Aberrant functional connectome in neurologically asymptomatic patients with end-stage renal disease

MQGA: A quantitative analysis of brain network hubs using multi-graph theoretical indices

Recent advancements in large-scale network studies have shown that connector hubs and provincial hubs are vital for coordinating complex cognitive tasks by facilitating information transfer between and within specialized modules. However, current methods for identifying these hubs often lack standardized measurement criteria, hindering quantitative analysis. This study proposes a novel computational method utilizing multi-graph theoretical index calculations to quantitatively analyze hub attributes in brain networks. Using benchmark network, random simulation network (N = 100), resting fMRI data from the ADHD-200 NYU dataset (HC = 110, ADHD = 146), and the Peking dataset (HC = 120, ADHD = 83), we introduce the Multi-criteria Quantitative Graph Analysis (MQGA) method, which employs betweenness centrality, degree centrality, and participation coefficient to determine the connector (con) hub index and provincial (pro) hub index. The method's accuracy, reliability, and stability were validated through correlation analysis of hub indices and labels, vulnerability tests, and consistency analysis across subjects. Results indicate that as network sparsity increases, the con hub index increases while the pro hub index decreases, with the optimal hub node index at 4 % sparsity. Vulnerability tests revealed that removing con nodes had a greater impact on network integrity than removing pro nodes. Both con and pro exhibited stability in consistency analyses, but con was more stable. The stability of hub scores in disease groups was significantly lower than in the healthy control group. High con values were found in the precuneus, postcentral gyrus, and precentral gyrus, whereas high pro values were identified in the precentral gyrus, postcentral gyrus, superior parietal lobule, precuneus, and superior temporal gyrus. This approach enhances the accuracy and sensitivity of hub node identification, facilitating precise comparisons and producing consistent, replicable results, advancing our understanding of brain network hub nodes, their roles in cognitive processes, and their implications for brain disease research.

The covariant structural and functional neuro-correlates of cognitive impairments in patients with end-stage renal diseases

Introduction

Cognitive impairment (CI) is a common complication of end-stage renal disease (ESRD) that is associated with structural and functional changes in the brain. However, whether a joint structural and functional alteration pattern exists that is related to CI in ESRD is unclear.

Methods

In this study, instead of looking at brain structure and function separately, we aim to investigate the covariant characteristics of both functional and structural aspects. Specifically, we took the fusion analysis approach, namely, multimodal canonical correlation analysis and joint independent component analysis (mCCA+jICA), to jointly study the discriminative features in gray matter volume (GMV) measured by T1-weighted (T1w) MRI, fractional anisotropy (FA) in white matter measured by diffusion MRI, and the amplitude of low-frequency fluctuation (ALFF) measured by blood oxygenation-level-dependent (BOLD) MRI in 78 ESRD patients versus 64 healthy controls (HCs), followed by a mediation effect analysis to explore the relationship between neuroimaging findings, cognitive impairments and uremic toxins.

Results

Two joint group-discriminative independent components (ICs) were found to show covariant abnormalities across FA, GMV, and ALFF (all p < 0.05). The most dominant joint IC revealed associative patterns of alterations of GMV (in the precentral gyrus, occipital lobe, temporal lobe, parahippocampal gyrus, and hippocampus), alterations of ALFF (in the precuneus, superior parietal gyrus, and superior occipital gyrus), and of white matter FA (in the corticospinal tract and inferior frontal occipital fasciculus). Another significant IC revealed associative alterations of GMV (in the dorsolateral prefrontal and orbitofrontal cortex) and FA (in the forceps minor). Moreover, the brain changes identified by FA and GMV in the above-mentioned brain regions were found to mediate the negative correlation between serum phosphate and mini-mental state examination (MMSE) scores (all p < 0.05).

Conclusion

The mCCA+jICA method was demonstrated to be capable of revealing covariant abnormalities across neuronal features of different types in ESRD patients as contrasted to HCs, and joint brain changes may play an important role in mediating the relationship between serum toxins and CIs in ESRD. Our results show the mCCA+jICA fusion analysis approach may provide new insights into similar neurobiological studies.

Characterizing Topological Properties of Brain Functional Networks Using Multi-Threshold Derivative for End-Stage Renal Disease with Mild Cognitive Impairment

Patients with end-stage renal disease (ESRD) experience changes in both the structure and function of their brain networks. In the past, cognitive impairment was often classified based on connectivity features, which only reflected the characteristics of the binary brain network or weighted brain network. It exhibited limited interpretability and stability. This study aims to quantitatively characterize the topological properties of brain functional networks (BFNs) using multi-threshold derivative (MTD), and to establish a new classification framework for end-stage renal disease with mild cognitive impairment (ESRDaMCI). The dynamic BFNs (DBFNs) were constructed and binarized with multiple thresholds, and then their topological properties were extracted from each binary brain network. These properties were then quantified by calculating their derivative curves and expressing them as multi-threshold derivative (MTD) features. The classification results of MTD features were compared with several commonly used DBFN features, and the effectiveness of MTD features in the classification of ESRDaMCI was evaluated based on the classification performance test. The results indicated that the linear fusion of MTD features improved classification performance and outperformed individual MTD features. Its accuracy, sensitivity, and specificity were 85.98 ± 2.92%, 86.10 ± 4.11%, and 81.54 ± 4.27%, respectively. Finally, the feature weights of MTD were analyzed, and MTD-cc had the highest weight percentage of 28.32% in the fused features. The MTD features effectively supplemented traditional feature quantification by addressing the issue of indistinct classification differentiation. It improved the quantification of topological properties and provided more detailed features for diagnosing cognitive disorders.

Predicting cognitive impairment in chronic kidney disease patients using structural and functional brain network: An application study of artificial intelligence

OBJECTIVE

To develop and validate artificial intelligence models for the prediction of cognitive impairment in chronic kidney disease (CKD) patients using structural and functional brain network.

METHODS

This study retrospectively recruited 621 CKD patients and 625 healthy controls in Jinling hospital and 57 CKD patients in Hainan hospital. These CKD patients were divided into cognitive function impairment (CFI) group and non-CFI group based on diagnostic criteria. All patients underwent brain MRI scan, neuropsychological test and laboratory exam. A deep learning model (Attention MLP) based on structural and functional sub-network (determined by the comparison between the patients and healthy controls) topological properties was developed to generate the MRI signature for the discrimination of CFI. Finally, a clinical-topological logistic regression model was built by combining MRI signature and clinical features. The area under curve (AUC), sensitivity and specificity were calculated to evaluate the model performance. Delong test was used to examine the difference of AUCs between models. The integrated discrimination improvement (IDI) and net reclassification index (NRI) between models were calculated.

RESULTS

Attention MLP model performed well in both internal test set and external test set (AUC = 0.744 and 0.763, respectively). After combining with the clinical features, the model performance was further improved both in the internal (AUC: 0.748) and external test sets (AUC: 0.774), while both IDI and NRI were significant (all p < 0.05) in the external test set. According to the comprehensive comparison, the AUC of the Attention MLP model was significantly or marginal significantly higher than that of traditional machine learning models (logistic regression: AUC = 0.634; support vector machine: AUC = 0.613; decision tree: AUC = 0.539; XGBoost: AUC = 0.639) in internal test set. The results showed that the model built on the combining of structural and functional networks data outperformed those on the single network, as well as the connection matrix.

CONCLUSION

The result indicated that the integration of the clinical information and the MRI signature generated by artificial intelligence model based on structural and functional network topological properties could help to predict the CFI of CKD patients effectively. Our results provided a set of quantifiable imaging biomarkers for CFI which may be beneficial to CKD patients.

Static and Dynamic Characteristics of Functional Network Connectivity in Neurologically Asymptomatic Patients Undergoing Maintenance Hemodialysis: A Resting-State Functional MRI Study

BACKGROUND

The characteristics of static functional network connectivity (sFNC) and dynamic FNC (dFNC) in neurologically asymptomatic patients undergoing maintenance hemodialysis are unknown. Elucidating these characteristics may improve our understanding of the mechanisms of neuropathological damage in these patients.

PURPOSE

To explore the static and dynamic characteristics of FNC in neurologically asymptomatic patients undergoing maintenance hemodialysis and the relationship between FNC-related parameters with the neuropsychological scores and blood biomarkers.

STUDY TYPE

Retrospective.

POPULATION

A total of 23 neurologically asymptomatic patients undergoing maintenance hemodialysis and 25 healthy controls matched for age, sex, and years of education.

FIELD STRENGTH/SEQUENCE

A 3.0 T MRI/functional MRI and three-dimensional-T1 structural imaging ASSESSMENT: Independent components; spatial map intensity; sFNC and dFNC strengths; and time attribute parameters (mean dwell time, fractional window, and number of transitions) were determined. Neuropsychological tests were performed. Blood biochemical tests were performed for the patients but not healthy controls.

STATISTICAL TESTS

Chi-squared test, one-sample t-test, two-sample t-test, partial correlation analysis, and family-wise error and false discovery rate correction. P < 0.05 denoted statistical significance.

RESULTS

Significant group differences in the strengths of sFNC and dFNC between networks were found. The sFNC strength between the visual and sensorimotor networks was significantly associated with the global cognitive function score (i.e. the Montreal Cognitive Assessment [MoCA]) (r = 0.606). The sFNC strength between the salience and default mode networks was significantly associated with anxiety scores (r = 0.458). In state 1, positive correlations were found between the mean dwell time and backward digital span task score (r = 0.562), fractional window and MoCA score (r = 0.576), and fractional window and backward digital span task score (r = 0.592).

DATA CONCLUSION

Neurologically asymptomatic patients undergoing maintenance hemodialysis had defective sFNC and dFNC. Our results provide a new perspective on the mechanism of neuropathological damage in patients undergoing maintenance hemodialysis.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 1.

Hypergraph representation of multimodal brain networks for patients with end-stage renal disease associated with mild cognitive impairment

The structure and function of brain networks (BN) may be altered in patients with end-stage renal disease (ESRD). However, there are relatively few attentions on ESRD associated with mild cognitive impairment (ESRDaMCI). Most studies focus on the pairwise relationships between brain regions, without taking into account the complementary information of functional connectivity (FC) and structural connectivity (SC). To address the problem, a hypergraph representation method is proposed to construct a multimodal BN for ESRDaMCI. First, the activity of nodes is determined by connection features extracted from functional magnetic resonance imaging (fMRI) (i.e., FC), and the presence of edges is determined by physical connections of nerve fibers extracted from diffusion kurtosis imaging (DKI) (i.e., SC). Then, the connection features are generated through bilinear pooling and transformed into an optimization model. Next, a hypergraph is constructed according to the generated node representation and connection features, and the node degree and edge degree of the hypergraph are calculated to obtain the hypergraph manifold regularization (HMR) term. The HMR and L₁ norm regularization terms are introduced into the optimization model to achieve the final hypergraph representation of multimodal BN (HRMBN). Experimental results show that the classification performance of HRMBN is significantly better than that of several state-of-the-art multimodal BN construction methods. Its best classification accuracy is 91.0891%, at least 4.3452% higher than that of other methods, verifying the effectiveness of our method. The HRMBN not only achieves better results in ESRDaMCI classification, but also identifies the discriminative brain regions of ESRDaMCI, which provides a reference for the auxiliary diagnosis of ESRD.

Reversal of neurovascular decoupling and cognitive impairment in patients with end-stage renal disease during a hemodialysis session: Evidence from a comprehensive fMRI analysis

Neurovascular (NV) decoupling is a potential neuropathologic mechanism of cognitive impairment in patients with end-stage renal disease (ESRD). Hemodialysis improves cognitive impairment at 24 h post-dialysis, which suggests a potential neuroprotective effect of hemodialysis treatment on the brain. We investigated the effects of hemodialysis treatment on the reversal of NV decoupling associated with cognitive improvement. A total of 39 patients with ESRD and 39 healthy controls were enrolled. All patients were imaged twice during a dialysis session: before hemodialysis (T1_pre-dialysis ) and at 24 h after dialysis (T2_{post-dialysis} ). The healthy controls were imaged once. NV coupling was characterized based on correlation coefficients between four types of blood oxygen level-dependent signals and cerebral blood flow (CBF). A battery of neuropsychological and blood tests was performed before the imaging. Patients with ESRD showed improvements in memory and executive function at T2_{post-dialysis} compared with that at T1_pre-dialysis . At both T1_pre-dialysis and T2_{post-dialysis} , patients with ESRD had lower amplitude of low-frequency fluctuation (ALFF)-CBF coupling than healthy controls. Additionally, patients with ESRD had higher ALFF-CBF coupling at T2_{post-dialysis} than at T1_pre-dialysis . Higher memory scores, higher hemoglobin level, lower total plasma homocysteine level, lower systolic blood pressure variance, and lower ultrafiltration volume were associated with higher ALFF-CBF coupling in patients with ESRD after a hemodialysis session. These findings indicate that partial correction of anemia and hyperhomocysteinemia, stable systolic blood pressure, and fluid restriction may be closely linked to the reversal of NV decoupling and improvement in cognition in patients with ESRD.

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.

K-Means Clustering Algorithm–Based Functional Magnetic Resonance for Evaluation of Regular Hemodialysis on Brain Function of Patients with End-Stage Renal Disease

This research was to evaluate the effects of regular hemodialysis (HD) on the brain function of patients with end-stage renal disease (ESRD). Resting-state functional magnetic resonance imaging (rs-fMRI) based on improved k-means clustering algorithm (k-means) was proposed to scan the brains of 30 regular dialysis patients with end-stage renal disease (ESRD) (experimental group) and 30 normal volunteers (control group). The proposed algorithm was compared with the traditional k-means algorithm and mean shift algorithm and applied to the magnetic resonance scan of patients with ESRD on long-term regular HD. The results showed that the neuropsychological cognitive function (NSCF) evaluation result of the test group was much better than that of the control group, and the difference was statistically obvious (P < 0.05). The results of blood biochemistry, Digit Symbol Substitution Test (DSST), and Montreal Cognitive Assessment Scale (MoCA) in the test group showed no statistical difference compared with those in the control group. The running time of the improved k-means algorithm was dramatically shorter than that of traditional k-means algorithm, showing statistical difference (P < 0.05). Comparison among the improved and traditional k-means algorithm and mean shift algorithm suggested that the improved k-means algorithm showed a lower error rate for image segmentation, and the differences were statistically remarkable (P < 0.05). In conclusion, the improved k-means algorithm showed better time efficiency and the lowest error rate in processing rs-fMRI images than the traditional k-means algorithm and mean shift algorithm, and the effects of regular HD on the brains of patients with ESRD were evaluated effectively.

Fiber connectivity density mapping in end-stage renal disease patients: a preliminary study

Abnormal brain structural connectivity of end-stage renal disease(ESRD) is associated with cognitive impairment. However, the characteristics of cortical structural connectivity have not been investigated in ESRD patients. Here, we study structural connectivity of the entire cerebral cortex using a fiber connectivity density(FiCD) mapping method derived from diffusion tensor imaging(DTI) data of 25 ESRD patients and 20 healthy controls, and between-group differences were compared in a vertexwise manner. We also investigated the associations between these abnormal cortical connectivities and the clinical variables using Pearson correlation analysis and multifactor linear regression analysis. Our results demonstrated that the mean global FiCD value was significantly decreased in ESRD patients. Notably, FiCD values were significantly changed(decreased or increased) in certain cortical regions, which mainly involved the bilateral dorsolateral prefrontal cortex(DLPFC), inferior parietal cortex, lateral temporal cortex and middle occipital cortex. In ESRD patients, we found a trend of negative correlation between the increased FiCD values of bilateral middle frontal gyrus and serum creatinine, urea, parathyroid hormone(PTH) levels and dialysis duration. Only the white matter hyperintensity(WMH) scores were significantly negatively correlated with the global FiCD value in multifactor regression analysis. Our results suggested that ESRD patients exhibited extensive impaired cortical structural connectivity, which was related to the severity of WMHs. A compensation mechanism of cortical structural recombination may play a role in how the brain adapts to maintain optimal network function. Additionally, the serum creatinine, urea and PTH levels may be risk factors for brain structural network decompensation in ESRD patients.

Associations of the Disrupted Functional Brain Network and Cognitive Function in End-Stage Renal Disease Patients on Maintenance Hemodialysis: A Graph Theory-Based Study of Resting-State Functional Magnetic Resonance Imaging

Objective: Cognitive impairment (CI) is a common neurological complication in patients with end-stage renal disease undergoing maintenance hemodialysis (MHD). Brain network analysis based on graph theory is a promising tool for studying CI. Therefore, the purpose of this study was to analyze the changes of functional brain networks in patients on MHD with and without CI by using graph theory and further explore the underlying neuropathological mechanism of CI in these patients. Methods: A total of 39 patients on MHD (19 cases with CI and 20 without) and 25 healthy controls (HCs) matched for age, sex, and years of education were enrolled in the study. Resting-state functional magnetic resonance imaging (rs-fMRI) and T1-weighted high-resolution anatomical data were obtained, and functional brain networks for each subject were constructed. The brain network parameters at the global and regional levels were calculated, and a one-way analysis of covariance was used to compare the differences across the three groups. The associations between the changed graph-theory parameters and cognitive function scores in patients on MHD were evaluated using Spearman correlation analysis. Results: Compared with HCs, the global parameters [sigma, gamma, and local efficiency (Eloc)] in both patient groups decreased significantly (p < 0.05, Bonferroni corrected). The clustering coefficient (Cp) in patients with CI was significantly lower than that in the other two groups (p < 0.05, Bonferroni corrected). The regional parameters were significantly lower in the right superior frontal gyrus, dorsolateral (SFGdor) and gyrus rectus (REC) of patients with CI than those of patients without CI; however the nodal local efficiency in the left amygdala was significantly increased (all p < 0.05, Bonferroni corrected). The global Cp and regional parameters in the three brain regions (right SFGdor, REC, and left amygdala) were significantly correlated with the cognitive function scores (all FDR q < 0.05). Conclusion: This study confirmed that the topology of the functional brain network was disrupted in patients on MHD with and without CI and the disruption of brain network was more severe in patients with CI. The abnormal brain network parameters are closely related to cognitive function in patients on MHD.

Altered resting-state functional networks in patients with hemodialysis: a graph-theoretical based study

Recent studies have demonstrated that hemodialysis patients exhibit disruptions in functional networks with invisible cerebral alterations. We explored the alterations of functional connectivity in hemodialysis patients using the graph-theory method. A total of 46 hemodialysis patients (53.11 ± 1.58 years, 28 males) and 47 healthy controls (55.57 ± 0.86 years, 22 males) were scanned by using resting-state functional magnetic resonance imaging. The brains of these patients were divided into 90 regions and functional connectivity was constructed with the automatic anatomical labeling atlas. In the defined threshold range, the graph-theory analysis was performed to compare the topological properties including global, regional and edge parameters between the hemodialysis and the healthy control groups. Both hemodialysis patients and healthy control subjects demonstrated common small-world property of the brain functional connections. At the global level, the parameters normalized clustering coefficients and small-worldness were significantly decreased in hemodialysis patients compared with those noted in healthy controls. At the regional level, abnormal nodal metrics (increased or decreased nodal degree, betweenness centrality and efficiency) were widely found in hemodialysis patients compared with those of healthy controls. The network-based statistical method was employed and two disrupted neural circuits with 18 nodes and 19 edges (P = 0.0139, corrected) and 10 nodes and 11 edges (P = 0.0399, corrected) were detected. Of note, the edge-increased functional connectivity was associated with the salience network and the frontal-temporal-basal ganglia connection, whereas the edge-decreased functional connectivity was associated with the frontoparietal network. The graph-theory method may be one of the potential tools to detect disruptions of cerebral functional connectivity and provide important evidence for understanding the neuropathology of hemodialysis patients from the disrupted network organization perspective.

Abnormal rich club organization in end-stage renal disease patients before dialysis initiation and undergoing maintenance hemodialysis

Background

End-stage renal disease (ESRD) patients are at a substantially higher risk for developing cognitive impairment compared with the healthy population. Dialysis is an essential way to maintain the life of ESRD patients. Based on previous research, there isn’t an uncontested result whether cognition was improved or worsened during dialysis.

Methods

To explore the impact of dialysis treatment on cognitive performance, we recruited healthy controls (HCs), predialysis ESRD patients (predialysis group), and maintenance hemodialysis ESRD patients (HD group). All ESRD patients performed six blood biochemistry tests (hemoglobin, urea, cystatin C, Na+, K+, and parathyroid hormone). Neuropsychological tests were used to measure cognitive function. By using diffusion tensor imaging and graph-theory approaches, the topological organization of the whole-brain structural network was investigated. Generalized linear models (GLMs) were performed to investigate blood biochemistry predictors of the neuropsychological tests and the results of graph analyses in the HD group and predialysis group.

Results

Neuropsychological analysis showed the HD group exhibited better cognitive function than the predialysis group, but both were worse than HCs. Whole-brain graph analyses revealed that increased global efficiency and normalized shortest path length remained in the predialysis group and HD group than the HCs. Besides, a lower normalized clustering coefficient was found in the predialysis group relative to the HCs and HD group. For the GLM analysis, only the Cystatin C level was significantly associated with the average fiber length of rich club connections in the predialysis group.

Conclusions

Our study revealed that dialysis had a limited effect on cognitive improvement.

Preferentially Disrupted Core Hubs Within the Default-Mode Network in Patients With End-Stage Renal Disease: A Resting-State Functional Magnetic Resonance Imaging Study

Neuroimaging evidence implies that cognitive impairment in patients with end-stage renal disease (ESRD) is related to the disruption of the default-mode network (DMN). The DMN can be divided into three functionally independent subsystems, which include the cortical hub subsystem [consisting of the posterior cingulate cortex (PCC) and the anterior medial prefrontal cortex (aMPFC)], the dorsal medial prefrontal cortex (dMPFC) subsystem, and the medial temporal lobe (MTL) subsystem. However, it is unknown how the functional connectivity (FC) in DMN subsystems is differentially impaired in ESRD. This prospective study was carried out at the Affiliated Hospital of Qingdao University, China, between August 2018 and July 2020. Thirty-two ESRD patients and forty-five healthy controls (HCs) were recruited for this study and received resting-state functional magnetic resonance imaging (rs-fMRI) scanning, and FCs on predefined regions of interest (ROIs) were individually calculated in three DMN subsystems using both ROI- and seed-based FC analyses to examine FC alterations within and between DMN subsystems. The two-sample t-test was used for the comparisons between groups. We also tested the associations between FC changes and clinical information using Pearson's correlation analysis. The results demonstrated that ESRD patients, compared with HCs, exhibit reduced FC specifically within the cortical hubs and between the DMN hubs and two subsystems (the dMPFC and MTL subsystems). Moreover, the FC values between the aMPFC and PCC were positively correlated with creatinine and urea levels in the ESRD patients. Our results suggest that the cortical hubs (PCC and aMPFC) are preferentially disrupted and that other subsystems may be progressively damaged to a certain degree as the disease develops.

Disturbed neurovascular coupling in hemodialysis patients

Background

Altered cerebral blood flow (CBF) and amplitude of low-frequency fluctuation (ALFF) have been reported in hemodialysis patients. However, neurovascular coupling impairments, which provide a novel insight into the human brain, have not been reported in hemodialysis patients.

Methods

We combined arterial spin labeling (ASL) and blood oxygen level dependent (BOLD) techniques to investigate neurovascular coupling alterations and its relationships with demographic and clinical data in 46 hemodialysis patients and 47 healthy controls. To explore regional neuronal activity, ALFF was obtained from resting-state functional MRI. To measure cerebral vascular response, CBF was calculated from ASL. The across-voxel CBF-ALFF correlations for global neurovascular coupling and CBF/ALFF ratio for regional neurovascular coupling were compared between hemodialysis patients and healthy controls. Two-sample t-tests were used to compare the intergroup differences in CBF and ALFF. Multiple comparisons were corrected using a voxel-wise false discovery rate (FDR) method (P < 0.05).

Results

All hemodialysis patients and healthy controls showed significant across-voxel correlations between CBF and ALFF. Hemodialysis patients showed a significantly reduced global CBF-ALFF coupling (P = 0.0011) compared to healthy controls at the voxel-level. Of note, decreased CBF/ALFF ratio was exclusively located in the bilateral amygdala involved in emotional regulation and cognitive processing in hemodialysis patients. In hemodialysis patients, the decreased CBF (right olfactory cortex, anterior cingulate gyrus and bilateral insula) and ALFF (bilateral precuneus and superior frontal gyrus) were mainly located in the default mode network and salience network-related regions as well as increased CBF in the bilateral thalamus.

Conclusions

These novel findings reveal that disrupted neurovascular coupling may be a potential neural mechanism in hemodialysis patients.

Structural and Functional Alterations in Hemodialysis Patients: A Voxel-Based Morphometry and Functional Connectivity Study

Structural and functional brain alterations have been always observed in end-stage renal disease (ESRD) patients undergoing hemodialysis. The present study aimed to investigate the gray matter volume (GMV) changes in hemodialysis patients compared with those noted in healthy subjects, as well as explore the associated functional connectivity alterations based on the abnormal GMV regions. The experiments revealed the effects of regional morphometry aberrance on the brain functional integrity. A total of 46 hemodialysis patients (53.11 ± 1.58 years, 28 males) and 47 healthy subjects (55.57 ± 0.86 years, 22 males) were enrolled in the present study. All subjects underwent high-resolution T1-weighted imaging, resting-state functional MR imaging, and laboratory examinations were performed in hemodialysis patients. The GMV deficits were analyzed using voxel-based morphometry (VBM) and regions with GMV alteration were defined as seeds for functional connectivity analysis. Correlation analyses between significantly different regions and the results of the blood examination were further performed. We found that bilateral thalamus exhibited significantly increased volumes in the hemodialysis patients compared with those of the healthy subjects. However, the bilateral rectus, bilateral caudate, and bilateral temporal gyrus demonstrated significantly decreased volumes. When the regions with GMV alterations were defined as seeds, the hemodialysis patients exhibited decreased integrations in the thalamo-cortical network and within the basal-ganglia connection. The present study revealed the presence of different types of structural and functional brain impairments in hemodialysis patients.

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.

Altered functional connectivity density in the brains of hemodialysis end-stage renal disease patients: An in vivo resting-state functional MRI study

Background

End-stage renal disease (ESRD) patients usually suffer from a high prevalence of central nervous system abnormalities, including cognitive impairment and emotional disorders, which severely influence their quality of life. There have been many neuroimaging research developments in ESRD patients with brain function abnormalities; however, the dysfunction of the salience network (SN) of them has received little attention. The purpose of this study was to investigate the changes of global functional connectivity density (gFCD) in brains of ESRD patients undergoing hemodialysis using resting-state functional magnetic resonance imaging (re-fMRI).

Methods

re-fMRI data were collected from 30 ESRD patients undergoing hemodialysis (14 men, 38.33±7.44 years old) and 30 matched healthy controls (13 men, 39.17±5.7 years old). Neuropsychological tests including the Montreal Cognitive Assessment (MoCA) and Beck Depression Inventory (BDI) were used to evaluate the neurocognitive and psychiatric conditions of the subjects. Blood biochemistry tests, including hemoglobin level, serum albumin level, blood urea level, serum phosphate, serum calcium, and parathyroid hormone level, and dialysis-related indicators, including blood pressure fluctuations in dialysis, single-pool Kt/V(spKt/V), and ultrafiltration volume of dialysis were obtained from the ESRD patients. A two-sample t-test was used to examine the group differences in gFCD between ESRD patients and healthy controls after controlling for age, gender and education.

Results

Compared with healthy controls, ESRD patients exhibited a significantly increased gFCD in the salience network, including the bilateral insula, and dorsal anterior cingulated cortex (dACC), and there was no significant correlation between gFCD and the structural mean grey matter volume in patients for every cluster in the brain regions showing significant different gFCD between the two groups. Furthermore, there were significant negative correlations between the degree of connectivity in the right insula and spKt/V.

Conclusion

Our findings revealed abnormal intrinsic dysconnectivity pattern of salience network-related regions in ESRD patients from the whole brain network perspective. The negative correlation between the right insula and spKt/V suggested that increased fractional removal of urea may reduce the pathological activity in the insula.

Brain network disintegration as a final common pathway for delirium: a systematic review and qualitative meta-analysis

Delirium is an acute neuropsychiatric syndrome characterized by altered levels of attention and awareness with cognitive deficits. It is most prevalent in elderly hospitalized patients and related to poor outcomes. Predisposing risk factors, such as older age, determine the baseline vulnerability for delirium, while precipitating factors, such as use of sedatives, trigger the syndrome. Risk factors are heterogeneous and the underlying biological mechanisms leading to vulnerability for delirium are poorly understood. We tested the hypothesis that delirium and its risk factors are associated with consistent brain network changes. We performed a systematic review and qualitative meta-analysis and included 126 brain network publications on delirium and its risk factors. Findings were evaluated after an assessment of methodological quality, providing N=99 studies of good or excellent quality on predisposing risk factors, N=10 on precipitation risk factors and N=7 on delirium. Delirium was consistently associated with functional network disruptions, including lower EEG connectivity strength and decreased fMRI network integration. Risk factors for delirium were associated with lower structural connectivity strength and less efficient structural network organization. Decreased connectivity strength and efficiency appear to characterize structural brain networks of patients at risk for delirium, possibly impairing the functional network, while functional network disintegration seems to be a final common pathway for the syndrome.

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Delirium is consistently associated with functional network impairments.

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Risk factors are associated with lower structural connectivity strength.

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Risk factors are associated with a less efficient structural network organization.

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Structural impairments make the functional network more vulnerable to deterioration.

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Functional network disintegration seems to be a final common pathway for delirium.

Enhanced Network Efficiency of Functional Brain Networks in Primary Insomnia Patients

Accumulating evidence from neuroimaging studies suggests that primary insomnia (PI) affects interregional neural coordination of multiple interacting functional brain networks. However, a complete understanding of the whole-brain network organization from a system-level perspective in PI is still lacking. To this end, we investigated in topological organization changes in brain functional networks in PI. 36 PI patients and 38 age-, sex-, and education-matched healthy controls were recruited. All participants underwent a series of neuropsychological assessments and resting-state functional magnetic resonance imaging scans. Individual whole-brain functional network were constructed and analyzed using graph theory-based network approaches. There were no significant differences with respect to age, sex, or education between groups (P > 0.05). Graph-based analyses revealed that participants with PI had a significantly higher total number of edges (P = 0.022), global efficiency (P = 0.014), and normalized global efficiency (P = 0.002), and a significantly lower normalized local efficiency (P = 0.042) compared with controls. Locally, several prefrontal and parietal regions, the superior temporal gyrus, and the thalamus exhibited higher nodal efficiency in participants with PI (P < 0.05, false discovery rate corrected). In addition, most of these regions showed increased functional connectivity in PI patients (P < 0.05, corrected). Finally, altered network efficiency was correlated with neuropsychological variables of the Epworth Sleepiness Scale and Insomnia Severity Index in patients with PI. PI is associated with abnormal organization of large-scale functional brain networks, which may account for memory and emotional dysfunction in people with PI. These findings provide novel implications for neural substrates associated with PI.

Aberrant cortical thickness in neurologically asymptomatic patients with end-stage renal disease

PURPOSE

The aim of this study is to investigate the morphology of cortical gray matter in patients with end-stage renal disease (ESRD) and the relationship between cortical thickness and kidney function.

PATIENTS AND METHODS

Three-dimensional high-resolution brain structural magnetic resonance imaging data were collected from 35 patients with ESRD (28 men, 18-61 years old) and 40 age- and gender-matched healthy controls (HCs, 32 men, 22-58 years old). Vertex-wise analysis was then performed to compare the brains of the patients with ESRD with those of HCs to identify abnormalities in the brains of the former. Multiple biochemical measures of renal metabolin, vascular risk factors, general cognitive ability, and dialysis duration were correlated with brain morphometry alterations for the patients.

RESULTS

Patients with ESRD showed lesser cortical thickness than the HCs. The most significant cluster with decreased cortical thickness was found in the right prefrontal cortex (P<0.05, random-field theory correction). In addition, the four local peak vertices in the prefrontal cluster were lateral prefrontal cortex (Peaks 1 and 2), medial prefrontal cortex (Peak 3), and ventral prefrontal cortex (Peak 4). Significant negative correlations were observed between the cortical thicknesses of all four peak vertices and blood urea nitrogen; a negative correlation, between the cortical thickness in three of four peaks and serum creatinine; and a positive correlation, between cortical thickness in the medial prefrontal cortex (Peak 3) and hemoglobin.

CONCLUSION

These results provided compelling evidence for cortical abnormality of ESRD patients and suggested that kidney function may be the key factor for predicting changes of brain tissue structure.

Spatial Disassociation of Disrupted Functional Connectivity for the Default Mode Network in Patients with End-Stage Renal Disease

PURPOSE

To investigate the aberrant functional connectivity of the default mode network (DMN) in patients with end-stage renal disease (ESRD) and their clinical relevance.

MATERIALS AND METHODS

Resting-state functional MRI data were collected from 31 patients with ESRD (24 men, 24-61 years) and 31 age- and gender-matched healthy controls (HCs, 21 men, 26-61years). A whole-brain seed-based functional connectivity analysis of these collected R-fMRI data was performed by locating the seeds in the posterior cingulate cortex (PCC) and ventromedial prefrontal cortex (vmPFC) to investigate the functional connectivity of the posterior and anterior DMN over the whole brain, respectively.

RESULTS

Compared to the HCs, the patients exhibited significantly decreased functional connectivity with the PCC in the left middle temporal gyrus, the right anterior cingulate gyrus, and the bilateral medial superior frontal gyrus. For the vmPFC seed, only the right thalamus showed significantly decreased functional connectivity in the patients with ESRD compared to HCs. Interestingly, functional connectivity between the PCC and right medial superior frontal gyrus exhibited a significantly positive correlation with the hemoglobin level in the patients.

CONCLUSION

Our findings suggest a spatially specific disruption of functional connectivity in the DMN in patients with ESRD, thereby providing novel insights into our understanding of the neurophysiology mechanism that underlies the disease.

Abnormal degree centrality in neurologically asymptomatic patients with end-stage renal disease: A resting-state fMRI study

OBJECTIVE

End-stage renal disease (ESRD), characterized by multi-organ dysfunction, has been shown to co-occur with abnormal brain function. Previous resting-state fMRI studies suggested that regional brain spontaneous activity and functional connectivity within the default mode network are abnormal in ESRD patients. The current study aimed to depict intrinsic dysconnectivity pattern of whole-brain functional networks in voxel level in neurologically asymptomatic patients with ESRD.

METHODS

fMRI datasets were acquired from 22 ESRD patients (without clinical neurological disease) and 29 healthy control (HC) subjects. We investigated the degree centrality for a given element in a network to reveal the changes of functional connectivity throughout the huge human functional network. In the brain regions showing a difference between the HC and ESRD groups, we further conducted receptive operation characteristic (ROC) analyses to confirm the accuracy, sensitivity and specificity of our results.

RESULTS

ESRD patients showed decreased functional connectivity in the left inferior parietal and left precuneus within the brain network; both regions are important components of the default-mode network (DMN). In contrast, patients showed increased connectivity in depression-related regions including bilateral inferior frontal gyrus and right superior temporal gyrus. These regions showed an acceptable accuracy (0.68-0.75), sensitivity (0.64-0.70) and high specificity (0.82-0.96) in distinguishing between the two groups.

CONCLUSIONS

Our findings reveal abnormal intrinsic dysconnectivity pattern of whole-brain functional networks in ESRD patients.

SIGNIFICANCE

Our results could lead to a better understanding of the intrinsic dysconnectivity patterns of default-mode network-related regions in ESRD patients from the whole brain network perspective.

Multimodality MRI Findings in Patients with End-Stage Renal Disease

Patients with end-stage renal disease (ESRD) suffer from a number of complex neurological complications including vascular damage and cognitive dysfunction. It is of great significance to detect the neurological complications and improve the prognosis of ESRD patients. Many new noninvasive MRI techniques have been steadily used for the diagnosis of occult central nervous system complications in ESRD patients. This gives an opportunity to understand the pathophysiological mechanisms of these neurological disorders. This paper is a review that presents the MRI findings of occult brain damage in ESRD patients, outlines the applications of advanced MRI techniques, and introduces a brief perspective in this study field.