabetes mellitus (T2DM) are both important risk factors for Alzheimer’s disease (AD). We aimed to investigate whether a T2DM-specific polygenic risk score (PRS_sT2DM) can predict the conversion of aMCI to AD and further explore the underlying neurological mechanism. All aMCI patients were from the Alzheimer’s disease Neuroimaging Initiative (ADNI) database and were divided into conversion (aMCI-C, n = 164) and stable (aMCI-S, n = 222) groups. PRS_sT2DM was calculated by PRSice-2 software to explore the predictive efficacy of the aMCI conversion to AD. We found that PRS_sT2DM could independently predict the aMCI conversion to AD after removing the common variants of these two diseases. PRS_sT2DM was significantly negatively correlated with gray matter volume (GMV) of the right superior frontal gyrus in the aMCI-C group. In all aMCI patients, PRS_sT2DM was significantly negatively correlated with the cortical volume of the right superior occipital gyrus. The cortical volume of the right superior occipital gyrus could significantly mediate the association between PRS_sT2DM and aMCI conversion. Gene-based analysis showed that T2DM-specific genes are highly expressed in cortical neurons and involved in ion and protein binding, neural development and generation, cell junction and projection, and PI3K-Akt and MAPK signaling pathway, which might increase the aMCI conversion by affecting the Tau phosphorylation and amyloid-beta (Aβ) accumulation. Therefore, the PRS_sT2DM could be used as a measure to predict the conversion of aMCI to AD.

Changes of brain function in patients with type 2 diabetes mellitus measured by different analysis methods: A new coordinate-based meta-analysis of neuroimaging

Objective

Neuroimaging meta-analysis identified abnormal neural activity alterations in patients with type 2 diabetes mellitus (T2DM), but there was no consistency or heterogeneity analysis between different brain imaging processing strategies. The aim of this meta-analysis was to determine consistent changes of regional brain functions in T2DM via the indicators obtained by using different post-processing methods.

Methods

Since the indicators obtained using varied post-processing methods reflect different neurophysiological and pathological characteristics, we further conducted a coordinate-based meta-analysis (CBMA) of the two categories of neuroimaging literature, which were grouped according to similar data processing methods: one group included regional homogeneity (ReHo), independent component analysis (ICA), and degree centrality (DC) studies, while the other group summarized the literature on amplitude of low-frequency fluctuation (ALFF) and cerebral blood flow (CBF).

Results

The final meta-analysis included 23 eligible trials with 27 data sets. Compared with the healthy control group, when neuroimaging studies were combined with ReHo, ICA, and DC measurements, the brain activity of the right Rolandic operculum, right supramarginal gyrus, and right superior temporal gyrus in T2DM patients decreased significantly. When neuroimaging studies were combined with ALFF and CBF measurements, there was no clear evidence of differences in the brain function between T2DM and HCs.

Conclusion

T2DM patients have a series of spontaneous abnormal brain activities, mainly involving brain regions related to learning, memory, and emotion, which provide early biomarkers for clarifying the mechanism of cognitive impairment and neuropsychiatric disorders in diabetes.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=247071, PROSPERO [CRD42021247071].

Integrated Prediction Framework for Clinical Scores of Cognitive Functions in ESRD Patients

The clinical scores are applied to determine the stage of cognitive function in patients with end-stage renal disease (ESRD). However, accurate clinical scores are hard to come by. This paper proposed an integrated prediction framework with GPLWLSV to predict clinical scores of cognitive functions in ESRD patients. GPLWLSV incorporated three parts, graph theoretic algorithm (GTA) and principal component analysis (PCA), whale optimization algorithm with Levy flight (LWOA), and least squares support vector regression machine (LSSVRM). GTA was adopted to extract features from the brain functional networks in ESRD patients, while PCA was used to select features. LSSVRM was built to explore the relationship between the selected features and the clinical scores of ESRD patients. Whale optimization algorithm (WOA) was introduced to select better parameters of the kernel function in LSSVRM; it aims to improve the exploration competence of LSSVRM. Levy flight was used to optimize the ability to jump out of local optima in WOA and improve the convergence of coefficient vectors in WOA, which lead to an increase in the generalization ability and convergence speed of WOA. The results validated that the prediction accuracy of GPLWLSV was higher than that of several comparable frameworks, such as GPSV, GPLSV, and GPWLSV. In particular, the average of root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) between the predicted scores and the actual scores of ESRD patients was 2.40, 2.06, and 9.83%, respectively. The proposed framework not only can predict the clinical scores more accurately but also can capture imaging markers associated with decline of cognitive function. It helps to understand the potential relationship between structural changes in the brain and cognitive function of ESRD patients.

Regional homogeneity of adolescents with high-functioning autism spectrum disorder and its association with symptom severity

BACKGROUND AND PURPOSE

Previous studies have revealed abnormal regional homogeneity (ReHo) in individuals with autism spectrum disorder (ASD); however, there is little consistency across the findings within these studies, partly due to small sample size and great heterogeneity among participants between studies. Additionally, few studies have explored the association between ReHo aberrance and clinical symptoms in individuals with ASD.

METHODS

Forty-eight adolescents with high-functioning ASD and 63 group-matched typically developing (TD) controls received functional magnetic resonance imaging at rest. Group-level analysis was performed to detect differences in ReHo between ASD and TD. Evaluation of symptom severity in individuals with ASD was based on the Autism Behavior Checklist (ABC). Voxel-wise correlation analysis was undergone to examine the correlations between the symptom severity and ReHo map in individuals with ASD within brain areas with ReHo abnormalities.

RESULTS

Compared with the TD controls, individuals with ASD exhibited increased ReHo in the bilateral anterior cingulate cortex, left caudate, right posterior cerebellum (cerebellar tonsil), and bilateral brainstem and decreased ReHo in the left precentral gyrus, left inferior parietal lobule, bilateral postcentral gyrus, and right anterior cerebellum (culmen). The correlation analysis indicated that the ReHo value in the brainstem was negatively associated with the ABC total scores and the scores of Relating factor, respectively.

CONCLUSIONS

Our findings indicated that widespread ReHo abnormalities occurred in ASD, shedding light on the underlying neurobiology of pathogenesis and symptomatology of ASD.

Convolutional Neural Networks for Classification of T2DM Cognitive Impairment Based on Whole Brain Structural Features

Purpose

Cognitive impairment is generally found in individuals with type 2 diabetes mellitus (T2DM). Although they may not have visible symptoms of cognitive impairment in the early stages of the disorder, they are considered to be at high risk. Therefore, the classification of these patients is important for preventing the progression of cognitive impairment.

Methods

In this study, a convolutional neural network was used to construct a model for classifying 107 T2DM patients with and without cognitive impairment based on T1-weighted structural MRI. The Montreal cognitive assessment score served as an index of the cognitive status of the patients.

Results

The classifier could identify T2DM-related cognitive decline with a classification accuracy of 84.85% and achieved an area under the curve of 92.65%.

Conclusions

The model can help clinicians analyze and predict cognitive impairment in patients and enable early treatment.

A scoping review of resting-state brain functional alterations in Type 2 diabetes

Resting-state functional magnetic resonance imaging (rs-fMRI) has been actively used in the last decade to investigate brain functional connectivity alterations in Type 2 Diabetes Mellitus (T2DM) to understand the neuropathophysiology of T2DM in cognitive degeneration. Given the emergence of new analysis techniques, this scoping review aims to map the rs-fMRI analysis techniques that have been applied in the literature and reports the latest rs-fMRI findings that have not been covered in previous reviews. Graph theory, the contemporary rs-fMRI analysis, has been used to demonstrate altered brain topological organisations in people with T2DM, which included altered degree centrality, functional connectivity strength, the small-world architecture and network-based statistics. These alterations were correlated with T2DM patients' cognitive performances. Graph theory also contributes to identify unbiased seeds for seed-based analysis. The expanding rs-fMRI analytical approaches continue to provide new evidence that helps to understand the mechanisms of T2DM-related cognitive degeneration.

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

Type 2 diabetes mellitus (T2DM) is associated with cognitive impairment in many domains. There are several pieces of evidence that changes in neuronal neuropathies and metabolism have been observed in T2DM. Structural and functional MRI shows that abnormal connections and synchronization occur in T2DM brain circuits and related networks. Neuroplasticity and energy metabolism appear to be principal effector systems, which may be related to amyloid beta (Aβ) deposition, although there is no unified explanation that includes the complex etiology of T2DM with cognitive impairment. Herein, we assume that cognitive impairment in diabetes may lead to abnormalities in neuroplasticity and energy metabolism in the brain, and those reflected to MRI structural connectivity and functional connectivity, respectively.

Altered Functional Topological Organization in Type-2 Diabetes Mellitus With and Without Microvascular Complications

Microvascular complications can accelerate cognitive impairment in patients with type 2 diabetes mellitus (T2DM) and have a high impact on their quality of life; however, the underlying mechanism is still unclear. The complex network in the human brain is the physiological basis for information processing and cognitive expression. Therefore, this study explored the relationship between the functional network topological properties and cognitive function in T2DM patients with and without microvascular complications (T2DM-C and T2DM-NC, respectively). Sixty-seven T2DM patients and 41 healthy controls (HCs) underwent resting-state functional MRI and neuropsychological assessment. Then, graph theoretical network analysis was performed to explore the global and nodal topological alterations in the functional whole brain networks of T2DM patients. Correlation analyses were performed to investigate the relationship between the altered topological parameters and cognitive/clinical variables. The T2DM-C group exhibited significantly higher local efficiency (Eloc), normalized cluster coefficient (γ), and small-world characteristics (σ) than the HCs. Patients with T2DM at different clinical stages (T2DM-C and T2DM-NC) showed varying degrees of abnormalities in node properties. In addition, compared with T2DM-NC patients, T2DM-C patients showed nodal properties disorders in the occipital visual network, cerebellum and middle temporal gyrus. The Eloc metrics were positively correlated with HbA1c level (P = 0.001, r = 0.515) and the NE values in the right paracentral lobule were negatively related with serum creatinine values (P = 0.001, r = −0.517) in T2DM-C patients. This study found that T2DM-C patients displayed more extensive changes at different network topology scales. The visual network and cerebellar may be the central vulnerable regions of T2DM-C patients.

Altered Gray Matter Volume, Functional Connectivity, and Degree Centrality in Early-Onset Type 2 Diabetes Mellitus

Background: Structural and functional brain alterations that underlie cognitive decline have been observed in elderly adults with type 2 diabetes mellitus (T2DM); however, whether these alterations can be observed in patients with early-onset T2DM remains unclear. Therefore, we aimed to describe the abnormalities in brain volume and functional patterns in patients with early-onset T2DM in the present study.

Methods: We enrolled 20 patients with early-onset T2DM and 20 healthy controls (HCs). Changes in brain volume were assessed using voxel-based morphology (VBM), while changes in brain function were assessed using degree centrality (DC) and functional connectivity (FC).

Results: Compared to HCs, patients with early-onset T2DM exhibited gray matter reductions in the left orbital superior, middle, and inferior frontal gyri as well as the right superior frontal gyrus. The gray matter reductions in the right superior frontal gyrus were negatively associated with the urine albumin to creatinine ratio. Furthermore, increased DC values were observed in the left superior temporal gyrus, left Heschl gyrus, and left hippocampus in patients with early-onset T2DM. An FC analysis of these regions revealed elevated connectivity in the right precuneus, left inferior parietal gyrus, left Heschl gyrus, bilateral post-central gyrus, bilateral insula, bilateral superior temporal gyrus, and bilateral medial and paracingulate gyrus. Furthermore, the FC of the hubs to the superior temporal gyrus, insula, and Heschl gyrus was increased and positively correlated with trail making test-B.

Conclusion: Decreased local gray matter volume and increased DC and FC may represent the neurobiological mechanism underlying cognitive dysfunction in patients with early-onset T2DM.

Right Frontoinsular Cortex: A Potential Imaging Biomarker to Evaluate T2DM-Induced Cognitive Impairment

Cognitive impairment in type 2 diabetes mellitus (T2DM) is associated with functional and structural abnormalities in the intrinsic brain network. The salience network (SN) is a neurocognitive network that maintains normal cognitive function, but it has received little attention in T2DM. We explored SN changes in patients with T2DM with normal cognitive function (DMCN) and in patients with T2DM with mild cognitive impairment (DMCI). Sixty-five T2DM patients and 31 healthy controls (HCs) underwent a neuropsychological assessment, independent component analysis (ICA), and voxel-based morphometry (VBM) analysis. The ICA extracted the SN for VBM to compare SN functional connectivity (FC) and gray matter (GM) volume (GMV) between groups. A correlation analysis examined the relationship between abnormal FC and GMV and clinical/cognitive variables. Compared with HCs, DMCN patients demonstrated increased FC in the left frontoinsular cortex (FIC), right anterior insula, and putamen, while DMCI patients demonstrated decreased right middle/inferior frontal gyrus FC. Compared with DMCN patients, DMCI patients showed decreased right FIC FC. There was no significant difference in SN GMV in DMCN and DMCI patients compared with HCs. FIC GMV was decreased in the DMCI patients compared with DMCN patients. In addition, right FIC FC and SN GMV positively correlated with Montreal Cognitive Assessment and Mini-Mental State Examination (MMSE) scores. These findings indicate that changes in SN FC, and GMV are complex non-linear processes accompanied by increased cognitive dysfunction in patients with T2DM. The right FIC may be a useful imaging biomarker for supplementary assessment of early cognitive dysfunction in patients with T2DM.

Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson's Disease: A Histogram Analysis

This study aimed to investigate the value of amplitude of low-frequency fluctuation (ALFF)-based histogram analysis in the diagnosis of Parkinson's disease (PD) and to investigate the regions of the most important discriminative features and their contribution to classification discrimination. Patients with PD (n = 59) and healthy controls (HCs; n = 41) were identified and divided into a primary set (80 cases, including 48 patients with PD and 32 HCs) and a validation set (20 cases, including 11 patients with PD and nine HCs). The Automated Anatomical Labeling (AAL) 116 atlas was used to extract the histogram features of the regions of interest in the brain. Machine learning methods were used in the primary set for data dimensionality reduction, feature selection, model construction, and model performance evaluation. The model performance was further validated in the validation set. After feature data dimension reduction and feature selection, 23 of a total of 1,276 features were entered in the model. The brain regions of the selected features included the frontal, temporal, parietal, occipital, and limbic lobes, as well as the cerebellum and the thalamus. In the primary set, the area under the curve (AUC) of the model was 0.974, the sensitivity was 93.8%, the specificity was 90.6%, and the accuracy was 93.8%. In the validation set, the AUC, sensitivity, specificity, and accuracy were 0.980, 90.9%, 88.9%, and 90.0%, respectively. ALFF-based histogram analysis can be used to classify patients with PD and HCs and to effectively identify abnormal brain function regions in PD patients.

ANALYSIS OF RS-FMRI IMAGES CLARIFIES BRAIN ALTERATIONS IN TYPE 2 DIABETES MELLITUS PATIENTS WITH COGNITIVE IMPAIRMENT

Type 2 Diabetes Mellitus (T2DM) increases the risk of cognitive impairment (CI); however, the underlying pathophysiological mechanisms are still not well understood. We propose to clarify the altered spontaneous brain activity and functional connectivity implicated in CI of T2DM by analyzing resting state functional MRI (rs-fMRI) data. Totally 22 T2DM patients with cognitive impairment (T2DM-CI) and 31 T2DM patients with normal cognition (T2DM-NC) are included in this study. The whole brain amplitude of low frequency fluctuation (ALFF) value, regional homogeneity (ReHo) value and functional connectivity (FC) analysis using posterior cingulate cortex (PCC) as a seed region are investigated through comparison between groups of T2DM-CI and T2DM-NC. It is found that, compared with T2DM-NC, T2DM-CI demonstrates the decreased ALFF in the regions of precuneus, posterior cingulate gyrus, middle occipital gyrus and left superior/middle frontal gyrus, but the increased ALFF in the left middle frontal gyrus and left superior temporal gyrus. In T2DM-CI, ReHo decreases in bilateral posterior cingulate gyrus, right precuneus, right inferior frontal gyrus, but increases in the middle frontal gyrus and right superior occipital gyrus. Higher FC between PCC and bilateral inferior parietal lobule and right middle/inferior frontal gyrus, lower FC between PCC and bilateral precuneus and right superior frontal gyrus are observed in T2DM-CI group. Compared with T2DM-NC, patients with T2DM-CI have presented altered ALFF, ReHo and FC in and between important brain regions. The observed alterations are thought to be implicated with cognitive impairment of T2DM as the potential imaging pathophysiological basis.

Disrupted Topological Organization of the Brain Structural Network in Patients With Thyroid-Associated Ophthalmopathy

Purpose

Increasing evidence indicated that thyroid-associated ophthalmopathy (TAO) might be a neural related disease more than an ocular disease. In this study, we aimed to investigate the alterations of structural brain connectome in patients with TAO.

Methods

Twenty-seven patients with TAO and 27 well-matched healthy controls underwent diffusion tensor imaging. Graph theoretical analyses, including global (shortest path length, clustering coefficient, small-worldness, global efficiency, and local efficiency) and nodal (nodal betweenness, nodal degree, and nodal efficiency) topological properties and network-based statistics were performed to evaluate TAO-related changes in brain network pattern. Correlations were assessed between the network properties and clinical variables, including disease duration, visual acuity, neuropsychiatric measurements, and serum thyroid function indexes.

Results

Compared with healthy controls, patients with TAO exhibited preserved global network parameters but altered nodal properties. We found decreased nodal betweenness and nodal degree in right anterior cingulate and paracingulate gyri, decreased nodal degree and nodal efficiency in the right orbital part of middle frontal gyrus (ORBmid), whereas increased nodal degree and nodal efficiency in the left cuneus. Decrease of structural connectivity strength was found involving the right ORBmid, right putamen, left caudate nucleus, and left medial superior frontal gyrus. Significant correlations were also found between nodal properties and neuropsychological performances as well as visual acuity.

Conclusions

Patients with TAO developed disruption of structural brain network connectome. Disrupted topological organization of the brain structural network may be associated with the clinical-psychiatric dysfunction of patients with TAO.

Aberrant Brain Functional Connectivity Strength and Effective Connectivity in Patients with Type 2 Diabetes Mellitus

Alterations of brain functional connectivity in patients with type 2 diabetes mellitus (T2DM) have been reported by resting-state functional magnetic resonance imaging studies, but the underlying precise neuropathological mechanism remains unclear. This study is aimed at investigating the implicit alterations of functional connections in T2DM by integrating functional connectivity strength (FCS) and Granger causality analysis (GCA) and further exploring their associations with clinical characteristics. Sixty T2DM patients and thirty-three sex-, age-, and education-matched healthy controls (HC) were recruited. Global FCS analysis of resting-state functional magnetic resonance imaging was performed to explore seed regions with significant differences between the two groups; then, GCA was applied to detect directional effective connectivity (EC) between the seeds and other brain regions. Correlations of EC with clinical variables were further explored in T2DM patients. Compared with HC, T2DM patients showed lower FCS in the bilateral fusiform gyrus, right superior frontal gyrus (SFG), and right postcentral gyrus, but higher FCS in the right supplementary motor area (SMA). Moreover, altered directional EC was found between the left fusiform gyrus and bilateral lingual gyrus and right medial frontal gyrus (MFG), as well as between the right SFG and bilateral frontal regions. In addition, triglyceride, insulin, and plasma glucose levels were correlated with the abnormal EC of the left fusiform, while disease duration and cognitive function were associated with the abnormal EC of the right SFG in T2DM patients. These results suggest that T2DM patients show aberrant brain function connectivity strength and effective connectivity which is associated with the diabetes-related metabolic characteristics, disease duration, and cognitive function, providing further insights into the complex neural basis of diabetes.

Altered regional homogeneity and functional brain networks in Type 2 diabetes with and without mild cognitive impairment

Patients with Type-2 Diabetes Mellitus (T2DM) have a considerably higher risk of developing mild cognitive impairment (MCI) and dementia. The initial symptoms are very insidious at onset. We investigated the alterations in spontaneous brain activity and network connectivity through regional homogeneity (ReHo) and graph theoretical network analyses, respectively, of resting-state functional Magnetic Resonance Imaging (rs-fMRI) in T2DM patients with and without MCI, so as to facilitate early diagnose. Twenty-five T2DM patients with MCI (DM-MCI), 25 T2DM patients with normal cognition (DM-NC), 27 healthy controls were enrolled. Whole-brain ReHo values were calculated and topological properties of functional networks were analyzed. The DM-MCI group exhibited decreased ReHo in the left inferior/middle occipital gyrus and right inferior temporal gyrus, and increased ReHo in frontal gyrus compared to the DM-NCs. Significant correlations were found between ReHo values and clinical measurements. The DM-MCI group illustrated greater clustering coefficient/local efficiency and altered nodal characteristics (efficiency, degree and betweenness), which increased in certain occipital, temporal and parietal regions but decreased in the right inferior temporal gyrus, compared to the DM-NCs. The altered ReHo and impaired network organization may underlie the impaired cognitive functions in T2DM and suggesting a compensation mechanism. These rs-fMRI measures have the potential as biomarkers of disease progression in diabetic encephalopathy.

Disrupted brain functional networks in patients with end-stage renal disease undergoing hemodialysis

Patterns of change in whole-brain functional networks remain poorly understood in patients with end-stage renal disease (ESRD) undergoing hemodialysis (HD). We conducted a prospective research to investigate the topological properties of whole-brain functional networks in those patients using a graph-based network analysis. Resting-state functional magnetic resonance imaging was performed on 51 ESRD patients (25 HD and 26 nondialysis patients) and 36 healthy controls (HCs). We compared the topological properties of brain functional networks among the three groups, and analyzed the relationships between those significant parameters and clinical variables in ESRD patients. Progressively disrupted global topological organizations were observed from nondialysis patients to HD patients compared with HCs (all p < 0.05 after Bonferroni correction). HD patients, relative to HCs, showed significantly decreased nodal centralities in the left temporal pole: superior temporal gyrus, bilateral median cingulate and paracingulate gyri, bilateral hippocampus, bilateral parahippocampal gyrus, and bilateral amygdala, and showed increased nodal centralities in the orbital part of the bilateral middle frontal gyrus, left cuneus, and left superior occipital gyrus (all p < 0.05 after Bonferroni correction). Furthermore, nodal centralities in the bilateral hippocampus were significantly decreased in HD patients compared with nondialysis patients (p < 0.05 after Bonferroni correction). Dialysis duration negatively correlated with global efficiency in ESRD patients undergoing HD (r = -0.676, FDR q = 0.004). This study indicates that ESRD patients exhibit disruptions in brain functional networks, which are more severe in HD patients, and these alterations are correlated with cognitive performance and clinical markers.

Altered Functional Hubs and Connectivity in Type 2 Diabetes Mellitus Without Mild Cognitive Impairment

Background: Type 2 diabetes mellitus (T2DM)-related cognitive decline is associated with neuroimaging changes. However, only a few studies have focused on early functional alteration in T2DM prior to mild cognitive impairment (MCI). This study aimed to investigate the early changes of global connectivity patterns in T2DM by using a resting-state functional magnetic resonance imaging (rs-fMRI) technique. Methods: Thirty-four T2DM subjects and 38 age-, sex-, and education-matched healthy controls (HCs) underwent rs-fMRI in a 3T MRI scanner. Degree centrality (DC) was used to identify the functional hubs of the whole brain in T2DM without MCI. Then the functional connectivity (FC) between hubs and the rest of the brain was assessed by using the hub-based approach. Results: Compared with HCs, T2DM subjects showed increased DC in the right cerebellum lobules III-V. Hub-based FC analysis found that the right cerebellum lobules III-V of T2DM subjects had increased FC with the right cerebellum crus II and lobule VI, the right temporal inferior/middle gyrus, and the right hippocampus. Conclusions: Increased DC in the right cerebellum regions III-V, as well as increased FC within cerebellar regions and ipsilateral cerebrocerebellar regions, may indicate an important pathophysiological mechanism for compensation in T2DM without MCI.

Implementation of Imaging Methods in Evaluation of T2DM-Correlated Brain Alterations and Cognitive Dysfunction

Introduction:

There has been mounting evidence that type 2 diabetes mellitus (T2DM) populations are prone to aberrant brain functionality and cognitive deficits. Hyperglycemic status and insulin resistance, among other factors, have been associated with compromised brain neural congruity, leading to lower cognitive performance.

Aim:

The aim of the present paper is to provide a comprehensive review of imaging techniques and their applicability in detection of brain changes in the setting of T2DM.

Methods:

A search of PubMed electronic database was followed. Primary search terms included “imaging methods”, “type 2 diabetes” and “cognitive impairment”.

Results:

A range of imaging modalities that can be of value in depiction of diabetes-mediated structural and functional brain aberrations.

Conclusion:

An increasingly body of research points to the adverse effect T2DM exerts on brain integrity and higher cognitive skills. Findings support the role of imaging techniques in delineation of brain divergence in middle-aged and older diabetic populations.