Functional-structural relationship in large-scale brain networks of patients with end stage renal disease after kidney transplantation: A longitudinal study

Effect of Dialysis on Structural Brain Connectivity in Patients with End-Stage Renal Disease

INTRODUCTION

Patients with end-stage renal disease (ESRD) are known to have reduced structural and functional brain connectivity in the brain regions associated with cognitive function. However, the effect of dialysis on brain connectivity remains unclear. This study aimed to evaluate the effects of dialysis on structural brain connectivity in patients with ESRD.

METHODS

This prospective study included 20 patients with ESRD in the pre-dialysis stage and 35 healthy controls. The patients underwent T2-weighted and three-dimensional T1-weighted magnetic resonance imaging before and 3 months after dialysis initiation. Moreover, the cortical thickness was calculated. We applied graph theoretical analysis to calculate the structural covariance network based on cortical thickness. We compared the cortical thickness and structural covariance network of patients with ESRD in the pre-dialysis stage with those of healthy controls and with those of patients with ESRD in the post-dialysis stage.

RESULTS

The mean cortical thickness in both hemispheres was lower in patients with ESRD in the pre-dialysis stage than in healthy controls (2.296 vs. 2.354, p = 0.030; 2.282 vs. 2.362, p = 0.004, respectively) and was higher in patients with ESRD in the post-dialysis stage than in those in the pre-dialysis stage (2.333 vs. 2.296, p = 0.001; 2.322 vs. 2.282, p = 0.002, respectively). Analysis of the structural covariance network revealed that the assortative coefficient was lower in patients with ESRD in the pre-dialysis stage than in healthy controls (-0.062 vs. -0.031, p = 0.029) and was higher in patients with ESRD in the post-dialysis stage than in those in the pre-dialysis stage (-0.002 vs. -0.062, p = 0.042).

CONCLUSION

We observed differences in the cortical thickness and structural covariance networks before and after dialysis in patients with ESRD. This indicates that dialysis affects structural brain connectivity, contributing to the understanding of the pathophysiological mechanism of cognitive function alterations resulting from dialysis in patients with ESRD.

Alterations of brain structural and functional connectivity networks topology and decoupling in pediatric myelin oligodendrocyte glycoprotein antibody-associated disease

OBJECTIVE

To investigate the alteration in structural and functional connectivity networks (SCN and FCN) as well as their coupling in pediatric myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), and determine if these properties could serve as potential biomarkers for the disease.

MATERIALS AND METHODS

In total of 32 children with MOGAD and 30 age- and sex-matched healthy controls (HC) were employed to construct the SCN and FCN, respectively. The graph-theoretical analyses of the global properties, node properties of the 90 brain nodes, and the structural-functional connectivity (SC-FC) coupling of the two networks were performed. The graph-theoretical properties that exhibited significant differences were analyzed using partial correlation analysis in conjunction with the clinical scales, including the expanded disability status scale (EDSS), modified Rankin scale (mRS), and pediatric cerebral performance category (PCPC) of the MOGAD group. Subsequently, a machine learning model was developed to discriminate between MOGAD and the HC group, aiming to explore the potential of these properties as biomarkers.

RESULTS

The SCN of the MOGAD group exhibited aberrant global properties, including an increased characteristic path length (Lp) and a decreased global efficiency (Eg), along with reduced nodal properties such as degree centrality (Dc), nodal efficiency (Ne), and local efficiency in multiple nodes. The FCN of the MOGAD group only exhibited decreased Dc, Ne, and betweenness centrality in two nodes of nodal properties. Besides, MOGAD showed a significant decrease in SC-FC coupling compared to the HC group. The analysis of partial correlation revealed significant correlations between several properties and the scales of EDSS and mRS in the MOGAD group. The machine learning method was used to extract six features and establish the model, achieving a classification accuracy of 82.3% for MOGAD.

CONCLUSIONS

Pediatric MOGAD showed a more pronounced impairment in the SCN along with decoupling of SC-FC. Both partial correlation analysis and discriminant modeling suggest that alterations in brain network properties have the potential as biomarkers for assessing brain damage in MOGAD.

Difference in functional connectivity between end-stage renal disease patients with and without restless legs syndrome: A prospective study

PURPOSE

The purpose of this study was to examine differences in functional connectivity between patients with end-stage renal disease (ESRD) with and without restless legs syndrome (RLS). In addition, the study aimed to identify any potential associations between RLS severity and functional connectivity.

METHODS

We enrolled patients with ESRD who had been undergoing hemodialysis. Patients with and without RLS were separated into two groups. Using functional near-infrared spectroscopy (fNIRS) and a graph theory approach, we determined the functional connectivity of patients with ESRD. The data were collected during a 300-s resting state evaluation performed in the dialysis room prior to dialysis.

RESULTS

Eighteen of 48 patients with ESRD were diagnosed with RLS, whereas 30 patients did not exhibit RLS symptoms. Notably, functional connectivity metrics differed significantly between patients with and without RLS. Specifically, patients with ESRD and RLS displayed higher values for mean clustering coefficient (0.474 vs. 0.352, p = 0.001), global efficiency (0.520 vs. 0.414, p = 0.001), strength (6.538 vs. 4.783, p = 0.001), and transitivity (0.714 vs. 0.521, p = 0.001), while values for diameter (5.451 vs. 7.338, p = 0.002), eccentricity (4.598 vs. 5.985, p = 0.004), and characteristic path length (2.520 vs. 3.271, p = 0.002) were lower in patients with ESRD and RLS compared to those without RLS. In addition, there were correlations between the RLS severity score and the assortative coefficient (r = 0.479, p = 0.044), the small-worldness index (r = -0.475, p = 0.046), and transitivity (r = 0.500, p = 0.034).

CONCLUSIONS

We demonstrated differences in functional connectivity between patients with ESRD with and without RLS, which may shed light on the pathophysiology of RLS. Notably, a number of functional connectivity metrics demonstrated strong associations with RLS severity. Our study also confirmed the applicability of fNIRS as a tool for investigating functional connectivity in patients with RLS.

The effects of the dialysis on the white matter tracts in patients with end-stage renal disease using differential tractography study

This study aimed to determine whether white matter tracts correlate with kidney function using correlation tractography, and to investigate the effects of dialysis on white matter tracts in patients with end-stage renal disease (ESRD) using differential tractography. Ten patients with ESRD, who had a glomerular filtration rate of < 15 mL/min/1.73 m2, were enrolled in this prospective study. Diffusion tensor imaging (DTI) was performed both before and after dialysis. We discovered that white matter tracts correlated with the estimated glomerular filtration rate based on pre- and post-dialysis DTI using correlation tractography and investigated the differences in the white matter tracts between pre- and post-dialysis DTI in patients with ESRD using differential tractography. Correlation tractography revealed no quantitative anisotropy of the white matter tracts that correlated with the estimated glomerular filtration rate in pre- and post-dialysis patients with ESRD. Differential tractography revealed significant differences in several white matter tracts, particularly the cingulum, thalamic radiation, corpus callosum, and superior longitudinal fasciculus, between pre- and post-dialysis DTI, which revealed increased diffusion density after dialysis. We demonstrated the significant effects of dialysis on several white matter tracts in patients with ESRD using differential tractography, which showed increased diffusion density after dialysis. In this study, we confirmed the effects of dialysis on brain structure, especially white matter tracts.

The effects of hemodialysis on the functional brain connectivity in patients with end-stage renal disease with functional near-infrared spectroscopy

This study aimed to investigate functional brain connectivity in patients with end-stage renal disease (ESRD) undergoing hemodialysis using functional near-infrared spectroscopy (fNIRS) and to analyze the effect of hemodialysis on functional brain connectivity. We prospectively enrolled patients with ESRD undergoing hemodialysis for > 6 months without any history of neurological or psychiatric disorders. fNIRS data were acquired using a NIRSIT Lite device. Measurements were performed thrice in the resting state for each patient: before the start of hemodialysis (pre-HD), 1 h after the start of hemodialysis (mid-HD), and after the end of hemodialysis (post-HD). We processed and exported all data, and created a weighted connectivity matrix using Pearson correlation analysis. We obtained functional connectivity measures from the connectivity matrix by applying a graph theoretical analysis. We then compared differences in functional connectivity measures according to hemodialysis status in patients with ESRD. We included 34 patients with ESRD. There were significant changes in the mean clustering coefficient, transitivity, and assortative coefficient between the pre- and post-HD periods (0.353 vs. 0.399, p = 0.047; 0.523 vs. 0.600, p = 0.042; and 0.043 vs. - 0.012, p = 0.044, respectively). However, there were no changes in the mean clustering coefficient, transitivity, and assortative coefficient between the pre- and mid-HD periods, or between the mid- and post-HD periods. In addition, there were no significant differences in the average strength, global efficiency, and local efficiency among the pre-, mid-, and post-HD periods. We demonstrated a significant effect of hemodialysis on functional brain connectivity in patients with ESRD. Functional brain connectivity changes more efficiently during hemodialysis.

Functional-structural large-scale brain networks are correlated with neurocognitive impairment in acute mild traumatic brain injury

Background

This study was conducted to investigate topological changes in large-scale functional connectivity (FC) and structural connectivity (SC) networks in acute mild traumatic brain injury (mTBI) and determine their potential relevance to cognitive impairment.

Methods

Seventy-one patients with acute mTBI (29 males, 42 females, mean age 43.54 years) from Nanjing First Hospital and 57 matched healthy controls (HC) (33 males, 24 females, mean age 46.16 years) from the local community were recruited in this prospective study. Resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) were acquired within 14 days (mean 3.29 days) after the onset of mTBI. Then, large-scale FC and SC networks with 116 regions from the automated anatomical labeling (AAL) brain atlas were constructed. Graph theory analysis was used to analyze global and nodal metrics. Finally, correlations were assessed between topological properties and neurocognitive performances evaluated by the Montreal Cognitive Assessment (MoCA). Bonferroni correction was performed out for multiple comparisons in all involved analyses.

Results

Compared with HC, acute mTBI patients had a higher normalized clustering coefficient (γ) for FC (Cohen's d=4.076), and higher γ and small worldness (σ) for SC (Cohen's d=0.390 and Cohen's d=0.395). The mTBI group showed aberrant nodal degree (Dc), nodal efficiency (Ne), and nodal local efficiency (Nloc) for FC and aberrant Dc, nodal betweenness (Bc), nodal clustering coefficient (NCp) and Ne for SC mainly in the frontal and temporal, cerebellum, and subcortical areas. Acute mTBI patients also had higher functional-structural coupling strength at both the group and individual levels (Cohen's d=0.415). These aberrant global and nodal topological properties at functional and structural levels were associated with attention, orientation, memory, and naming performances (all P<0.05).

Conclusions

Our findings suggested that large-scale FC and SC network changes, higher correlation between FC and SC and cognitive impairment can be detected in the acute stage of mTBI. These network aberrances may be a compensatory mechanism for cognitive impairment in acute mTBI patients.

Artificial Intelligence Algorithm-Based MRI in the Diagnosis of Complications after Renal Transplantation

This study was to explore the diagnostic value of magnetic resonance imaging (MRI) optimized by residual segmentation attention dual channel network (DRSA-U-Net) in the diagnosis of complications after renal transplantation and to provide a more effective examination method for clinic. 89 patients with renal transplantation were selected retrospectively, and all underwent MRI. The patients were divided into control group (conventional MRI image diagnosis) and observation group (MRI image diagnosis based on DRSA-U-Net). The accuracy of MRI images in the two groups was evaluated according to the comprehensive diagnostic results. The root mean square error (RMSE) and peak signal-to-noise ratio (PSNR) of DRSA-U-Net on T1WI and T2WI sequences were better than those of U-Net and dense U-Net $\left(P<0.05\right)$ ; comprehensive examination showed that 39 patients had obstruction between ureter and bladder anastomosis, 13 cases had rejection, 10 cases had perirenal hematoma, 5 cases had renal infarction, and 22 cases had no complications; the diagnostic sensitivity, specificity, accuracy, and consistency of the observation group were higher than those of the control group $\left(P<0.05\right)$ . In the control group, the sensitivity, specificity, and accuracy in the diagnosis of complications after renal transplantation were 66.5%, 84.1%, and 78.32%, respectively; in the observation group, the sensitivity, specificity, and accuracy in the diagnosis were 67.8%, 86.7%, and 80.6%, respectively. DRSA-U-Net denoising algorithm can clearly display the information of MRI images on the kidney, ureter, and surrounding tissues, improve its diagnostic accuracy in complications after renal transplantation, and has good clinical application value.

Relationships between Diffusion Tensor Imaging and Resting State Functional Connectivity in Patients with Schizophrenia and Healthy Controls: A Preliminary Study

Schizophrenia is widely seen as a disorder of dysconnectivity. Neuroimaging studies have examined both structural and functional connectivity in the disorder, but these modalities have rarely been integrated directly. We scanned 29 patients with schizophrenia and 25 healthy control subjects, and we acquired resting state fMRI and diffusion tensor imaging. We used the Functional and Tractographic Connectivity Analysis Toolbox (FATCAT) to estimate functional and structural connectivity of the default mode network. Correlations between modalities were investigated, and multimodal connectivity scores (MCS) were created using principal component analysis. Of the 28 possible region pairs, 9 showed consistent (>80%) tracts across participants. Correlations between modalities were found among those with schizophrenia for the prefrontal cortex, posterior cingulate, and lateral temporal lobes, with frontal and parietal regions, consistent with frontotemporoparietal network involvement in the disorder. In patients, MCS correlated with several aspects of the Positive and Negative Syndrome Scale, with higher multimodal connectivity associated with outward-directed (externalizing) behavior and lower multimodal connectivity related to psychosis per se. In this preliminary sample, we found FATCAT to be a useful toolbox to directly integrate and examine connectivity between imaging modalities. A consideration of conjoint structural and functional connectivity can provide important information about the network mechanisms of schizophrenia.

Decreased Neural Connectivity in the Default Mode Network Among Youth and Young Adults With Chronic Kidney Disease

An increasing amount of literature has indicated that chronic kidney disease (CKD) is associated with cognitive deficits that increase with worsening disease severity. Although abnormalities in brain structure have been widely documented, few studies to date have examined the functioning of brain areas associated with the specific cognitive domains affected by CKD (namely, attention and executive functions). Furthermore, few studies have examined functional connectivity among CKD youth who are relatively early in the course of the disease. The present study used functional magnetic resonance imaging to examine the resting state connectivity in 67 youth with CKD (mean age, 17 y) and 58 age-matched healthy controls. Using seed-based multiple regression, decreased connectivity was observed within the anterior cingulate portion of the default mode network. In addition, decreased connectivity within the dorsolateral prefrontal cortex, paracingulate gyrus, and frontal pole were correlated significantly with disease severity. These data indicate that connectivity deficits in circuits implementing attentional processes may represent an early marker for cognitive decline in CKD.

Review of the structural and functional brain changes associated with chronic kidney disease

Chronic kidney disease (CKD) leads to profound metabolic and hemodynamic changes, which damage other organs, such as heart and brain. The brain abnormalities and cognitive deficit progress with the severity of the CKD and are mostly expressed among hemodialysis patients. They have great socio-economic impact. In this review, we present the current knowledge of involved mechanisms.

Functional-structural relationship in large-scale brain networks of patients with end stage renal disease after kidney transplantation: A longitudinal study

It is unclear how the brain network changed after kidney transplantation (KT). We explored the patterns of large‐scale complex network after KT in end‐stage renal disease (ESRD) patients with resting‐state functional MRI (rs‐fMRI) and diffusion tensor imaging (DTI). Twenty‐one ESRD patients (14 men; mean age, 31.5 ± 9.9 years) scheduled for KT and 17 age‐ and gender‐matched healthy controls (HC) (8 men; mean age, 28.9 ± 7.2 years) were enrolled in this study. Each participant underwent rs‐fMRI and DTI scans in three time points (pre‐KT, 1 and 6 months after KT [for ESRD]). Graph theory analysis was used to characterize the topological properties by using functional and structural network connectivities intergroup correlation analysis was performed between functional/structural MR indexes and clinical markers. Compared with HC, pre‐KT ESRD patients showed an altered topological organization in both functional and structural networks. Compared with pre‐KT, increased node degree and node efficiency were observed for both functional and structural networks at 1 month after KT (all p < .05), which were further increased at 6 months after KT (p < .05). Both functional and structural networks did not recover completely at 6 months after KT (all p < .05). The patients showed an increased functional–structural connectivity coupling at 1 month after KT compared with HC (p = .041). A trend of progressive recovery of functional and structural connectivity networks was observed in ERSD patients after KT, which did not recover to the normal levels even in 6 months after KT. The study results underlie cognitive function recovery in ESRD patients following KT in the neuropathophysiological perspective.