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

Topological regularization of networks in temporal lobe epilepsy: a structural MRI study

Objective

Patients with temporal lobe epilepsy (TLE) often exhibit neurocognitive disorders; however, we still know very little about the pathogenesis of cognitive impairment in patients with TLE. Therefore, our aim is to detect changes in the structural connectivity networks (SCN) of patients with TLE.

Methods

Thirty-five patients with TLE were compared with 47 normal controls (NC) matched according to age, gender, handedness, and education level. All subjects underwent thin-slice T1WI scanning of the brain using a 3.0 T 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 TLE patients.

Results

Although small-world networks were retained, the structural covariance network of TLE patients exhibited topological irregularities in regular architecture as evidenced by an increase in the small world properties (p < 0.001), normalized clustering coefficient (p < 0.001), and a decrease in the transfer coefficient (p < 0.001) compared with the NC group. Locally, TLE patients showed a decrease in nodal betweenness and degree in the left lingual gyrus, right middle occipital gyrus and right thalamus compared with the NC group (p < 0.05, uncorrected). The degree of structural networks in both TLE (Temporal Lobe Epilepsy) and control groups was distributed exponentially in truncated power law. In addition, the stability of random faults in the structural covariance network of TLE patients was stronger (p = 0.01), but its fault tolerance was lower (p = 0.03).

Conclusion

The objective of this study is to investigate the potential neurobiological mechanisms associated with temporal lobe epilepsy through graph theoretical analysis, and to examine the topological characteristics and robustness of gray matter structural networks at the network level.

Abnormal topological parameters in the default mode network in patients with impaired cognition undergoing maintenance hemodialysis

Objective

The role of the default mode network (DMN) in the cognitive impairment experienced by patients with end-stage renal disease (ESRD) undergoing maintenance hemodialysis (MHD) remains unknown. This study tested the hypothesis that the topological architecture of the DMN plays a key role in ESRD-related cognitive impairment.

Methods

For this study, 43 ERSD patients receiving MHD and 41 healthy control (HC) volunteers matched for gender, age and education underwent resting-state functional magnetic resonance imaging examinations. DMN architecture was depicted by 20 selected DMN subregions. Graph theory approaches were applied to investigate multiple topological parameters within the DMN in resting state at the global, local and edge levels.

Results

Globally, the MHD group exhibited topological irregularities as indicated by reduced values for the clustering coeffcient (C_p), normalized C_p (γ), world-index (σ), and local effciency (E_loc) compared with the HC group. Locally, the MHD group showed greater nodal betweenness in the left retrosplenial cortex (RC) compared with the HC group. At the edge level, the MHD group exhibited disconnected resting-state functional connections (RSFCs) in the medial temporal lobe (MTL) subsystem including the ventral medial prefrontal cortex (VMPC)–left posterior inferior parietal lobule, VMPC–right parahippocampal cortex (PC), and right RC–left PC RSFCs. Additionally, the VMPC–right PC RSFC was positively correlated with the Digit Span Test score and E_loc, and the right RC–left PC RSFC was positively correlated with the Montreal Cognitive Assessment score and E_loc in the MHD group.

Conclusions

ESRD patients undergoing MHD showed local inefficiency, abnormal nodal centralities, and hypoconnectivity within the DMN, implying that the functional differentiation and local information transmission efficiency of the DMN are disturbed in ESRD. The disconnected RSFCs in the MTL subsystem likely facilitated topological reconfiguration in the DMN of ESRD patients, leading to impairments of multidomain neurocognition including memory and emotion regulation.

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

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.

Abnormal cortical thickness and structural covariance networks in systemic lupus erythematosus patients without major neuropsychiatric manifestations

BACKGROUND

Non-neuropsychiatric systemic lupus erythematosus (non-NPSLE) has been confirmed to have subtle changes in brain structure before the appearance of obvious neuropsychiatric symptoms. Previous literature mainly focuses on brain structure loss in non-NPSLE; however, the results are heterogeneous, and the impact of structural changes on the topological structure of patients' brain networks remains to be determined. In this study, we combined neuroimaging and network analysis methods to evaluate the changes in cortical thickness and its structural covariance networks (SCNs) in patients with non-NPSLE.

METHODS

We compare the cortical thickness of non-NPSLE patients (N=108) and healthy controls (HCs, N=88) using both surface-based morphometry (SBM) and regions of interest (ROI) methods, respectively. After that, we analyzed the correlation between the abnormal cortical thickness results found in the ROI method and a series of clinical features. Finally, we constructed the SCNs of two groups using the regional cortical thickness and analyzed the abnormal SCNs of non-NPSLE.

RESULTS

By SBM method, we found that cortical thickness of 34 clusters in the non-NPSLE group was thinner than that in the HC group. ROI method based on Destrieux atlas showed that cortical thickness of 57 regions in the non-NPSLE group was thinner than that in the HC group and related to the course of disease, autoantibodies, the cumulative amount of immunosuppressive agents, and cognitive psychological scale. In the SCN analysis, the cortical thickness SCNs of the non-NPSLE group did not follow the small-world attribute at a few densities, and the global clustering coefficient appeared to increase. The area under the curve analysis showed that there were significant differences between the two groups in clustering coefficient, degree, betweenness, and local efficiency. There are a total of seven hubs for non-NPSLE, and five hubs in HCs, the two groups do not share a common hub distribution.

CONCLUSION

Extensive and obvious reduction in cortical thickness and abnormal topological organization of SCNs are observed in non-NPSLE patients. The observed abnormalities may not only be the realization of brain damage caused by the disease, but also the contribution of the compensatory changes within the nervous system.

Multimodal brain imaging effect of "Adjust Zang-fu and Arouse Spirit" electroacupuncture on diabetic cognitive impairment: study protocol of a randomized, sham-controlled pilot trial

BACKGROUND

Diabetic cognitive impairment (DCI) is a serious chronic complication caused by diabetes. The pathogenesis of DCI is complex, but brain nerve injury and brain nerve cell apoptosis are important pathological changes. Multimodal brain imaging is one of the most important techniques to study the neural mechanism of the brain. For the clinical treatment of DCI, there is no effective targeted Western medicine and a lack of clear drug intervention methods. Therefore, there is an urgent need to find effective complementary and alternative methods and clarify their mechanism. This research seeks to explore the multimodal brain imaging effect of "Adjust Zang-fu and Arouse Spirit" electroacupuncture for DCI.

METHODS

This clinical research will be a randomized, sham-controlled pilot trial. Eligible participants will be randomly assigned to the intervention group (n = 60) and the control group (n = 30). The intervention group will be divided into the "Adjust Zang-fu and Arouse Spirit" electroacupuncture group (n = 30) and sham electroacupuncture group (n = 30). All participants will continue to receive routine hypoglycemic therapy. The treatment period is the same in both groups. The primary outcomes include functional magnetic resonance imaging (fMRI), magnetic resonance spectroscopy (MRS), Montreal Cognitive Assessment Scale (MoCA), and Clinical Dementia Rating (CDR). The secondary outcomes include blood glucose and blood lipid tests, Instrumental Activities of Daily Living Scale (IADL), Hachinski Ischemic Scale (HIS), Self-Rating Anxiety Scale (SAS), and Self-Rating Depression Scale (SDS). Outcomes will be assessed at baseline and before and after treatment, and adverse events will be examined. Inter- and intragroup analyses will be performed.

DISCUSSION

This randomized controlled study, combined with multimodal brain imaging techniques and a clinical evaluation scale, was designed to explore the mechanism of "Adjust Zang-fu and Arouse Spirit" electroacupuncture in improving the central nervous system in DCI.

TRIAL REGISTRATION

Chinese Clinical Trial Registration ChiCTR2000040268 . Registered on 26 November 2020.

Identifying Type 2 Diabetic Brains by Investigating Disease-Related Structural Changes in Magnetic Resonance Imaging

Diabetes with high blood glucose levels may damage the brain nerves and thus increase the risk of dementia. Previous studies have shown that dementia can be reflected in altered brain structure, facilitating computer-aided diagnosis of brain diseases based on structural magnetic resonance imaging (MRI). However, type 2 diabetes mellitus (T2DM)-mediated changes in the brain structures have not yet been studied, and only a few studies have focused on the use of brain MRI for automated diagnosis of T2DM. Hence, identifying MRI biomarkers is essential to evaluate the association between changes in brain structure and T2DM as well as cognitive impairment (CI). The present study aims to investigate four methods to extract features from MRI, characterize imaging biomarkers, as well as identify subjects with T2DM and CI.

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

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.

Abnormal Topological Organization of Sulcal Depth-Based Structural Covariance Networks in Parkinson's Disease

Recent research on Parkinson's disease (PD) has demonstrated the topological abnormalities of structural covariance networks (SCNs) using various morphometric features from structural magnetic resonance images (sMRI). However, the sulcal depth (SD)-based SCNs have not been investigated. In this study, we used SD to investigate the topological alterations of SCNs in 60 PD patients and 56 age- and gender-matched healthy controls (HC). SCNs were constructed by thresholding SD correlation matrices of 68 regions and analyzed using graph theoretical approaches. Compared with HC, PD patients showed increased normalized clustering coefficient and normalized path length, as well as a reorganization of degree-based and betweenness-based hubs (i.e., less frontal hubs). Moreover, the degree distribution analysis showed more high-degree nodes in PD patients. In addition, we also found the increased assortativity and reduced robustness under a random attack in PD patients compared to HC. Taken together, these findings indicated an abnormal topological organization of SD-based SCNs in PD patients, which may contribute in understanding the pathophysiology of PD at the network level.

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

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.