Concomitant functional impairment and reorganization in the linkage between the cerebellum and default mode network in patients with type 2 diabetes mellitus

Altered functional connectivity between the default mode network in diabetic retinopathy patients

OBJECTIVES

Previous studies have demonstrated that diabetic retinopathy is associated with cognitive impairment. This study aimed to investigate the intrinsic functional connectivity pattern within the default mode network (DMN) and its associations with cognitive impairment in diabetic retinopathy patients using resting-state functional MRI (rs-fMRI).

METHODS

A total of 34 diabetic retinopathy patients and 37 healthy controls were recruited for rs-fMRI scanning. Both groups were age, gender, and education level matched. The posterior cingulate cortex (PCC) was chosen as the region of interest for detecting functional connectivity changes.

RESULTS

Compared with the healthy control group, diabetic retinopathy patients showed increased functional connectivity between PCC and left medial superior frontal gyrus and increased functional connectivity between PCC and right precuneus.

CONCLUSION

Our study highlights that diabetic retinopathy patients show enhanced functional connectivity within DMN, suggesting that a compensatory increase of neural activity might occur in DMN, which offers new insight into the potential neural mechanism of cognitive impairment in diabetic retinopathy patients.

The language-related cerebro-cerebellar pathway in humans: a diffusion imaging-based tractographic study

Background

The cerebellum and cerebral cortex form the most important cortico-cerebellar system in the brain. However, diffusion magnetic resonance imaging (MRI)-based tractography of the connecting white matter between the cerebellum and cerebral cortex, which support language function, has not been extensively reported on. This work aims to serve as a guideline for facilitating the analysis of white matter tracts along the language-related cerebro-cerebellar pathway (LRCCP), which includes the corticopontine, pontocerebellar, corticorubral, rubroolivary, olivocerebellar, and dentatorubrothalamic tracts.

Methods

The LRCCP templates were developed via processing the high-resolution, population-averaged atlas available in the Human Connectome Project (HCP)-1065 dataset (2017 Q4, 1,200-subject release) in DSI Studio. The deterministic tracking was performed with the manually selected regions of interest (ROIs) on this atlas according to prior anatomic knowledge. Templates were then applied to the MRI datasets of 30 health participants acquired from a single hospital to verify the practicability of the tracking. The diffusion tensor and shape analysis metrics were calculated for all LRCCP tracts. Differences in the tracking metrics between the left and right hemispheres were compared, and the related white matter asymmetry was discussed.

Results

The LRCCP templates were successfully created and applied to healthy participants for quantitative analysis. Significantly higher mean fractional anisotropy (FA) values were discovered on the left (L) corticorubral tract [L, 0.43±0.02 vs. right (R), 0.41±0.02; P<0.01] and left dentatorubrothalamic tract (L, 0.47±0.02 vs. R, 0.46±0.02; P<0.01). Significant differences in tract volume and streamline number were observed between the corticopontine, corticorubral, and dentatorubrothalamic tracts. The size of the right corticopontine and corticorubral tracts were smaller, and both had smaller streamline numbers and innervation areas when compared with the contralateral sides. The R dentatorubrothalamic tract showed a larger volume (R, 23,582.47±4,160.71 mm³ vs. L, 19,821.27±2,983.91 mm³; P<0.01) and innervation area (R, 2,117.37±433.98 mm² vs. L, 1,610.00±356.19 mm²; P<0.01) than did the L side. No significant differences were observed in the rubroolivary tracts.

Conclusions

This work suggests the feasibility of applying tractography templates of the LRCCP to quantitatively evaluate white matter properties associated with language function. Lateralized diffusion metrics were observed in preliminary experiments. LRCCP tractography-based research may provide a potential quantitative method to better understanding neuroplasticity.

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.

Alteration in topological organization characteristics of gray matter covariance networks in patients with prediabetes

OBJECTIVES

Prediabetes is associated with an increased risk of cognitive impairment and neurodegenerative diseases. However, the exact mechanism of prediabetes-related brain diseases has not been fully elucidated. The brain structure of patients with prediabetes has been damaged to varying degrees, and these changes may affect the topological characteristics of large-scale brain networks. The structural covariance of connected gray matter has been demonstrated valuable in inferring large-scale structural brain networks. The alterations of gray matter structural covariance networks in prediabetes remain unclear. This study aims to examine the topological features and robustness of gray matter structural covariance networks in prediabetes.

METHODS

A total of 48 subjects were enrolled in this study, including 23 patients with prediabetes (the PD group) and 25 age-and sex-matched healthy controls (the Ctr group). All subjects' high-resolution 3D T1 images of the brain were collected by a 3.0 Tesla MR machine. Mini-mental state examination was used to evaluate the cognitive status of each subject. We calculated the gray matter volume of 116 brain regions with automated anatomical labeling (AAL) template, and constructed gray matter structural covariance networks by thresholding interregional structural correlation matrices as well as graph theoretical analysis. The area under the curve (AUC) in conjunction with permutation testing was employed for testing the differences in network measures, which included small world parameter (Sigma), normalized clustering coefficient (Gamma), normalized path length (Lambda), global efficiency, characteristic path length, local efficiency, mean clustering coefficient, and network robustness parameters.

RESULTS

The network in both groups followed small-world characteristics, showing that Sigma was greater than 1, the Lambda was much higher than 1, and Gamma was close to 1. Compared with the Ctr group, the network of the PD group showed increased Sigma, Lambda, and Gamma across a range of network sparsity. The Gamma of the PD group was significantly higher than that in the Ctr group in the network sparsity range of 0.12-0.16, but there was no difference between the 2 groups (all P>0.05). The grey matter network showed an increased characteristic path length and a decreased global efficiency in the PD group, but AUC analysis showed that there was no significant difference between groups (all P>0.05). For the network separation measures, the local efficiency and mean clustering coefficient of the gray matter network in the PD group were significantly increased and AUC analysis also confirmed it (P=0.001 and P=0.004, respectively). In addition, network robustness analysis showed that the grey matter network of the PD group was more vulnerable to random damage (P=0.001).

CONCLUSIONS

The prediabetic gray matter network shows an increased average clustering coefficient and local efficiency, and is more vulnerable to random damage than the healthy control, suggesting that the topological characteristics of the prediabetes grey matter covariant network have changed (network separation enhanced and network robustness reduced), which may provide new insights into the brain damage relevant to the disease.