Predicting the cognitive function status in end-stage renal disease patients at a functional subnetwork scale.

Brain functional networks derived from functional magnetic resonance imaging (fMRI) provide a promising approach to understanding cognitive processes and predicting cognitive abilities. The topological attribute parameters of global networks are taken as the features from the overall perspective. It is constrained to comprehend the subtleties and variances of brain functional networks, which fell short of thoroughly examining the complex relationships and information transfer mechanisms among various regions. To address this issue, we proposed a framework to predict the cognitive function status in the patients with end-stage renal disease (ESRD) at a functional subnetwork scale (CFSFSS). The nodes from different network indicators were combined to form the functional subnetworks. The area under the curve (AUC) of the topological attribute parameters of functional subnetworks were extracted as features, which were selected by the minimal Redundancy Maximum Relevance (mRMR). The parameter combination with improved fitness was searched by the enhanced whale optimization algorithm (E-WOA), so as to optimize the parameters of support vector regression (SVR) and solve the global optimization problem of the predictive model. Experimental results indicated that CFSFSS achieved superior predictive performance compared to other methods, by which the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were up to 0.5951, 0.0281 and 0.9994, respectively. The functional subnetwork effectively identified the active brain regions associated with the cognitive function status, which offered more precise features. It not only helps to more accurately predict the cognitive function status, but also provides more references for clinical decision-making and intervention of cognitive impairment in ESRD patients.

Prognostic value of transthoracic echocardiography score for the prognosis of continuous ambulatory peritoneal dialysis patients.

BACKGROUND: We devoted ourselves to proving that the initial transthoracic echocardiography score (TTES) had predictive significance for patients with continuous ambulatory peritoneal dialysis (CAPD). METHODS: In this retrospective analysis, 274 CAPD patients who had PD therapy were recruited sequentially. TTE exams were performed three months following the start of PD therapy. All patients were divided into two groups based on the strength of their TTES levels. TTES's predictive value for CAPD patients was then determined using LASSO regression and Cox regression. RESULTS: During a median of 52 months, 46 patients (16.8%) died from all causes, and 32 patients (11.7%) died from cardiovascular disease (CV). The TTES was computed as follows: 0.109 × aortic root diameter (ARD, mm) - 0.976 × LVEF (> 55%, yes or no) + 0.010 × left ventricular max index, (LVMI, g/m2) + 0.035 × E/e' ratio. The higher TTES value (≥ 3.7) had a higher risk of all-cause death (hazard ratio, HR, 3.70, 95% confidence index, 95%CI, 1.45-9.46, P = 0.006) as well as CV mortality (HR, 2.74, 95%CI 1.15-19.17, P = 0.042). Moreover, the TTES had an attractive predictive efficiency for all-cause mortality (AUC = 0.762, 95%CI 0.645-0.849) and CV mortality (AUC = 0.746, 95%CI 0.640-0.852). The introduced nomogram, which was based on TTES and clinical variables, exhibited a high predictive value for all-cause and CV mortality in CAPD patients. CONCLUSION: TTES is a pretty good predictor of clinical outcomes, and the introduced TTES-based nomogram yields an accurate prediction value for CAPD patients.

Macroscale neurovascular coupling and functional integration in end-stage renal disease patients with cognitive impairment: A multimodal MRI study.

End-stage renal disease (ESRD) is associated with vascular and neuronal dysfunction, causing neurovascular coupling (NVC) dysfunction, but how NVC dysfunction acts on the mechanism of cognitive impairment in ESRD patients from local to remote is still poorly understood. We recruited 48 ESRD patients and 35 demographically matched healthy controls to scan resting-state functional MRI and arterial spin labeling, then investigated the four types of NVC between amplitude of low-frequency fluctuation (ALFF), fractional ALFF, regional homogeneity, degree centrality, and cerebral blood perfusion (CBF), and associated functional networks. Our results indicated that ESRD patients showed NVC dysfunction in global gray matter and multiple brain regions due to the mismatch between CBF and neural activity, and associated disrupted functional connectivity (FC) within sensorimotor network (SMN), visual network (VN), default mode network (DMN), salience network (SN), and disrupted FC between them with limbic network (LN), while increased FC between SMN and DMN. Anemia may affect the NVC of middle occipital gyrus and precuneus, and increased pulse pressure may result in disrupted FC with SMN. The NVC dysfunction of the right precuneus, middle frontal gyrus, and parahippocampal gyrus and the FC between the right angular gyrus and the right anterior cingulate gyrus may reflect cognitive impairment in ESRD patients. Our study confirmed that ESRD patients may exist NVC dysfunction and disrupted functional integration in SMN, VN, DMN, SN and LN, serving as one of the mechanisms of cognitive impairment. Anemia and increased pulse pressure may be related risk factors.

Left Ventricular Vertical Run-Length Nonuniformity MRI Adds Prognostic Value to MACE in Patients with End-Stage Renal Disease.

BACKGROUND: Vertical run-length nonuniformity (VRLN) is a texture feature representing heterogeneity within native T1 images and reflects the extent of cardiac fibrosis. In uremic cardiomyopathy, interstitial fibrosis was the major histological alteration. The prognostic value of VRLN in patients with end-stage renal disease (ESRD) remains unclear. PURPOSE: To evaluate the prognostic value of VRLN MRI in patients with ESRD. STUDY TYPE: Prospective. POPULATION: A total of 127 ESRD patients (30 participants in the major adverse cardiac events, MACE group). FIELD STRENGTH/SEQUENCE: 3.0 T/steady-state free precession sequence, modified Look-Locker imaging. ASSESSMENT: MRI image qualities were assessed by three independent radiologists. VRLN values were measured in the myocardium on the mid-ventricular short-axis slice of T1 mapping. Left ventricular (LV) mass, LV end-diastolic and end-systolic volume, as well as LV global strain cardiac parameters were measured. STATISTICAL TESTS: The primary endpoint was the incident of MACE from enrollment time to January 2023. MACE is a composite endpoint consisting of all-cause mortality, acute myocardial infarction, stroke, heart failure hospitalization, and life-threatening arrhythmia. Cox proportional-hazards regression was performed to test whether VRLN independently correlated with MACE. The intraclass correlation coefficients of VRLN were calculated to evaluate intraobserver and interobserver reproducibility. The C-index was computed to examine the prognostic value of VRLN. P-value <0.05 were considered statistically significant. RESULTS: Participants were followed for a median of 26 months. VRLN, age, LV end-systolic volume index, and global longitudinal strain remained significantly associated with MACE in the multivariable model. Adding VRLN to a baseline model containing clinical and conventional cardiac MRI parameters significantly improved the accuracy of the predictive model (C-index of the baseline model: 0.781 vs. the model added VRLN: 0.814). DATA CONCLUSION: VRLN is a novel marker for risk stratification toward MACE in patients with ESRD, superior to native T1 mapping and LV ejection fraction. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY STAGE: 2.

Right Ventricular Strain Analysis By Tissue Tracking Cardiac Magnetic Resonance Imaging In Pediatric Patients With End-Stage Renal Disease.

PURPOSE: To investigate right ventricular (RV) volume and mass by cardiac magnetic resonance (CMR) and the added value of tissue tracking strain analysis as markers of RV dysfunction in pediatric patients with end-stage renal disease (ESRD) and preserved RV ejection fraction. MATERIALS AND METHODS: Twenty-five children with ESRD and preserved RVEF (>50%) and 10 healthy control children were enrolled. Tissue tracking CMR was used to assess Global Longitudinal, circumferential (GCS), and radial short and long axes (GRS SAX and GRS LAX) RV strains in the patients group compared with controls. Correlations between strain parameters and other CMR parameters and clinical biomarkers were assessed. Binary logistic regression was used to test the independence of cofounders and detect their significance. RESULTS: RV end-diastolic volume and mass (RVMi) were significantly higher in patients (97.2±19.3 mL/m 2 and 26.6±7gr/m 2 ) than control (71±7.8 mL/m 2 and 11.9±2 gr/m 2 , P values 0.000). All RV global strain parameters were significantly impaired in patients compared with control (all P values <0.05). RV Global Longitudinal was significantly correlated to LVEF (r=-0.416, P =0.039), LVEDVi (r=0.481, P =0.015), LVMi (r=0.562, P =0.004), and systolic blood pressure index (r=0.586, P =0.002). RV GRS (LAX) was significantly correlated to LV GCS (r=-0.462, P =0.020) and LV GRS (SAX) (r=0.454, P =0.023). GRS (SAX) and GCS demonstrated the highest diagnostic accuracy (area under curve: 0.82 and 0.81) to detect strain impairment. Univariate binary logistic regression with patients versus control as dependent variables identified LVMi, RV end-diastolic volume, RVMi, weight, body surface area, RV GCS, RV GRS (LAX), RV GRS (SAX), LV GCS, and LV GRS (SAX) as significantly correlated to patients with ESRD. When adjusted to other cofounders in the multivariable model, only RVMi remained as an independent significant cofounder (Odds ratio:0.395, P =0.046). CONCLUSION: RV global strain, volume, and mass by CMR are markers of RV dysfunction in ESRD pediatric patients with preserved RVEF.

Multi-modal feature selection with self-expression topological manifold for end-stage renal disease associated with mild cognitive impairment.

Effectively selecting discriminative brain regions in multi-modal neuroimages is one of the effective means to reveal the neuropathological mechanism of end-stage renal disease associated with mild cognitive impairment (ESRDaMCI). Existing multi-modal feature selection methods usually depend on the Euclidean distance to measure the similarity between data, which tends to ignore the implied data manifold. A self-expression topological manifold based multi-modal feature selection method (SETMFS) is proposed to address this issue employing self-expression topological manifold. First, a dynamic brain functional network is established using functional magnetic resonance imaging (fMRI), after which the betweenness centrality is extracted. The feature matrix of fMRI is constructed based on this centrality measure. Second, the feature matrix of arterial spin labeling (ASL) is constructed by extracting the cerebral blood flow (CBF). Then, the topological relationship matrices are constructed by calculating the topological relationship between each data point in the two feature matrices to measure the intrinsic similarity between the features, respectively. Subsequently, the graph regularization is utilized to embed the self-expression model into topological manifold learning to identify the linear self-expression of the features. Finally, the selected well-represented feature vectors are fed into a multicore support vector machine (MKSVM) for classification. The experimental results show that the classification performance of SETMFS is significantly superior to several state-of-the-art feature selection methods, especially its classification accuracy reaches 86.10%, which is at least 4.34% higher than other comparable methods. This method fully considers the topological correlation between the multi-modal features and provides a reference for ESRDaMCI auxiliary diagnosis.

Abnormal grey matter structural changes in patients with end-stage kidney disease and mild cognitive impairment: correlations with clinical features.

End-stage kidney disease and mild cognitive impairment (ESKD-MCI) affect the quality of life and long-term treatment outcomes of patients affected by these diseases. Clarifying the morphological changes from brain injuries in ESKD-MCI and their relationship with clinical features is helpful for the early identification and intervention of MCI before it progresses to irreversible dementia. This study gathered data from 23 patients with ESKD-MCI, 24 patients with ESKD and non-cognitive impairment (NCI), and 27 health controls (HCs). Structural magnetic resonance studies, cognitive assessments, and general clinical data were collected from all participants. Voxel-based morphometry analysis was performed to compare grey matter (GM) volume differences between the groups. The patients' GM maps and clinical features were subjected to univariate regression to check for possible correlations. Patients with ESKD-MCI displayed significantly more impairments in multiple cognitive domains, including global cognition, visuospatial and executive function, and memory, compared to patients with ESKD-NCI. Using a more liberal threshold (P < 0.001, uncorrected), we found that compared to patients with ESKD-NCI, patients with ESKD-MCI exhibited clusters of regions with lower GM volumes, including the right hippocampus (HIP), parahippocampal gyrus (PHG), Rolandic operculum, and supramarginal gyrus. The volumes of the right HIP and PHG were negatively correlated with serum calcium levels. ESKD-MCI was associated with a subtle volume reduction of GM in several brain areas known to be involved in memory, language, and auditory information processing. We speculate that these slight morphometric impairments may be associated with disturbed calcium metabolism.

Abnormal white matter along fibers by automated fiber quantification in patients undergoing hemodialysis.

BACKGROUND: Abnormal white matter has been reported in patients with end-stage renal disease (ESRD). However, few studies have investigated the relationship between specific damage segments and cognition in ESRD. This study aimed to delineate white matter alterations in ESRD and its relationship with cognition. METHODS: A total of 36 patients undergoing hemodialysis and 25 healthy controls underwent diffusion tensor imaging (DTI) and a series of neuropsychiatric tests. Automated fiber quantification was used to extract distinct DTI indices, and the relationship between the specific segment of the white matter and clinical properties was investigated. Furthermore, a support vector machine was applied to differentiate patients with ESRD from healthy controls. RESULTS: Fractional anisotropy values decreased in multiple fiber bundles, including bilateral thalamic radiata, cingulum cingulate, inferior fronto-occipital fasciculus (IFOF), uncinate, Callosum_Forceps_Major/Callosum_Forceps_Minor (CFMaj/CFMin), and left uncinate from the tract level in patients with ESRD. Specific damaged segments were detected in 8 fiber bundles, including bilateral thalamic radiation, cingulum cingulate, IFOF, CFMin, and left corticospinal tract. Few alterations in these fiber bundles were correlated with cognition impairment and hemoglobin levels. The tract profiles of the left thalamic radiata and left cingulum cingulate could be used to differentiate hemodialysis patients from healthy controls, with an accuracy of 76.9% and 67.6%, respectively. CONCLUSIONS: This study revealed white matter damage in hemodialysis patients. This damage occurred in specific segments of the tract, especially in the left thalamic radiata and left cingulum cingulate, which might become a new biomarker for patients with ESRD and cognition impairment.

Disrupted network communication predicts mild cognitive impairment in end-stage renal disease: an individualized machine learning study based on resting-state fMRI.

End-Stage Renal Disease (ESRD) is known to be associated with a range of brain injuries, including cognitive decline. The purpose of this study is to investigate the functional connectivity (FC) of the resting-state networks (RSNs) through resting state functional magnetic resonance imaging (MRI), in order to gain insight into the neuropathological mechanism of ESRD. A total of 48 ESRD patients and 49 healthy controls underwent resting-state functional MRI and neuropsychological tests, for which Independent Components Analysis and graph-theory (GT) analysis were utilized. With the machine learning results, we examined the connections between RSNs abnormalities and neuropsychological test scores. Combining intra/inter network FC differences and GT results, ESRD was optimally distinguished in the testing dataset, with a balanced accuracy of 0.917 and area under curve (AUC) of 0.942. Shapley additive explanations results revealed that the increased functional network connectivity between DMN and left frontoparietal network (LFPN) was the most critical predictor for ESRD associated mild cognitive impairment diagnosis. Moreover, hypoSN (salience network) was positively correlated with Attention scores, while hyperLFPN was negatively correlated with Execution scores, indicating correlations between functional disruption and cognitive impairment measurements in ESRD patients. This study demonstrated that both the loss of FC within the SN and compensatory FC within the lateral frontoparietal network coexist in ESRD. This provides a network basis for understanding the individual brain circuits and offers additional noninvasive evidence to comprehend the brain networks in ESRD.

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.

Investigation of the correlation between brain functional connectivity and ESRD based on low-order and high-order feature analysis of rs-fMRI.

BACKGROUND: The lack of analysis of brain networks in individuals with end-stage renal disease (ESRD) is an obstacle to detecting and preventing neurological complications of ESRD. PURPOSE: This study aims to explore the correlation between brain activity and ESRD based on a quantitative analysis of the dynamic functional connectivity (dFC) of brain networks. It provides insights into differences in brain functional connectivity between healthy individuals and ESRD patients and aims to identify the brain activities and regions most relevant to ESRD. METHODS: Differences in brain functional connectivity between healthy individuals and ESRD patients were analyzed and quantitatively evaluated in this study. Blood oxygen level-dependent (BOLD) signals obtained through resting-state functional magnetic resonance imaging (rs-fMRI) were used as information carriers. First, a connectivity matrix of dFC was constructed for each subject using Pearson correlation. Then a high-order connectivity matrix was built by applying the "correlation's correlation" method. Second, sparsification of the high-order connectivity matrix was performed using the graphical least absolute shrinkage and selection operator (gLASSO) model. The discriminative features of the sparse connectivity matrix were extracted and sifted using central moments and t-tests, respectively. Finally, feature classification was conducted using a support vector machine (SVM). RESULTS: The experiment showed that functional connectivity was reduced to some degree in certain brain regions of ESRD patients. The sensorimotor, visual, and cerebellum subnetworks had the highest numbers of abnormal functional connectivities. It is inferred that these three subnetworks most likely have a direct relationship to ESRD. CONCLUSIONS: The low-order and high-order dFC features can identify the positions where brain damage occurs in ESRD patients. In contrast to healthy individuals, the damaged brain regions and the disruption of functional connectivity in ESRD patients were not limited to specific regions. This indicates that ESRD has a severe impact on brain function. Abnormal functional connectivity was mainly associated with the three functional brain regions responsible for visual processing, emotional, and motor control. The findings presented here have the potential for use in the detection, prevention, and prognostic evaluation of ESRD.

Nuclear medicine imaging findings in end-stage renal disease and renal transplant complications.

The aim of this review is to discuss end-stage renal disease (ESRD) and renal transplant complications and present the nuclear medicine imaging findings. The conditions discussed are renal osteodystrophy, metastatic calcification, and renal transplant complications, such as vascular occlusion and acute tubular necrosis. A total of eight nuclear medicine imaging scintigraphy primarily of bone and renal scintigraphy were selected and the imaging features of the complications are discussed. This article highlights the role of nuclear medicine imaging in diagnosis, quantitative and qualitative assessment of renal function, and monitoring of complications associated with ESRD and renal transplant.

Evaluation of chronic renal failure with cone beam computed tomography radiomorphometric indices and fractal analysis in the mandible.

OBJECTIVES: The purpose of this study is to evaluate the mandibular bone structure of patients with chronic renal failure (CRF) and compare to control group via the fractal analysis (FA) and radiomorphometric indices in the cone beam computed tomography (CBCT) images. METHODS: Three observers retrospectively investigated 44 CBCT images to compare patients with CRF to healthy controls. FA was performed in two different areas, volume of interests (VOI) were chosen in the mandibular ramus and corpus. The CT cortical index (CTCI), CT mental index (CTMI), and CT mandibular index (CTI) were performed to determine cortical porosity and thickness. The normality distribution of numerical data was tested using the Kolmogorov-Smirnova and Shapiro-Wilk tests. According to the results, the Mann-Whitney U test and independent group t test were used for parameters. The chi-square test was used to evaluate the distribution of categorical variables by groups. RESULTS: There were statistically significant differences in VOI1 and VOI2. The fractal dimension (FD) values in VOI1 and VOI2 were significantly lower in study group. There were no significant differences in CTCI, CTMI and CTI measurements between both groups. CONCLUSIONS: CRF is a prevalent cause of radiographic abnormalities in jawbones. The FD values in trabecular bone decreased in study group, although there were no significant differences in the radiomorphometric indices. FA in CBCT images could be useful for a three-dimensional evaluation of trabecular bone structure.

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 (T1pre-dialysis ) and at 24 h after dialysis (T2post-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 T2post-dialysis compared with that at T1pre-dialysis . At both T1pre-dialysis and T2post-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 T2post-dialysis than at T1pre-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.

Texture analysis of native T1 images as a novel method for non-invasive assessment of heart failure with preserved ejection fraction in end-stage renal disease patients.

OBJECTIVES: To explore the diagnostic potential of texture analysis applied to native T1 maps obtained from cardiac magnetic resonance (CMR) images for the assessment of heart failure with preserved ejection fraction (HFpEF) among patients with end-stage renal disease (ESRD). METHODS: This study, conducted from June 2018 to November 2020, included 119 patients (35 on hemodialysis, 55 on peritoneal dialysis, and 29 with kidney transplants) in Renji Hospital. Native T1 maps were assessed with texture analysis, using a freely available software package, in participants who underwent cardiac MRI at 3.0 T. Four texture features, selected by dimension reduction specific to the diagnosis of HFpEF, were analyzed. Multivariate logistic regression was performed to examine the independent association between the selected features and HFpEF in ESRD patients. RESULTS: Seventy-six of 119 patients were diagnosed with HFpEF. Demographic, laboratory, cardiac MRI, and echocardiogram characteristics were compared between HFpEF and non-HFpEF groups. The four texture features that were analyzed showed statistically significant differences between groups. In multivariate analysis, age, left atrial volume index (LAVI), and sum average 4 (SA4) turned out to be independent predictors for HFpEF in ESRD patients. Combining the texture feature, SA4, with typical predictive factors resulted in higher C-index (0.923 vs. 0.898, p = 0.045) and a sensitivity and specificity of 79.2% and 95.2%, respectively. CONCLUSIONS: Texture analysis of T1 maps adds diagnostic value to typical clinical parameters for the assessment of heart failure with preserved ejection fraction in patients with end-stage renal disease. KEY POINTS: • Non-invasive assessment of HFpEF can help predict prognosis in ESRD patients and help them take timely preventative measures. • Texture analysis of native T1 maps adds diagnostic value to the typical clinical parameters for the assessment of HFpEF in patients with ESRD.

Abnormal cerebral micro-structures in end-stage renal disease patients related to mild cognitive impairment.

PURPOSE: To investigate the mediating effect of abnormal brain micro-structures on the relationship between clinical risk factors and mild cognitive impairment (MCI), and further predict individual cognitive function in end-stage renal disease (ESRD) patients undergoing maintenance hemodialysis. METHODS: In total, 40 ESRD patients and 30 healthy controls were prospectively enrolled in our study. All subjects completed diffusion kurtosis imaging (DKI) examinations and Montreal cognitive assessment (MoCA) test. Between-group differences in the DKI metrics were analyzed. In addition, the mediating effects of altered brain micro-structures on the association between clinical risk factors and MCI were determined by mediation analysis. Finally, cognitive function was predicted based on DKI metrics and clinical characteristics by applying the optimized least squares support vector regression machine. RESULTS: We observed disrupted brain micro-structures in ESRD patients with MCI, as indicated by significantly altered DKI parameters. Significant correlations were found between the DKI metrics, clinical characteristics, and MoCA scores. In ESRD patients, low hemoglobin level and high serum creatine level were clinical risk factors for MCI. A decreased axial kurtosis value in the left hippocampus may partially mediate the impact of serum creatine on MCI. Furthermore, the prediction model could predict cognitive scores associated with ESRD with relatively high accuracy. CONCLUSION: Aberrant micro-structures partially mediated the association between clinical risk factors and MCI, which is a novel insight into the progression of cognitive dysfunction in ESRD patients. Combined DKI metrics and clinical characteristics could be used as features to efficiently predict cognitive function associated with ESRD.

Classification Algorithms for Brain Magnetic Resonance Imaging Images of Patients with End-Stage Renal Disease and Depression.

This study was aimed to explore the relationship between depression and brain function in patients with end-stage renal disease (ESRD) complicated with depression based on brain magnetic resonance imaging (MRI) image classification algorithms. 30 people in the healthy control group and 70 people in the observation group were selected as the research objects. First, the preprocessing algorithms were applied on MRI images. With the depression classification algorithm based on deep learning, the features were extracted from the capsule network to construct a classification network, and the network structure was compared to obtain the difference in the distribution of brain lesions. Different classifiers and degree centrality, functional connection, low-frequency amplitude ratio, and low-frequency amplitude were selected to analyze the effectiveness of features. In the deep learning method, the neural network model was constructed, and feature extraction and classification network were carried out. The classification layer was based on the capsule network. The results showed that the correct rate of the deep learning feature extraction network structure combined with the capsule network classification was 82.47%, the recall rate was 83.69%, and the accuracy was 88.79%, showing that the capsule network can improve the heterogeneity of depression. The combination of fractional amplitude of low-frequency fluctuation (fALFF), DC, and amplitude of low-frequency fluctuation (ALFF) can achieve the accuracy of 100%. In summary, MRI images showed that patients with depression may have neurological abnormalities in the white matter area. In this study, the classification algorithm based on brain MRI images can effectively improve the classification performance.

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.