Abnormal brain functional networks in end-stage renal disease patients with cognitive impairment

Neuroprotective Effects of Sodium Nitroprusside on CKD-Induced Cognitive Dysfunction in Rats: Role of CBS and Nrf2/HO-1 Pathway

Chronic kidney disease (CKD) is a conceivable new risk factor for cognitive disorder and dementia. Uremic toxicity, oxidative stress, and peripheral-central inflammation have been considered important mediators of CKD-induced nervous disorders. Nitric oxide (NO) is a retrograde neurotransmitter in synapses, and has vital roles in intracellular signaling in neurons. This research aims to determine the effectiveness of NO in CKD-induced cognitive deficits by considering the nuclear factor-erythroid factor 2-related factor 2 (Nrf2)/ heme oxygenase-1 (HO-1) signaling pathway and the important roles of cystathionine beta-synthase (CBS, H2S producing enzyme). Forty rats were divided into four experimental groups: sham, five-sixth (5/6) nephrectomy (5/6Nx, CKD), CKD + NO donor (Sodium nitroprusside, SNP), CKD + SNP and a CBS inhibitor (amino-oxy acetic acid, AOAA). To assess the neurocognitive abilities, eleven weeks after 5/6Nx, behavioral tests (Novel object recognition test, Passive avoidance test, and Barnes maze test) were done. Twelfth week after 5/6Nx, blood urea nitrogen (BUN) and serum creatinine (sCr) levels, as well as the nuclear factor-erythroid factor 2-related factor 2 (Nrf2), heme oxygenase-1 (HO-1) expression levels and neuronal injury in the hippocampus and prefrontal cortex were assessed. As predicted, the levels of BUN and sCr (both P < 0.001) and neuronal injury in the hippocampus (P < 0.001 for CA1; CA3; DG) and prefrontal cortex (P < 0.001) increased in CKD rats as well as 5/6Nx induced reduction of Nrf2 (both P < 0.001) /HO-1(P < 0.001; P < 0.01 respectively) pathway activity in the hippocampus and prefrontal cortex in CKD rats. Moreover, CKD leads to cognitive disorder and memory loss (Novel object recognition test (NOR) (P < 0.001), Passive avoidance test (PA) (P < 0.001) and Barnes maze (BA) (Escape latency (P < 0.001); Error (P < 0.001)). SNP treatment significantly improved Nrf2 (both P < 0.001) /HO-1 (P < 0.001; P < 0.05 respectively) pathways and neuronal injury (P < 0.001 for CA1; CA3; DG) in the hippocampus and prefrontal cortex in CKD rats as well as enhanced learning and memory ability in CKD rats. However, ameliorating effects of SNP on cognitive disorder (NOR (P < 0.05), PA (P < 0.001) and BA (Escape latency (P < 0.05); Error (P < 0.001)) and Nrf2 (P < 0.01; P < 0.001 in the hippocampus and prefrontal cortex respectively) /HO-1 (P < 0.05 in both) signaling pathway activity were nullified by CBS inhibitor and H2S reduction. In conclusion, this study demonstrated that NO improved CKD-induced cognitive impairment and neuronal death which is may be depended to CBS activity and endogenous H2S levels.

Cortical structural network characteristics in non-cognitive impairment end-stage renal disease

Objective

Explore alterations in topological features of gray matter volume (GMV) and structural networks in non-cognitive impairment end-stage renal disease (Non-CI ESRD).

Materials and methods

Utilizing graph theory, we collected structural magnetic resonance imaging (sMRI) data from 38 Non-CI ESRD patients and 50 normal controls (NC). We compared, and extracted the GMV across subject groups, constructed corresponding structural covariance networks (SCNs), and investigated the alterations in SCNs feature parameters between groups.

Results

In Non-CI ESRD patients, The GMV were reduced in several brain regions, predominantly on the left side (p < 0.05, FWE correction). The small-world network characteristics of the patient group's brain networks showed a tendency toward regular. In a few densities, global network parameters, transitivity, (p < 0.05) was significantly increased in the ESRD group. Regional network measurements revealed inconsistent changes in regional efficiency across different brain areas. In the analysis of network hubs, the right temporal pole is likely a compensatory hub for Non-CI ESRD patients. The SCNs in Non-CI ESRD patients demonstrated reduced topological stability against targeted attacks.

Conclusion

This study reveals that patients with renal failure exhibited subtle changes in brain network characteristics even before a decline in cognitive scores. These changes involve compensatory activation in certain brain regions, which enhances network transitivity to maintain the efficiency of whole-brain network information integration without significant loss. Additionally, the SCNs characteristics can serve as a neuroanatomical marker for brain alterations in Non-CI ESRD patients, offering new insights into the mechanisms of early brain injury in ESRD patients.

Abnormal intrinsic brain functional network dynamics in first-episode drug-naïve adolescent major depressive disorder

BACKGROUND

Alterations in brain functional connectivity (FC) have been frequently reported in adolescent major depressive disorder (MDD). However, there are few studies of dynamic FC analysis, which can provide information about fluctuations in neural activity related to cognition and behavior. The goal of the present study was therefore to investigate the dynamic aspects of FC in adolescent MDD patients.

METHODS

Resting-state functional magnetic resonance imaging data were acquired from 94 adolescents with MDD and 78 healthy controls. Independent component analysis, a sliding-window approach, and graph-theory methods were used to investigate the potential differences in dynamic FC properties between the adolescent MDD patients and controls.

RESULTS

Three main FC states were identified, State 1 which was predominant, and State 2 and State 3 which occurred less frequently. Adolescent MDD patients spent significantly more time in the weakly-connected and relatively highly-modularized State 1, spent significantly less time in the strongly-connected and low-modularized State 2, and had significantly higher variability of both global and local efficiency, compared to the controls. Classification of patients with adolescent MDD was most readily performed based on State 1 which exhibited disrupted intra- and inter-network FC involving multiple functional networks.

CONCLUSIONS

Our study suggests local segregation and global integration impairments and segregation-integration imbalance of functional networks in adolescent MDD patients from the perspectives of dynamic FC. These findings may provide new insights into the neurobiology of adolescent MDD.

Altered brain activity in patients with end-stage renal disease: A meta-analysis of resting-state functional imaging

INTRODUCTION

Previous studies have revealed abnormal resting-state brain activity in patients with end-stage renal disease (ESRD); however, the results are inconsistent. Thus, we conducted a coordinate-based meta-analysis of whole-brain resting-state functional neuroimaging studies in ESRD to identify the most consistent neural activity alterations in ESRD patients and explore their relation to serological indicators.

METHODS

A comprehensive literature search strategy was applied to select pertinent studies up to December 2022 in PubMed, Web of Science, and Embase databases. Voxel-wise meta-analysis was conducted via the latest meta-analytic algorithm, seed-based d mapping with permutation of subject images software. Meta-regression analyses were also conducted to explore the potential effect of clinical variables on resting-state neural activity.

RESULTS

Eleven studies comprising 304 patients with ESRD and 296 healthy controls (HCs) were included. Compared with HCs, ESRD patients showed decreased brain activity in the default mode network (DMN) regions, including the bilateral anterior cingulate cortex/medial prefrontal cortex, bilateral midcingulate cortex/posterior cingulate cortex, bilateral precuneus, and right angular gyrus. The neural activities in the bilateral midcingulate cortex, bilateral midcingulate cortex/posterior cingulate cortex, and right angular gyrus were significantly associated with serological indexes including hemoglobin, urea, and creatinine levels.

CONCLUSION

The present study provides a quantitative overview of brain activity alterations in patients with ESRD, and the results confirm the essential role of the DMN in ESRD patients, which may be the potential neural basis of their cognitive deficits. Additionally, some serological indicators may be used as predictive markers for progressive impairment of brain function.

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.

Alterations of amplitude of low-frequency fluctuations and fractional amplitude of low-frequency fluctuations in end-stage renal disease on maintenance dialysis: An activation likelihood estimation meta-analysis

BACKGROUND

Cognitive impairment (CI) is a common complication of end-stage renal disease (ESRD). Many resting-state functional magnetic resonance imaging (rs-fMRI) studies have identified abnormal spontaneous low-frequency brain activity in ESRD dialysis patients. However, these studies have reported inconsistent results. So far, no meta-analyses on this topic have been published. This meta-analysis aimed to identify the more consistently vulnerable brain regions in ESRD patients at rest and to reveal its possible neuropathophysiological mechanisms.

METHODS

We systematically searched PubMed, Cochrane Library, Web of Science, Medline, and EMBASE databases up to July 20, 2022 based on the amplitude of low-frequency fluctuation (ALFF) or fractional amplitude of low-frequency fluctuation (fALFF). Brain regions with abnormal spontaneous neural activity in ESRD compared to healthy controls (HCs) from previous studies were integrated and analyzed using an activation likelihood estimation (ALE) method. Jackknife sensitivity analysis was carried out to assess the reproducibility of the results.

RESULTS

In total, 11 studies (380 patients and 351 HCs) were included in the final analysis. According to the results of the meta-analysis, compared with HCs, ESRD patients had decreased ALFF/fALFF in the right precuneus, right cuneus, and left superior temporal gyrus (STG), while no brain regions with increased brain activity were identified. Jackknife sensitivity analysis showed that our results were highly reliable.

CONCLUSION

Compared to HCs, ESRD dialysis patients exhibit significant abnormalities in spontaneous neural activity associated with CI, occurring primarily in the default mode network, visual recognition network (VRN), and executive control network (ECN). This contributes to the understanding of its pathophysiological mechanisms.

SYSTEMATIC REVIEW REGISTRATION

[https://www.crd.york.ac.uk/prospero/], identifier [CRD42022348694].

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.

Aberrant brain gray matter and functional networks topology in end stage renal disease patients undergoing maintenance hemodialysis with cognitive impairment

Purpose

To characterize the topological properties of gray matter (GM) and functional networks in end-stage renal disease (ESRD) patients undergoing maintenance hemodialysis to provide insights into the underlying mechanisms of cognitive impairment.

Materials and methods

In total, 45 patients and 37 healthy controls were prospectively enrolled in this study. All subjects completed resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion kurtosis imaging (DKI) examinations and a Montreal cognitive assessment scale (MoCA) test. Differences in the properties of GM and functional networks were analyzed, and the relationship between brain properties and MoCA scores was assessed. Cognitive function was predicted based on functional networks by applying the least squares support vector regression machine (LSSVRM) and the whale optimization algorithm (WOA).

Results

We observed disrupted topological organizations of both functional and GM networks in ESRD patients, as indicated by significantly decreased global measures. Specifically, ESRD patients had impaired nodal efficiency and degree centrality, predominantly within the default mode network, limbic system, frontal lobe, temporal lobe, and occipital lobe. Interestingly, the involved regions were distributed laterally. Furthermore, the MoCA scores significantly correlated with decreased standardized clustering coefficient (γ), standardized characteristic path length (λ), and nodal efficiency of the right insula and the right superior temporal gyrus. Finally, optimized LSSVRM could predict the cognitive scores of ESRD patients with great accuracy.

Conclusion

Disruption of brain networks may account for the progression of cognitive dysfunction in ESRD patients. Implementation of prediction models based on neuroimaging metrics may provide more objective information to promote early diagnosis and intervention.

Abnormal Dynamic Functional Connectivity in Patients With End-Stage Renal Disease

Dynamic functional connectivity (FC) analysis can capture time-varying properties of connectivity; however, studies focusing on dynamic FC in patients with end-stage renal disease (ESRD) are very limited. This is the first study to explore the dynamic aspects of whole-brain FC and topological properties in ESRD patients. Resting-state functional magnetic resonance imaging data were acquired from 100 ESRD patients [50 hemodialysis (HD) patients and 50 non-dialysis patients] and 64 healthy controls (HCs). Independent component analysis, a sliding-window approach and graph-theory methods were used to study the dynamic FC properties. The intrinsic brain FC were clustered into four configuration states. Compared with HCs, both patient groups spent longer time in State 3, in which decreased FC between subnetworks of the default mode network (DMN) and between the dorsal DMN and language network was observed in these patients, and a further reduction in FC between the DMN subnetworks was found in HD patients compared to non-dialysis patients. The number of transitions and the variability of global and local efficiency progressively decreased from that in HCs to that of non-dialysis patients to that of HD patients. The completion time of Trail Making Test A and Trail Making Test B positively correlated with the mean dwell time of State 3 and negatively correlated with the number of transitions in ESRD patients. Our findings suggest impaired functional flexibility of network connections and state-specific FC disruptions in patients with ESRD, which may underlie their cognitive deficits. HD may have an adverse effect on time-varying FC.

Multi-Modal Feature Selection with Feature Correlation and Feature Structure Fusion for MCI and AD Classification

Feature selection for multiple types of data has been widely applied in mild cognitive impairment (MCI) and Alzheimer's disease (AD) classification research. Combining multi-modal data for classification can better realize the complementarity of valuable information. In order to improve the classification performance of feature selection on multi-modal data, we propose a multi-modal feature selection algorithm using feature correlation and feature structure fusion (FC2FS). First, we construct feature correlation regularization by fusing a similarity matrix between multi-modal feature nodes. Then, based on manifold learning, we employ feature matrix fusion to construct feature structure regularization, and learn the local geometric structure of the feature nodes. Finally, the two regularizations are embedded in a multi-task learning model that introduces low-rank constraint, the multi-modal features are selected, and the final features are linearly fused and input into a support vector machine (SVM) for classification. Different controlled experiments were set to verify the validity of the proposed method, which was applied to MCI and AD classification. The accuracy of normal controls versus Alzheimer's disease, normal controls versus late mild cognitive impairment, normal controls versus early mild cognitive impairment, and early mild cognitive impairment versus late mild cognitive impairment achieve 91.85 ± 1.42%, 85.33 ± 2.22%, 78.29 ± 2.20%, and 77.67 ± 1.65%, respectively. This method makes up for the shortcomings of the traditional multi-modal feature selection based on subjects and fully considers the relationship between feature nodes and the local geometric structure of feature space. Our study not only enhances the interpretation of feature selection but also improves the classification performance, which has certain reference values for the identification of MCI and AD.

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.

Dynamic functional connectivity changes in the triple networks and its association with cognitive impairment in hemodialysis patients

INTRODUCTION

Cognitive impairment is common in hemodialysis (HD) patients; however, the underlying mechanisms have not been fully understood. The "triple-network model" that consists of the salience network (SN), central executive network (CEN), and default mode network (DMN) has been suggested to play an important role in various cognitive functions. However, dynamic functional connectivity (FC) alterations within the triple networks have not been investigated in HD patients.

METHODS

Sixty-six HD patients and 66 healthy controls (HCs) were included in this study. The triple networks were identified using a group spatial independent component analysis, and dynamic FC was analyzed using a sliding window approach and k-means clustering algorithm. Furthermore, we analyzed the relationships between altered dynamic FC parameters and clinical variables in HD patients.

RESULTS

The intrinsic brain FC within the triple networks was clustered into four configuration states. Compared with HCs, HD patients spent more time in State 1, which was characterized by weak connections between the DMN and CEN and SN. HD patients showed lower number of transitions across different states than HCs. Moreover, the number of transitions and mean dwell time in State 1 were associated with cognitive performance in HD patients.

CONCLUSION

Our findings suggest abnormal dynamic FC properties within the triple networks in HD patients, which may provide new insights into the pathophysiological mechanisms of their cognitive deficits from the perspective of dynamic FC.

Abnormal brain functional networks in end-stage renal disease patients with cognitive impairment

INTRODUCTION

Cognitive impairment (CI) is common in patients with end-stage renal disease (ESRD). Neuroimaging studies have demonstrated structural and functional brain alterations underlying CI in patients with ESRD. However, the patterns of change in whole-brain functional networks in ESRD patients with CI remain poorly understood.

METHODS

We enrolled 66 patients with ESRD (36 patients with CI and 30 patients without CI) and 48 healthy control subjects (HCs). We calculated the topological properties using a graph theoretical analysis. An analysis of covariance (ANCOVA) was used to compare network metrics among the three groups. Moreover, we analyzed the relationships between altered network measures and clinical variables in ESRD patients with CI.

RESULTS

Compared with HCs, both patient groups showed lower local efficiency and small-worldness. ESRD patients had decreased nodal centralities in the default mode regions and right amygdala. Comparison of the two patient groups showed significantly decreased global (small-worldness) and nodal (nodal centralities in the default mode regions) properties in the CI group. Altered nodal centralities in the bilateral medial part of the superior frontal gyrus, left posterior cingulate gyrus, and right precuneus were associated with cognitive performance in the CI group.

CONCLUSION

Disrupted brain functional networks were demonstrated in patients with ESRD, which were more severe in those with CI. Moreover, impaired nodal centralities in the default mode regions might underlie CI in patients with ESRD.