Altered spontaneous brain activities in maintenance hemodialysis patients with cognitive dysfunction and the construction of cognitive function prediction models

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OBJECTIVE: To measure changes in spontaneous brain activity in maintenance hemodialysis patients (MHD) with cognitive impairment (CI) base on resting-state functional magnetic resonance imaging (rs-fMRI) and predict cognitive function in maintenance hemodialysis patients by combining spontaneous brain activity and clinical indicators. METHODS: We selected 50 patients undergoing maintenance hemodialysis at the Second People's Hospital of Changzhou City from September 2020 to December 2021; 28 healthy volunteers were recruited during the same period, and all subjects underwent neuropsychological testing and rs-fMRI. MHD patients were divided into MHD-CI group and MHD-NCI group according to neuropsychological testing score. Data analysis was performed after image preprocessing to explore spontaneous brain activity changes in differential brain regions of MHD-CI patients and to analyze the correlation between spontaneous brain activity and clinical variables. Back propagation neural network (BPNN) was used to predict cognitive function. RESULTS: Compared with the MHD-NCI group, the patients with MHD-CI had more severe anemia and higher urea nitrogen levels, the lower mALFF values in the left postcentral gyrus, lower mfALFF values in the left inferior temporal gyrus, and greater mALFF values in the right caudate nucleus (p < 0.05). Correlation analysis showed that the mALFF values in the left postcentral gyrus of MHD patients were significantly positively correlated with hemoglobin levels (r = 0.551, p = 0.000) and MOCA scores (r = 0.457, p = 0.001), negatively correlated with urea nitrogen (r = –0.519, p = 0.000). left temporal inferior gyrus mfALFF values were significantly negatively correlated with urea nitrogen levels (r = –0.523, p = 0.000) and positively correlated with MOCA scores (r = 0.295, p = 0.038). The right caudate nucleus mALFF values were negatively correlated with MOCA scores (r = -0.455, p = 0.001). Based on quantifiable influencing factors, we construct different BPNN prediction models, indicating that the diagnostic efficacy of the model which inputs were hemoglobin, urea nitrogen and mALFF value in the left central posterior gyrus is optimal(R 2 =0.8054). CONCLUSION : In summary, the left inferior temporal gyrus and left postcentral gyrus might be the critical regions affecting cognitive function in MHD-CI patients, and correction of anemia and adjustment of urea nitrogen levels might help prevent CI in MHD patients. Combined with rs-fMRI not only reveals the neurophysiological mechanism of cognitive impairment, but also can serves as a neuroimaging marker for the diagnosis and evaluation of cognitive impairment in patients with MHD. resting-state magnetic resonance imaging maintenance hemodialysis cognitive dysfunction low-frequency amplitude ratiometric low-frequency amplitude regional homogeneity Back propagation neural network Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background At present, the number of dialysis patients is increasing rapidly, and the treatment period is long, and various complications can occur during the treatment, among which cognitive impairment (CI) is one of the common complications among maintenance hemodialysis (MHD) patients, with an incidence of 6.6%-51%[ 1 ]. It primarily manifesting as impaired executive ability, impaired attention, memory impairment, and slow motor performance. These deficits reduce compliance and quality of life and negatively affect outcomes[ 2 , 3 ]. The primary means of assessing cognitive function is the neuropsychological scale which is subjective and poorly completed by the patient, and therefore easily overlooked and hinders the early identification of cognitive impairment. Resting-state functional magnetic resonance imaging (rs-fMRI) evaluates neural networks by measuring blood-oxygen-level-dependent signals at rest. It has been used to investigate the pattern of neuropathological alterations in MHD patients[ 4 – 7 ]. Low-frequency amplitude (ALFF), ratio low-frequency amplitude (fALFF), and regional homogeneity (ReHo) are three commonly used rs-fMRI methods to quantify neural activity. ALFF and fALFF reflect the functional intensity of local brain regions, reflecting the signal of each voxel in the 0.01–0.08 Hz frequency band[ 8 ]. ReHo measures the regional coherence of neural activity between adjacent voxels[ 9 ]. Several studies applied rs-fMRI methods to explore changes in spontaneous brain activity in MHD patients and found that reduced ALFF/fALFF/ReHo values in the default mode network (DMN) region were associated with cognitive decline[ 4 , 5 , 10 , 11 ]. However, these studies did not focus on brain activity changes in MHD patients with CI. It is known that the mechanisms of cognitive dysfunction in maintenance hemodialysis patients are directly or indirectly related to various risk factors such as neurological damage due to uremic toxin accumulation, hypoglobinemia due to anemia and malnutrition, hemodynamic fluctuations, and electrolyte imbalance due to long-term hemodialysis[ 12 ]. Therefore, this study aim to explore comprehensively the critical brain regions related to cognitive function in combination with resting-state functional MRI, illustrate the neurophysiological mechanisms underlying the occurrence of cognitive impairment in MHD-CI patients, and to avoid interference from over-confounding factors, we divided hemodialysis patients into two groups based on MOCA scores, analyzed clinical factors related to cognitive function in a cohort of maintenance hemodialysis patients with the same risk factor, and analyzed the correlation between them for early prevention. At present, the early identification of CI is mostly based on the judgment of clinical experience, without quantitative evaluation criteria, and the risk of CI cannot be accurately predicted. In order to further improve the artificial decision-making process, the construction and application of disease prediction model play an important reference value for disease screening, and prediction model has become a research hotspot in the field of MCI risk prediction. This study will use the Back propagation neural network (BPNN) to predict cognitive function, and a large number of experimental tests and theoretical studies have shown that the BPNN algorithm is a kind efficient learning algorithms, which has been shown to have better diagnostic efficacy than the logistic regression model in several fields, it has good self-learning and adaptive ability[ 13 – 15 ]. In this study, the mean ALFF/ fALFF/ ReHo values of the brain regions with significant between-group difference were used as neuroimaging markers, and clinical indicators related to cognitive function were incorporated into the BPNN to comprehensively assess the cognitive function of maintenance hemodialysis patients and facilitate early diagnosis. Moreover, this is the first study to use BPNN to predict cognitive function in combination with rs-fMRI and clinical indicators, providing clinical guidance for early diagnosis of cognitive function. Objectives And Methods Research subjects This study included 55 patients undergoing regular maintenance hemodialysis at Changzhou Second People's Hospital from September 2020 to December 2021. Inclusion criteria were (1) dialysis age of 6 months or more, (2) age between 18 and 60 years, and (3) right-handedness. Thirty healthy individuals matched for age, sex, and education were recruited as controls. Exclusion criteria were (1) presence of psychiatric or neurological disorders, including brain tumor, traumatic brain injury, stroke, schizophrenia, and epilepsy, (2) inability to complete neuropsychological scales, (3) chronic advanced liver failure or heart failure, (4) history of drug and alcohol dependence, (5) type 2 diabetes, depression, and sleep disorders; (6) contraindications to MRI scanning, and (7) head movement artifacts interfering with experimental data acquisition or measurement. According to the exclusion criteria, five MHD patients were excluded (three with motion artifacts in data measurement and two with lacunar cerebral infarction lesions), and two controls were excluded due to incomplete data processing. The study group comprised 30 MHD-CI patients ([18 males, 12 females], age 49.53 ± 8.23 years), 20 MHD-NCI ([15 males, 5 females], age 47.00 ± 10.87 years), and 28 controls ([14 males, 14 females], age 47.57 ± 10.22] years). The study was approved by the Ethics Committee of the Second People's Hospital of Changzhou City (KY032-01). Research Methodology Laboratory tests Blood tests were performed on all subjects within 24h prior to imaging, including erythrocytes, leukocytes, hemoglobin, erythrocyte pressure product, albumin, LDL, triglycerides, serum creatinine, blood urea nitrogen, blood uric acid, serum potassium, serum sodium, serum calcium, serum phosphorus, parathyroid hormone, and urea clearance index (Kt/V). Neuropsychological Scales All subjects completed the neuropsychological scales 1 h before MRI. The Montreal cognitive assessment (MOCA) was used to assess overall cognitive function, with dimensions including visuospatial, executive ability, memory, naming, attention, language, abstract thinking, and orientation. The total score is 30 points; when the subject has less than 12 years of education plus one point, less than 26 years is cognitive impairment.[ 16 ] The test was administered by professionally trained personnel. Test administrators were professionally trained, and the testing environment was quiet. Mri Acquisition T2-FLAIR images, 3D-T1-weighted imaging (T1WI), and rs-fMRI images were acquired in all subjects using a GE Discovery MR 750W 3.0T scanner; rs-fMRI was performed after dialysis. Patients were asked to close their eyes, relax, and avoid thinking during the scan. The T2-FLAIR was used to exclude organic lesions; rs-fMRI sequential scanning was performed with machine scan parameters: repetition time (TR) = 2000 ms, echo time (TE) = 40 ms, flip angle (FA) = 90°, matrix = 64 × 64, field of view (FOV) 240 mm × 240 mm, and slice thickness = 6 mm. High-resolution whole-brain T1WI structural images were obtained using a three-dimensional (3D) brain volumetric imaging (3D BRAVO) sequence. Scanning parameters were TR = 8.2 ms, TE = 3.2 ms, layer thickness = 1.2 mm, layer interval = 0 mm, flip angle = 12°, matrix = 256×256, FOV = 240 mm×240 mm, and number of layers scanned = 152 slices. Mri Data Analysis Data preprocessing was performed using the DPARSF ( http://rfmri.org/DPARSF ) toolkit based on the MATLAB 2018a ( https://www.mathworks.com/ ) platform with the following steps: (1) conversion of DICOM format to NFITI format; (2) removal of the first ten time points; (3) time layer and head motion correction; (4) spatial normalization using T1 joint segmentation, Montreal Neurological Institute (MNI) template and resampling voxel size of 3 mm × 3 mm × 3 mm; (5) regression of head motion, brain white matter signal, cerebrospinal fluid signal, and global signal as covariates. Then, ALFF and fALFF values were analyzed follow the steps below, and a Gaussian smoothing kernel with a full-width half-height value of 4 mm was used to perform spatial smoothing and remove low-frequency linear drift. The average amplitude value of the low-frequency amplitude of the brain (0.01–0.08 Hz) was extracted, and the fALFF value was obtained by dividing the sum of ALFF values in this frequency band by the sum of amplitudes in the full frequency band. Normalization was performed to obtain the average ALFF/fALFF (mALFF/mfALFF) as a parameter for further statistical analysis; reHo maps were generated before spatial smoothing, and the normalized images were filtered (0.01–0.08 Hz) and normalized before spatial smoothing. The ReHo maps were obtained as ReHo parameters for statistical analysis. Statistical analysis For baseline data, SPSS 24.0 ( https://www.ibm.com/cn-zh/products/spss-statistics ) was used for statistical analysis. Qualitative data (gender) were expressed as frequency and compared between groups using the χ 2 test; quantitative data conforming to a normal distribution were compared between groups using the independent samples t-test or analysis of variance, expressed as‾X \(\pm\) S. Quantitative data with skewed distribution were analyzed using the Mann-Whitney U-test or Kruskal-Wallis test, expressed as M (Q1, Q3). If significant differences were found in the comparisons between the three groups, the least significant difference method was used to assess the differences between functional data. P < 0.05 was considered a statistically significant difference. Data is z-scored standardized using MATLAB software prior to correlation analysis. Comparisons of ALFF/fALFF/ReHo values among the three groups were performed as follows: differences between groups were calculated using the DPABI ( http://www.rfmri.org/dpabi)toolbo x; analysis of variance was performed with post hoc tests using the least significant difference method; corrected p-values were calculated for comparing any pair; p-mapping was converted to Z-mapping; a Gaussian random field correction was performed using Z-mapping to correct for multiple comparisons, with statistical thresholds set at the voxel level at p < 0.001 and at the cluster level (two-tailed) set to p < 0.05. All coordinates were reported in Montreal Neurological Institute space. Correlation analysis and was performed as follows: Pearson correlation analysis was performed between significantly different mALFF/mfALFF/mReHo values and clinical variables extracted from MHD-CI patients and MHD-NCI group. P < 0.05 was considered a statistically significant difference. Back propagation neural network (BPNN) was used to predict cognitive function. Results Demographic and clinical characteristics There were no significant differences in age, gender, or education of the subjects across groups (p > 0.05). MOCA scores were significantly lower in the MHD-CI group than in the MHD-NCI and control groups (p = 0.000); the difference in MOCA scores between the HC and MHD-NCI groups was not statistically significant (p = 0.148). Compared to the control group, MHD-CI patients had significantly lower erythrocytes, hemoglobin, albumin, and higher urea nitrogen, creatinine, phosphorus, and parathyroid hormone (p < 0.05). Compared to the MHD-NCI group, MHD-CI patients had significantly lower hemoglobin (p = 0.009) and significantly higher urea nitrogen levels (p = 0.000), with no significant differences between the remaining clinical indicators. The details are presented in Table 1 . Table 1 General information and laboratory tests for the three groups of subjects. MHD-CI: maintenance hemodialysis patients with cognitive impairment; MHD-NCI: maintenance hemodialysis patients without cognitive impairment; HC: healthy controls. MOCA: Montreal cognitive assessment scale, Kt/V: dialysis effectiveness index. A: Independent samples t-test; B: Mann-whitney U test; C: test of variance (ANOVA); D: Kruskal Wallis tes t; χ 2 : chi-square test. &: statistically significant difference in the MHD-CI group/MHD-NCI group compared to HC; #: statistically significant difference in the MHD-CI group compared to the MHD-NCI group. Protocols MHD-CI (n = 30) MHD-NCI (n = 20) HC (n = 28) P value Age (years) 49.53 ± 8.23 47.00 ± 10.87 47.57 ± 10.22 0.610 C Gender (male/female) 18/12 15/5 14/14 0.229 χ2 Years of education (years) 9.00(5.75-9.00) 9.00(8.25-9.00) 8.00(6.00–9.00) 0.211 D MOCA (points) 23.00(17.00–24.00) &# 28.00(27.00–29.00) 26.00(26.00–27.00) 0.000 D Duration of dialysis (years) 3.00(1.00-4.75) 1.50(1.00–3.00) - 0.270 B Leukocytes (×10 9 /L) 5.91 ± 2.28 5.85 ± 1.66 5.73 ± 1.98 0.946 C Red blood cells (×10 9 /L) 3.44 ± 0.50 & 3.60 ± 0.45 & 4.46 ± 0.60 0.000 C Hemoglobin (g/L) 96.16 ± 15.7323 &# 112.95 ± 9.25 & 116.07 ± 7.91 0.000 C Hematocrit (%) 32.90(29.85–36.37) 33.00(28.92-35.00) 34.00(32.50–36.70) 0.191 D Albumin (g/L) 37.85(34.57–40.70) & 39.15(36.00-42.47) & 42.20(39.97–45.70) 0.000 D Glucose (mmol/L) 4.84 (4.50–5.51) 4.74 (4.38–5.62) 5.05 (4.43–6.42) 0.530 D Blood Urea nitrogen (mmol/L) 30.691 ± 6.17 &# 21.19 ± 7.39 & 5.67 ± 2.12 0.000 C Creatinine (µmol/L) 787.21 ± 342.55 & 904.32 ± 481.21 & 61.28 ± 10.30 0.000 C Uric acid (µmol/L) 313.69 ± 154.20 351.03 ± 152.37 290.11 ± 91.21 0.307 C Cholesterol (mmol/L) 4.05 ± 1.25 3.79 ± 1.03 3.91 ± 0.69 0.680 C Triglycerides (mmol/L) 1.45 ± 0.62 1.64 ± 1.13 1.26 ± 0.51 0.238 C Blood sodium (mmol/L) 140.08 ± 2.73 139.81 ± 2.26 139.55 ± 2.96 0.762 C Blood potassium (mmol/L) 4.11 (3.80–4.74) 4.35 (3.86–4.68) 4.10 (3.5–4.56) 0.386 D Blood calcium (mmol/L) 2.19 (2.04–2.36) 2.16 (2.08–2.34) 2.42 (1.94–2.68) 0.765 D Blood phosphorus (mmol/L) 1.94 (1.44–2.37) & 1.80 (1.61–2.04) & 1.18 (0.85–1.80) 0.002 D Parathyroid hormone (ng/L) 295.45(136.70-453.72) & 288.50(128.27-405.82) & 22.15(17.47–42.27) 0.000 D Kt/V (ml-s-1/1.73m2) 1.51 ± 0.06 1.54 ± 0.09 - 0.259 A Results Of Malff In Brain Regions Among The Three Groups In the MHD-CI group, mALFF values were significantly higher in the left fusiform gyrus, left parahippocampal gyrus, right hippocampus, left caudate nucleus, and right caudate nucleus and lower values in the left postcentral gyrus than in the control group. Compared with the MHD-NCI, the MHD-CI group had significantly higher mALFF values in the right caudate nucleus and significantly lower mALFF values in the left posterior central gyrus. The details are presented in Table 2 , Figs. 1 and 2 . Table 2 Significant differences in mALFF values among groups. mALFF: mean low frequency amplitude; MHD-CI: maintenance hemodialysis patients with cognitive impairment; MHD-NCI: maintenance hemodialysis patients without cognitive impairment; HC: healthy controls. MNI, Montreal Neurological Institute. Condition Brain Regions X MNI Y Z Voxel size Z value P value MHD-CI > MHD-NCI right caudate nucleus 18 18 42 11 3.441 HC Left fusiform gyrus -30 -9 -33 31 3.702 < 0.001 left parahippocampal gyrus -18 0 -21 27 4.137 < 0.001 Right hippocampus 21 -24 -12 34 4.634 < 0.001 Left caudate nucleus -12 15 24 26 4.321 < 0.001 right caudate nucleus 15 18 12 16 3.728 < 0.001 MHD-CI < MHD-NCI Left postcentral gyrus -45 -27 51 11 -3.678 < 0.001 MHD-CI < HC Left postcentral gyrus -51 -33 48 19 -3.821 HC Right hippocampus 30 -21 -18 11 3.534 < 0.001 Results Of Mfalff In Brain Regions Among The Three Groups The left medial superior frontal gyrus mfALFF was significantly lower in the MHD-CI and MHD-NCI groups than in the control group, and the left inferior temporal gyrus mfALFF values were significantly lower in the MHD-CI group than in the MHD-NCI group, and No significant differences were seen in the other paired groups. The details are presented in Table 3 and Fig. 3 . Table 3 Significant differences in mfALFF values among groups. MHD-CI: maintenance hemodialysis patients with cognitive impairment; MHD-NCI: maintenance hemodialysis patients without cognitive impairment; HC: healthy controls. mfALFF: mean ratio low frequency amplitude; MNI, Montreal Neurological Institute. Condition Brain Regions X MNI Y Z Voxel size Z value P value MHD-CI < MHD-NCI Left inferior temporal gyrus -45 -45 -30 14 -4.208 < 0.001 MHD-CI < HC Left medial superior frontal gyrus 0 54 18 17 -4.233 < 0.001 MHD-NCI < HC Left medial superior frontal gyrus 6 51 12 11 -3.534 < 0.001 Results Of Mreho In Brain Regions Among The Three Groups Compared with the control group, the MHD-CI group had significantly lower left superior occipital gyrus values and significantly higher left rolandic operculum mReHo values, while no significant differences were seen in the other paired groups. The details are presented in Table 4 and Fig. 4 . Table 4 Significant differences in mReHo values among groups. MHD-CI: maintenance hemodialysis patients with cognitive impairment; HC: healthy controls. mReHo: mean regional concordance; MNI, Montreal Neurological Institute. Condition Brain Regions X MNI Y Z Voxel size Z value P value MHD-CI < HC Left supraoccipital gyrus -18 -96 15 14 -3.801 HC Left rolandic operculum -48 0 12 19 3.815 < 0.001 Correlation Analysis In MHD patients, pearson correlation analysis revealed that altered mALFF/mfALFF values correlated with statistically significant clinical indicators. The mALFF values in the left postcentral gyrus of MHD patients were significantly positively correlated with hemoglobin levels (r = 0.551, p = 0.000) and MOCA scores (r = 0.457, p = 0.001), negatively correlated with urea nitrogen (r = − 0.519, p = 0.000); left temporal inferior gyrus mfALFF values were significantly negatively correlated with urea nitrogen levels (r = − 0.523, p = 0.000) and positively correlated with MOCA scores (r = 0.295, p = 0.038). The right caudate nucleus mALFF values were negatively correlated with MOCA scores (r = -0.455, p = 0.001), but were not significantly associated with clinical indicators. Details are shown in Fig. 5 . Back Propagation Neural Network Model Cognition function predictors were designed using BPNN model in four schemes based on altered spontaneous brain activity and clinical indicators in MHD patients. Scheme I (Fig. 6 a) was designed based on the hemoglobin levels and blood urea nitrogen levels which related to MOCA scores, the mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R-squared (R 2 ) values between the actual scores and predicted scores were 0.7995, 2.5285, 2.0548, 9.55%, and 0.6345, respectively. In scheme II (Fig. 6 b), Hb, BUN, and mALFF values in the right caudate nucleus were set as the model inputs, the MSE, RMSE, MAE, MAPE, and R 2 values between the actual scores and predicted scores were 0.7444, 2.3539, 1.7747,8.69%, and 0.6832, respectively. In scheme III (Fig. 6 c), Hb, BUN, and mALFF values in the left postcentral gyrus were set as the model inputs, the MSE, RMSE, MAE, MAPE, and R 2 values between the actual scores and predicted scores were 0.5835, 1.8451, 1.5153, 6.66%, and 0.8054, respectively. In scheme IV (Fig. 6 d), Hb, BUN, mALFF values in the left postcentral gyrus and right caudate nucleus were set as the model inputs, the MSE, RMSE, MAE, MAPE, and R 2 values between the actual scores and predicted scores were 0.5840, 1.8468, 1.7424, 7.78%, and 0.8050, respectively. Details are shown in Fig. 6 . Discussion The number of patients on maintenance hemodialysis is increasing, and the incidence of cognitive dysfunction in patients with MHD is increasing, which not only affects the quality of life and treatment of patients, but also brings a heavy burden to families and society. Comparing ALFF/fALFF/ReHo values across groups helps to demonstrate comprehensive functional changes in MHD-CI patients and identify brain regions and influencing factors associated with cognitive function in MHD-CI patients. Through the analysis of patients' cognitive function risk factors, it is possible to screen high-risk groups and implement early intervention, and establish BPNN prediction models to guide clinical practice. Compared with HC, we found that MHD-CI patients had abnormal spontaneous brain activity in several brain regions. The frontal lobe and occipital lobe are part of the default mode network (DMN) and had connections with the medial temporal lobe system such as hippocampus and parahippocampal gyrus[ 17 ], several rs-fMRI studies reported reduced spontaneous brain activity in the DMN in ESRD patients[ 5 , 10 ]. The enhanced spontaneous brain activity in the hippocampus and parahippocampal gyrus and caudate nucleus is thought to be a compensatory mechanism for impaired cognitive function in the brain. Continuous hyperfunction accelerates neurodegeneration and impaired cognitive function. The postcentral gyrus which located in the parietal lobe of the cerebral cortex is part of the somatosensory-motor network; reduced spontaneous brain activity suggests abnormal somatosensory regulation. Notably, compared with the HC group, only mfALFF in the left medial superior frontal gyrus was decreased, and mALFF in the right hippocampus was elevated in the MHD-NCI group, while no significant abnormal spontaneous brain activity was seen in the remaining regions. These findings suggest that MHD-CI patients tend to have more severe neurophysiological alterations, and compensatory effects. Therefore, we compared various brain areas and clinical indicators between the MHD-CI and MHD-NCI groups to identify the brain areas and risk factors associated with cognitive function. Compared to the MHD-NCI group, only the left inferior temporal gyrus mfALFF values were decreased, the left postcentral gyrus mALFF values were decreased, and the right caudate nucleus mALFF values were increased in the MHD-CI group. The various brain areas were similar but slightly smaller compared to the comparison between the MHD-CI and control groups, further exploration of abnormally active brain areas associated with cognitive function in patients with MHD-CI. Previous studies found reduced mReHo values in the temporal lobe bilaterally in MHD patients and a positive correlation with cognitive function.[ 4 , 17 ] The temporal lobe is associated with the frontal lobes, occipital lobes, and parietal lobes through U-shaped fiber structures and is associated with visual and language comprehension and emotion regulation.[ 18 ] Anderson et al. suggested that the neurobiological features of CI are hypoperfusion and hypometabolism of the temporoparietal cortex and atrophy of the medial temporal lobe.[ 19 ] In the present study, we found reduced mfALFF in the left inferior temporal gyrus of MHD-CI patients, suggesting that abnormal spontaneous brain activity in this brain region might be involved in the underlying neurophysiological mechanisms of CI in MHD-CI patients. While the postcentral gyrus is located in the parietal lobe of the cerebral cortex and is part of the somatosensory center which is involved in daily activities and controls early movement.[ 12 ] Ma et al. found that patients with ESRD have altered topological properties of sensorimotor network nodes.[ 20 ] Papoiu et al. found that abnormal activation of the postcentral gyrus correlated with the degree of chronic limb pruritus in ESRD patients.[ 21 ] In the present study, we found that ALFF was reduced in the left postcentral gyrus, suggesting that MHD-CI patients may also have impaired somatosensory modulation. The present study also found high mALFF values in the right caudate nucleus, a region involved in controlling voluntary movement, learning, and memory.[ 22 ] The involvement of the right caudate nucleus in cortico-striato-thalamic circuits is relevant to cognitive function.[ 23 ] The visual area of the inferior temporal gyrus has been found to terminate primarily in the caudate nucleus.[ 24 ] Therefore, its abnormal brain activity might be considered a compensatory effect of impaired integrity of other brain regions.[ 10 ] The continued activation of the caudate nucleus might contribute to its dysfunctional integration. In general agreement with previous studies, we found that the DMN, somatomotor network, and limbic network integrity were more impaired in MHD-CI patients than in controls. In contrast, no significant difference was found between the default network brain activity abnormalities compared with MHD-NCI patients. This finding suggests that patients with MHD undergoing maintenance hemodialysis still have spontaneous brain activity abnormalities in several brain regions based on impaired integrity of the default network, providing objective imaging support for studying the neurophysiological mechanisms of cognitive dysfunction in MHD patients. We found that patients with MHD-CI had more severe anemia and higher urea nitrogen levels than patients with MHD-NCI, with no significant differences between the remaining clinical indicators. The mALFF value of the left postcentral gyrus and the right caudate nucleus were correlated with hemoglobin level, indicating that anemia may be a risk factor for CI. Anemia decreases the oxygen-carrying capacity of the blood and reduces the oxygenation of the brain.[ 25 ] It causes decreased cerebral blood volume and thus hypoxia, which aggravates the dysregulation of iron metabolism associated with oxidative stress in the brain, leading to neurodegenerative changes.[ 26 ] Reza-Zaldivar et al. found varying degrees of improvement in motor and cognitive function after treatment with recombinant human erythropoietin in rats with chronic kidney disease.[ 27 ] Their findings suggest that correction of anemia might improve cognitive function. The present study found that mALFF values in the left postcentral gyrus and mfALFF values in the left inferior temporal gyrus were negatively correlated with urea nitrogen levels. Other studies showed that serum urea nitrogen is cleared faster than intracranial urea nitrogen, leading to brain edema due to changes in osmotic pressure on both sides of the blood-brain barrier, further altering cognitive function.[ 28 ] Tsuruya et al. suggested that urotoxins induce oxidative stress, neuronal death, and frontotemporal lobe volume atrophy, affecting cognitive function. Chen et al. found that abnormal brain activity in ESRD patients was associated with urea nitrogen.[ 4 ] These findings suggest that a high urea nitrogen level might be a risk factor for CI, explaining why there were more abnormal brain activity brain areas in the MHD-CI group than in the MHD-NCI and control groups.These findings might help explore the neurophysiological mechanisms of MHD-CI patients and guide the early intervention in specific brain regions. In our study, we combines quantifiable influencing factors to build different the BPNN prediction model, reflect different prediction precision of combination forecast model and the evaluation results. Compared with model one, the other three models add neuroimaging markers, and the prediction accuracy is significantly improved. The mALFF values in the right caudate nucleus and left postcentral were added to models 2 and 3, respectively, and the correlation coefficient between these two neuroimaging markers and MOCA scores were roughly similar, but the diagnostic efficacy of model 3 was significantly higher than that of model 2, which showed that spontaneous changes in brain activity in the left postcentral gyrus played a key role in the construction of cognitive function models. We combined two neuroimaging markers with clinical indicators to predict cognitive function, and found that the diagnostic efficacy of model 4 was not better than that of model 3, which further confirmed our conjecture. The main reason considered in this study is that the postcentral gyrus is located in the cerebral cortex to control somatic movement, and several studies have shown that in addition to the default network, the somatic motor network of patients with end-stage renal disease is also affected, and this study found that the left postcentral gyrus is positively correlated with cognitive function and hemoglobin level, which may make the input index more correlative, thereby improving the prediction accuracy of the model. The right caudate nucleus is negatively correlated with the MOCA score, and considering that its change may be compensatory, it is not possible to increase the accuracy of the model after joining. The inferior temporal gyrus, which belongs to the default network, is not significantly related to clinical indicators and MOCA scores, and most studies on BPNN suggest that the inclusion of non-significantly related indicators will reduce the accuracy of the model, so this study did not include them in the model input layer. In sum through the modeling, we found the mean ALFF in the regions with significant between-group difference can serve as neuroimaging markers for the cognition impairment assessment of MHD patients. The modeling results in prediction can effectively validate it. The established models could be useful tools in the clinical diagnosis and the pathophysiology mechanism exploration of cognitive impairment for MHD patients. There are limitations to the present study. First, this was a cross-sectional study, and it was impossible to determine causal relationships between changes in spontaneous brain activity and clinical indicators.Second, a full set of cognitive function scales was not performed, the neuropsychological scales will be refined for future assessments. Finally, building on current research, more large-sample, multi-center trials and predictive models need to be evaluated. Conclusion In summary, we found that MHD-CI patients had abnormal spontaneous brain activity in several brain regions, including the DMN, somatomotor network, and limbic network, the left inferior temporal gyrus and left postcentral gyrus might be the critical regions affecting cognitive function in MHD patients, and correction of anemia and adjustment of urea nitrogen levels might help prevent CI in MHD patients. Based on quantifiable influencing factors, we construct different BPNN prediction models, indicating that the diagnostic efficacy of the model which inputs were hemoglobin, urea nitrogen and mALFF value in the left central posterior gyrus is optimal. Combined with rs-fMRI not only reveals the neurophysiological mechanism of cognitive impairment, but also can serves as a neuroimaging marker for the diagnosis and evaluation of cognitive impairment in patients with MHD. Declarations Acknowledgements We thank Robert, PhD, Sciworks Editing Beijing for his assistance with language support. Author Contributions Author contributions included conception and study design (SQ and ZJH), data collection or acquisition (SQ, ZJH and WXX), statistical analysis (SQ and ZYT), interpretation of results (SQ and LTQ), drafting the manuscript work or revising it critically for important intellectual content (LTQ and SHF) and approval of final version to be published and agreement to be accountable for the integrity and accuracy of all aspects of the work (All authors). Availability of data and materia The datasets generated and analyzed during the current study are not publicly available due the fact that they constitute an excerpt of research in progress but are available from the corresponding author on reasonable request. Funding This work was supported by LGY2020035 from the High-level Health Personnel “Six One Project” Talent Project of Jiangsu Provence and Grant 20220159 from Changzhou Sci&Tech Program. Conflict of Interest The authors declared that they have no conflict of interest. Ethical approval The study was approved by the Ethics Committee of the Second People's Hospital of Changzhou City (KY032-01). All methods were carried out in accordance with relevant guidelines and regulations. Informed consent Informed consent was obtained from all individual participants included in the study. Consent of publication Not applicable. References San A, Hiremagalur B, Muircroft W, Grealish L: Screening of Cognitive Impairment in the Dialysis Population: A Scoping Review . Dement Geriatr Cogn Disord 2017, 44 (3-4):182-195. A.S. Buchman MDT, MD P.A. Boyle, PhD R.C. Shah, MD S.E. Leurgans, PhD D.A. Bennett, MD: Kidney function is associated with the rate of cognitive decline in the elderly . Neurology 2009;73:920 –927 2009. Hermann DM, Kribben A, Bruck H: Cognitive impairment in chronic kidney disease: clinical findings, risk factors and consequences for patient care . 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Zou QH, Zhu CZ, Yang Y, Zuo XN, Long XY, Cao QJ, Wang YF, Zang YF: An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF . J Neurosci Methods 2008, 172 (1):137-141. Zhou M, Hu X, Lu L, Zhang L, Chen L, Gong Q, Huang X: Intrinsic cerebral activity at resting state in adults with major depressive disorder: A meta-analysis . Prog Neuropsychopharmacol Biol Psychiatry 2017, 75 :157-164. Chen P, Hu R, Gao L, Wu B, Peng M, Jiang Q, Wu X, Xu H: Abnormal degree centrality in end-stage renal disease (ESRD) patients with cognitive impairment: a resting-state functional MRI study . Brain Imaging Behav 2021, 15 (3):1170-1180. Su H, Fu S, Liu M, Yin Y, Hua K, Meng S, Jiang G, Quan X: Altered Spontaneous Brain Activity and Functional Integration in Hemodialysis Patients With End-Stage Renal Disease . Front Neurol 2021, 12 :801336. Fu J, Chen X, Gu Y, Xie M, Zheng Q, Wang J, Zeng C, Li Y: Functional connectivity impairment of postcentral gyrus in relapsing-remitting multiple sclerosis with somatosensory disorder . European journal of radiology 2019, 118 :200-206. Wei W, Yang X: Comparison of Diagnosis Accuracy between a Backpropagation Artificial Neural Network Model and Linear Regression in Digestive Disease Patients: an Empirical Research . Comput Math Methods Med 2021, 2021 :6662779. Xiao F, Wang T, Gao L, Fang J, Sun Z, Xu H, Zhang J: Frequency-Dependent Changes of the Resting BOLD Signals Predicts Cognitive Deficits in Asymptomatic Carotid Artery Stenosis . Front Neurosci 2018, 12 :416. Lyu J, Shi H, Zhang J, Norvilitis J: Prediction model for suicide based on back propagation neural network and multilayer perceptron . Frontiers in neuroinformatics 2022, 16 :961588. Tiffin-Richards FE, Costa AS, Holschbach B, Frank RD, Vassiliadou A, Kruger T, Kuckuck K, Gross T, Eitner F, Floege J et al : The Montreal Cognitive Assessment (MoCA) - a sensitive screening instrument for detecting cognitive impairment in chronic hemodialysis patients . PLoS One 2014, 9 (10):e106700. Liang X, Wen J, Ni L, Zhong J, Qi R, Zhang LJ, Lu GM: Altered pattern of spontaneous brain activity in the patients with end-stage renal disease: a resting-state functional MRI study with regional homogeneity analysis . PLoS One 2013, 8 (8):e71507. Lin YH, Young IM, Conner AK, Glenn CA, Chakraborty AR, Nix CE, Bai MY, Dhanaraj V, Fonseka RD, Hormovas J et al : Anatomy and White Matter Connections of the Inferior Temporal Gyrus . World Neurosurg 2020, 143 :e656-e666. Anderson ND: State of the science on mild cognitive impairment (MCI) . CNS Spectr 2019, 24 (1):78-87. Ma X, Jiang G, Li S, Wang J, Zhan W, Zeng S, Tian J, Xu Y: Aberrant functional connectome in neurologically asymptomatic patients with end-stage renal disease . PLoS One 2015, 10 (3):e0121085. Papoiu AD, Emerson NM, Patel TS, Kraft RA, Valdes-Rodriguez R, Nattkemper LA, Coghill RC, Yosipovitch G: Voxel-based morphometry and arterial spin labeling fMRI reveal neuropathic and neuroplastic features of brain processing of itch in end-stage renal disease . J Neurophysiol 2014, 112 (7):1729-1738. Seger CA, Cincotta CM: The roles of the caudate nucleus in human classification learning . J Neurosci 2005, 25 (11):2941-2951. Haber SN, Knutson B: The reward circuit: linking primate anatomy and human imaging . Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology 2010, 35 (1):4-26. Haber SN: Corticostriatal circuitry . Dialogues in Clinical Neuroscience 2016, 18 (1):7-21. Duffin J, Hare GMT, Fisher JA: A mathematical model of cerebral blood flow control in anaemia and hypoxia . J Physiol 2020, 598 (4):717-730. Carvalho C, Correia SC, Santos RX, Cardoso S, Moreira PI, Clark TA, Zhu X, Smith MA, Perry G: Role of mitochondrial-mediated signaling pathways in Alzheimer disease and hypoxia . J Bioenerg Biomembr 2009, 41 (5):433-440. Reza-Zaldivar EE, Sandoval-Avila S, Gutierrez-Mercado YK, Vazquez-Mendez E, Canales-Aguirre AA, Esquivel-Solis H, Gomez-Pinedo U, Marquez-Aguirre AL: Human recombinant erythropoietin reduces sensorimotor dysfunction and cognitive impairment in rat models of chronic kidney disease . Neurologia (Engl Ed) 2020, 35 (3):147-154. SM S: Cerebral edema after rapid dialysis is not caused by an increase in brain organic osmolytes . J Am Soc Nephrol , 1995 Dec;6(6):1600-6 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2159328","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":145944599,"identity":"d6fbf16e-8c92-4c6c-9101-a54edc32f9cb","order_by":0,"name":"Qing Sun","email":"","orcid":"","institution":"Changzhou No.2 People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Sun","suffix":""},{"id":145944600,"identity":"b19fd113-f9c0-4572-aec4-3693a70146f4","order_by":1,"name":"Jiahui Zheng","email":"","orcid":"","institution":"Changzhou No.2 People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiahui","middleName":"","lastName":"Zheng","suffix":""},{"id":145944601,"identity":"fae5235f-532b-41f5-a964-6319eed263b1","order_by":2,"name":"Yutao Zhang","email":"","orcid":"","institution":"Changzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yutao","middleName":"","lastName":"Zhang","suffix":""},{"id":145944602,"identity":"fbfb6c5e-0bb1-4f03-b0a5-e03f4cdb5847","order_by":3,"name":"Xiangxiang Wu","email":"","orcid":"","institution":"Changzhou No.2 People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiangxiang","middleName":"","lastName":"Wu","suffix":""},{"id":145944603,"identity":"89f7cf7e-eddf-4443-abda-9b35b161d56f","order_by":4,"name":"Zhuqing Jiao","email":"","orcid":"","institution":"Changzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhuqing","middleName":"","lastName":"Jiao","suffix":""},{"id":145944604,"identity":"23a9b797-1787-4929-869b-8d0e1b0675c4","order_by":5,"name":"Lifang Xu","email":"","orcid":"","institution":"Changzhou No.2 People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lifang","middleName":"","lastName":"Xu","suffix":""},{"id":145944605,"identity":"65a193ed-6cd1-4daf-b90b-fbe4a3580c7c","order_by":6,"name":"Haifeng Shi","email":"","orcid":"","institution":"Changzhou No.2 People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Haifeng","middleName":"","lastName":"Shi","suffix":""},{"id":145944606,"identity":"da02f239-70cb-46fe-aeb7-d65592fc9873","order_by":7,"name":"Tongqiang Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYBACPmYgUWEgwcPP3tj44AMxWthAWs4UWMhJ9hxuNpxBlBYQceZDhbHBjPQ2aQ6itLDzmEkcMJBI3CD5sEGagcFOTreBoMOgWrZLJzYYFzAkG5sdIEKL9Aeglp2zExuSZzAcSNxGjBaIw24ebDjMQ4oWY4MbjI3NRGphK7YAagEGcmIz4wwDIvzCz394440Df+qAUXn8+Y8PFXZyBLUAAYsEgm1AWDkIMBOVTEbBKBgFo2AEAwBXST5ZaVQzygAAAABJRU5ErkJggg==","orcid":"","institution":"Changzhou No.2 People's Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Tongqiang","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2022-10-12 15:29:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2159328/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2159328/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":28294552,"identity":"0d9399b6-1860-4c53-8677-bde3adccddb8","added_by":"auto","created_at":"2022-10-26 19:15:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":399232,"visible":true,"origin":"","legend":"\u003cp\u003eClusters with significantly altered mALFF values among groups. The blue-yellow areas denote lower mALFF values in the MHD-CI group than in the (A) MHD-NCI group or (B) HC group. mALFF: mean low frequency amplitude; MHD-CI: maintenance hemodialysis patients with cognitive impairment; MHD-NCI: maintenance hemodialysis patients without cognitive impairment; HC: healthy controls.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2159328/v1/2243b8b7dfee9f7e5fd60a0a.png"},{"id":28294556,"identity":"d7742646-5e70-4644-b571-13ce6517b8e3","added_by":"auto","created_at":"2022-10-26 19:15:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":449827,"visible":true,"origin":"","legend":"\u003cp\u003eClusters with significantly altered mALFF values among groups. The red-yellow areas denote higher mALFF values in the MHD-CI group than in the (A) MHD-NCI group or (B) HC group；（C）MHD-NCI group compared to the HC group. mALFF: mean low frequency amplitude; MHD-CI: maintenance hemodialysis patients with cognitive impairment; MHD-NCI: maintenance hemodialysis patients without cognitive impairment; HC: healthy controls.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2159328/v1/9f651f8745ebaa0a4c302e0c.png"},{"id":28294788,"identity":"5ac003b8-12bb-472c-bb34-18bc22cc9a48","added_by":"auto","created_at":"2022-10-26 19:20:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":439462,"visible":true,"origin":"","legend":"\u003cp\u003eClusters with significantly altered mfALFF values among groups. The blue-yellow areas denote lower mfALFF values in the MHD-CI group than in the (A) MHD-NCI group or (B) HC group；（C）MHD-NCI group compared to the HC group. mfALFF: mean ratio low frequency amplitude; MHD-CI: maintenance hemodialysis patients with cognitive impairment; MHD-NCI: maintenance hemodialysis patients without cognitive impairment; HC: healthy controls.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2159328/v1/755b0d408b5bc7d8ca26bc9d.png"},{"id":28294553,"identity":"28ef6bf3-0f50-420b-b458-a91f59ceec5f","added_by":"auto","created_at":"2022-10-26 19:15:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":320923,"visible":true,"origin":"","legend":"\u003cp\u003eClusters with significantly altered mReHo values among groups. (A) The blue-yellow areas denote lower mReHo values in the MHD-CI group than the HC group. (B) The red-yellow areas denote higher mfALFF values in the MHD-CI group than the HC group. mReHo: mean regional concordance; MHD-CI: maintenance hemodialysis patients with cognitive impairment; MHD-NCI: maintenance hemodialysis patients without cognitive impairment; HC: healthy controls.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-2159328/v1/6dfbd6721a9fb79b389464ba.png"},{"id":28294925,"identity":"5b2508a3-5d76-4d7c-8fb1-5509b5e12813","added_by":"auto","created_at":"2022-10-26 19:25:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":97777,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis results. (a) Correlation matrix diagram of ALFF/fALFF values and clincal indicators of MHD patients.(b and c) , Blood urea nitrogen levels and mALFF values in the right caudate nucleus of MHD patients are negatively correlated with MOCA scores. \u0026nbsp;(d, e and f) Hemoglobin levels and mALFF values in the left postcentral gyrus and mfALFF values in the left inferior temporal gyrus of MHD patients are negatively correlated with MOCA scores.\u003c/p\u003e\n\u003cp\u003emfALFF-ITG.L: left inferior temporal gyrus; mALFF-PoCG.L: mALFF values in the left postcentral gyrus. mALFF: CAU.R: mALFF values in the right caudate nucleus.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-2159328/v1/813068c434195cdec0a60634.png"},{"id":28294557,"identity":"cb573852-11bc-4f44-b824-2467936cf1ef","added_by":"auto","created_at":"2022-10-26 19:15:26","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":143442,"visible":true,"origin":"","legend":"\u003cp\u003eBack propagation neural network model. (a) Actual scores and predicted scores of BPNN based on Hb and BUN values. (b) actual scores and predicted scores of BPNN based on Hb, BUN and mALFF values in the right caudate nucleus. (c) actual scores and predicted scores of BPNN based on Hb, BUN and mALFF values in the left postcentral gyrus.(d) actual scores and predicted scores of BPNN based on Hb, BUN and mALFF values in the left postcentral gyrus and right caudate nucleus.The dots in the figures indicate the subjects included in the test set of prediction models.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-2159328/v1/4b3c7f62233c715eb6d4b9d3.png"},{"id":33581256,"identity":"b792d6b0-c6d8-4b9c-a073-6583e12af95f","added_by":"auto","created_at":"2023-02-28 20:14:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2938492,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2159328/v1/545868cc-da32-440b-98b6-7df9dca3dd0b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Altered spontaneous brain activities in maintenance hemodialysis patients with cognitive dysfunction and the construction of cognitive function prediction models","fulltext":[{"header":"Background","content":"\u003cp\u003eAt present, the number of dialysis patients is increasing rapidly, and the treatment period is long, and various complications can occur during the treatment, among which cognitive impairment (CI) is one of the common complications among maintenance hemodialysis (MHD) patients, with an incidence of 6.6%-51%[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It primarily manifesting as impaired executive ability, impaired attention, memory impairment, and slow motor performance. These deficits reduce compliance and quality of life and negatively affect outcomes[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The primary means of assessing cognitive function is the neuropsychological scale which is subjective and poorly completed by the patient, and therefore easily overlooked and hinders the early identification of cognitive impairment.\u003c/p\u003e \u003cp\u003eResting-state functional magnetic resonance imaging (rs-fMRI) evaluates neural networks by measuring blood-oxygen-level-dependent signals at rest. It has been used to investigate the pattern of neuropathological alterations in MHD patients[\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Low-frequency amplitude (ALFF), ratio low-frequency amplitude (fALFF), and regional homogeneity (ReHo) are three commonly used rs-fMRI methods to quantify neural activity. ALFF and fALFF reflect the functional intensity of local brain regions, reflecting the signal of each voxel in the 0.01\u0026ndash;0.08 Hz frequency band[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. ReHo measures the regional coherence of neural activity between adjacent voxels[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Several studies applied rs-fMRI methods to explore changes in spontaneous brain activity in MHD patients and found that reduced ALFF/fALFF/ReHo values in the default mode network (DMN) region were associated with cognitive decline[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, these studies did not focus on brain activity changes in MHD patients with CI. It is known that the mechanisms of cognitive dysfunction in maintenance hemodialysis patients are directly or indirectly related to various risk factors such as neurological damage due to uremic toxin accumulation, hypoglobinemia due to anemia and malnutrition, hemodynamic fluctuations, and electrolyte imbalance due to long-term hemodialysis[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Therefore, this study aim to explore comprehensively the critical brain regions related to cognitive function in combination with resting-state functional MRI, illustrate the neurophysiological mechanisms underlying the occurrence of cognitive impairment in MHD-CI patients, and to avoid interference from over-confounding factors, we divided hemodialysis patients into two groups based on MOCA scores, analyzed clinical factors related to cognitive function in a cohort of maintenance hemodialysis patients with the same risk factor, and analyzed the correlation between them for early prevention.\u003c/p\u003e \u003cp\u003eAt present, the early identification of CI is mostly based on the judgment of clinical experience, without quantitative evaluation criteria, and the risk of CI cannot be accurately predicted. In order to further improve the artificial decision-making process, the construction and application of disease prediction model play an important reference value for disease screening, and prediction model has become a research hotspot in the field of MCI risk prediction. This study will use the Back propagation neural network (BPNN) to predict cognitive function, and a large number of experimental tests and theoretical studies have shown that the BPNN algorithm is a kind efficient learning algorithms, which has been shown to have better diagnostic efficacy than the logistic regression model in several fields, it has good self-learning and adaptive ability[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In this study, the mean ALFF/ fALFF/ ReHo values of the brain regions with significant between-group difference were used as neuroimaging markers, and clinical indicators related to cognitive function were incorporated into the BPNN to comprehensively assess the cognitive function of maintenance hemodialysis patients and facilitate early diagnosis. Moreover, this is the first study to use BPNN to predict cognitive function in combination with rs-fMRI and clinical indicators, providing clinical guidance for early diagnosis of cognitive function.\u003c/p\u003e"},{"header":"Objectives And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch subjects\u003c/h2\u003e \u003cp\u003eThis study included 55 patients undergoing regular maintenance hemodialysis at Changzhou Second People's Hospital from September 2020 to December 2021. Inclusion criteria were (1) dialysis age of 6 months or more, (2) age between 18 and 60 years, and (3) right-handedness. Thirty healthy individuals matched for age, sex, and education were recruited as controls. Exclusion criteria were (1) presence of psychiatric or neurological disorders, including brain tumor, traumatic brain injury, stroke, schizophrenia, and epilepsy, (2) inability to complete neuropsychological scales, (3) chronic advanced liver failure or heart failure, (4) history of drug and alcohol dependence, (5) type 2 diabetes, depression, and sleep disorders; (6) contraindications to MRI scanning, and (7) head movement artifacts interfering with experimental data acquisition or measurement. According to the exclusion criteria, five MHD patients were excluded (three with motion artifacts in data measurement and two with lacunar cerebral infarction lesions), and two controls were excluded due to incomplete data processing. The study group comprised 30 MHD-CI patients ([18 males, 12 females], age 49.53\u0026thinsp;\u0026plusmn;\u0026thinsp;8.23 years), 20 MHD-NCI ([15 males, 5 females], age 47.00\u0026thinsp;\u0026plusmn;\u0026thinsp;10.87 years), and 28 controls ([14 males, 14 females], age 47.57\u0026thinsp;\u0026plusmn;\u0026thinsp;10.22] years). The study was approved by the Ethics Committee of the Second People's Hospital of Changzhou City (KY032-01).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResearch Methodology\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eLaboratory tests\u003c/h2\u003e \u003cp\u003eBlood tests were performed on all subjects within 24h prior to imaging, including erythrocytes, leukocytes, hemoglobin, erythrocyte pressure product, albumin, LDL, triglycerides, serum creatinine, blood urea nitrogen, blood uric acid, serum potassium, serum sodium, serum calcium, serum phosphorus, parathyroid hormone, and urea clearance index (Kt/V).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNeuropsychological Scales\u003c/h3\u003e\n\u003cp\u003eAll subjects completed the neuropsychological scales 1 h before MRI. The Montreal cognitive assessment (MOCA) was used to assess overall cognitive function, with dimensions including visuospatial, executive ability, memory, naming, attention, language, abstract thinking, and orientation. The total score is 30 points; when the subject has less than 12 years of education plus one point, less than 26 years is cognitive impairment.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] The test was administered by professionally trained personnel. Test administrators were professionally trained, and the testing environment was quiet.\u003c/p\u003e\n\u003ch3\u003eMri Acquisition\u003c/h3\u003e\n\u003cp\u003eT2-FLAIR images, 3D-T1-weighted imaging (T1WI), and rs-fMRI images were acquired in all subjects using a GE Discovery MR 750W 3.0T scanner; rs-fMRI was performed after dialysis. Patients were asked to close their eyes, relax, and avoid thinking during the scan. The T2-FLAIR was used to exclude organic lesions; rs-fMRI sequential scanning was performed with machine scan parameters: repetition time (TR)\u0026thinsp;=\u0026thinsp;2000 ms, echo time (TE)\u0026thinsp;=\u0026thinsp;40 ms, flip angle (FA)\u0026thinsp;=\u0026thinsp;90\u0026deg;, matrix\u0026thinsp;=\u0026thinsp;64 \u0026times; 64, field of view (FOV) 240 mm \u0026times; 240 mm, and slice thickness\u0026thinsp;=\u0026thinsp;6 mm. High-resolution whole-brain T1WI structural images were obtained using a three-dimensional (3D) brain volumetric imaging (3D BRAVO) sequence. Scanning parameters were TR\u0026thinsp;=\u0026thinsp;8.2 ms, TE\u0026thinsp;=\u0026thinsp;3.2 ms, layer thickness\u0026thinsp;=\u0026thinsp;1.2 mm, layer interval\u0026thinsp;=\u0026thinsp;0 mm, flip angle\u0026thinsp;=\u0026thinsp;12\u0026deg;, matrix\u0026thinsp;=\u0026thinsp;256\u0026times;256, FOV\u0026thinsp;=\u0026thinsp;240 mm\u0026times;240 mm, and number of layers scanned\u0026thinsp;=\u0026thinsp;152 slices.\u003c/p\u003e\n\u003ch3\u003eMri Data Analysis\u003c/h3\u003e\n\u003cp\u003eData preprocessing was performed using the DPARSF (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rfmri.org/DPARSF\u003c/span\u003e\u003cspan address=\"http://rfmri.org/DPARSF\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) toolkit based on the MATLAB 2018a (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mathworks.com/\u003c/span\u003e\u003cspan address=\"https://www.mathworks.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) platform with the following steps: (1) conversion of DICOM format to NFITI format; (2) removal of the first ten time points; (3) time layer and head motion correction; (4) spatial normalization using T1 joint segmentation, Montreal Neurological Institute (MNI) template and resampling voxel size of 3 mm \u0026times; 3 mm \u0026times; 3 mm; (5) regression of head motion, brain white matter signal, cerebrospinal fluid signal, and global signal as covariates. Then, ALFF and fALFF values were analyzed follow the steps below, and a Gaussian smoothing kernel with a full-width half-height value of 4 mm was used to perform spatial smoothing and remove low-frequency linear drift. The average amplitude value of the low-frequency amplitude of the brain (0.01\u0026ndash;0.08 Hz) was extracted, and the fALFF value was obtained by dividing the sum of ALFF values in this frequency band by the sum of amplitudes in the full frequency band. Normalization was performed to obtain the average ALFF/fALFF (mALFF/mfALFF) as a parameter for further statistical analysis; reHo maps were generated before spatial smoothing, and the normalized images were filtered (0.01\u0026ndash;0.08 Hz) and normalized before spatial smoothing. The ReHo maps were obtained as ReHo parameters for statistical analysis.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFor baseline data, SPSS 24.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ibm.com/cn-zh/products/spss-statistics\u003c/span\u003e\u003cspan address=\"https://www.ibm.com/cn-zh/products/spss-statistics\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used for statistical analysis. Qualitative data (gender) were expressed as frequency and compared between groups using the χ\u003csup\u003e2\u003c/sup\u003e test; quantitative data conforming to a normal distribution were compared between groups using the independent samples t-test or analysis of variance, expressed as\u0026oline;X \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e S. Quantitative data with skewed distribution were analyzed using the Mann-Whitney U-test or Kruskal-Wallis test, expressed as M (Q1, Q3). If significant differences were found in the comparisons between the three groups, the least significant difference method was used to assess the differences between functional data. P \u0026lt; 0.05 was considered a statistically significant difference. Data is z-scored standardized using MATLAB software prior to correlation analysis.\u003c/p\u003e \u003cp\u003eComparisons of ALFF/fALFF/ReHo values among the three groups were performed as follows: differences between groups were calculated using the DPABI (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.rfmri.org/dpabi)toolbo\u003c/span\u003e\u003cspan address=\"http://www.rfmri.org/dpabi)toolbo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ex; analysis of variance was performed with post hoc tests using the least significant difference method; corrected p-values were calculated for comparing any pair; p-mapping was converted to Z-mapping; a Gaussian random field correction was performed using Z-mapping to correct for multiple comparisons, with statistical thresholds set at the voxel level at p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and at the cluster level (two-tailed) set to p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All coordinates were reported in Montreal Neurological Institute space.\u003c/p\u003e \u003cp\u003eCorrelation analysis and was performed as follows: Pearson correlation analysis was performed between significantly different mALFF/mfALFF/mReHo values and clinical variables extracted from MHD-CI patients and MHD-NCI group. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered a statistically significant difference. Back propagation neural network (BPNN) was used to predict cognitive function.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv class=\"Section2\" id=\"Sec11\"\u003e\n \u003ch2\u003eDemographic and clinical characteristics\u003c/h2\u003e\n \u003cp\u003eThere were no significant differences in age, gender, or education of the subjects across groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). MOCA scores were significantly lower in the MHD-CI group than in the MHD-NCI and control groups (p\u0026thinsp;=\u0026thinsp;0.000); the difference in MOCA scores between the HC and MHD-NCI groups was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.148). Compared to the control group, MHD-CI patients had significantly lower erythrocytes, hemoglobin, albumin, and higher urea nitrogen, creatinine, phosphorus, and parathyroid hormone (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Compared to the MHD-NCI group, MHD-CI patients had significantly lower hemoglobin (p\u0026thinsp;=\u0026thinsp;0.009) and significantly higher urea nitrogen levels (p\u0026thinsp;=\u0026thinsp;0.000), with no significant differences between the remaining clinical indicators. The details are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGeneral information and laboratory tests for the three groups of subjects. MHD-CI: maintenance hemodialysis patients with cognitive impairment; MHD-NCI: maintenance hemodialysis patients without cognitive impairment; HC: healthy controls. MOCA: Montreal cognitive assessment scale, Kt/V: dialysis effectiveness index. A: Independent samples t-test; B: Mann-whitney U test; C: test of variance (ANOVA); D: Kruskal Wallis tes t; \u0026chi;\u003csup\u003e2\u003c/sup\u003e: chi-square test. \u0026amp;: statistically significant difference in the MHD-CI group/MHD-NCI group compared to HC; #: statistically significant difference in the MHD-CI group compared to the MHD-NCI group.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProtocols\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMHD-CI\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMHD-NCI\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHC\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.53\u0026thinsp;\u0026plusmn;\u0026thinsp;8.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.00\u0026thinsp;\u0026plusmn;\u0026thinsp;10.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.57\u0026thinsp;\u0026plusmn;\u0026thinsp;10.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.610\u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender (male/female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18/12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15/5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14/14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.229\u003csup\u003e\u0026chi;2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYears of education (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.00(5.75-9.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.00(8.25-9.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.00(6.00\u0026ndash;9.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.211\u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMOCA (points)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.00(17.00\u0026ndash;24.00) \u003csup\u003e\u0026amp;#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.00(27.00\u0026ndash;29.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.00(26.00\u0026ndash;27.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDuration of dialysis (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00(1.00-4.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.50(1.00\u0026ndash;3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.270\u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeukocytes (\u0026times;10\u003csup\u003e9\u003c/sup\u003e /L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.946\u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRed blood cells (\u0026times;10\u003csup\u003e9\u003c/sup\u003e /L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHemoglobin (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.16\u0026thinsp;\u0026plusmn;\u0026thinsp;15.7323\u003csup\u003e\u0026amp;#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112.95\u0026thinsp;\u0026plusmn;\u0026thinsp;9.25\u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116.07\u0026thinsp;\u0026plusmn;\u0026thinsp;7.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHematocrit (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.90(29.85\u0026ndash;36.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.00(28.92-35.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.00(32.50\u0026ndash;36.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.191\u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlbumin (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.85(34.57\u0026ndash;40.70) \u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.15(36.00-42.47) \u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.20(39.97\u0026ndash;45.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlucose (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.84 (4.50\u0026ndash;5.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.74 (4.38\u0026ndash;5.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.05 (4.43\u0026ndash;6.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.530\u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlood Urea nitrogen (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.691\u0026thinsp;\u0026plusmn;\u0026thinsp;6.17\u003csup\u003e\u0026amp;#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.19\u0026thinsp;\u0026plusmn;\u0026thinsp;7.39\u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreatinine (\u0026micro;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e787.21\u0026thinsp;\u0026plusmn;\u0026thinsp;342.55\u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e904.32\u0026thinsp;\u0026plusmn;\u0026thinsp;481.21\u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.28\u0026thinsp;\u0026plusmn;\u0026thinsp;10.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUric acid (\u0026micro;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e313.69\u0026thinsp;\u0026plusmn;\u0026thinsp;154.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e351.03\u0026thinsp;\u0026plusmn;\u0026thinsp;152.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e290.11\u0026thinsp;\u0026plusmn;\u0026thinsp;91.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.307\u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCholesterol (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.680\u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTriglycerides (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.238\u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlood sodium (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140.08\u0026thinsp;\u0026plusmn;\u0026thinsp;2.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139.81\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139.55\u0026thinsp;\u0026plusmn;\u0026thinsp;2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.762\u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlood potassium (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.11 (3.80\u0026ndash;4.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.35 (3.86\u0026ndash;4.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.10 (3.5\u0026ndash;4.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.386\u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlood calcium (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.19 (2.04\u0026ndash;2.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.16 (2.08\u0026ndash;2.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.42 (1.94\u0026ndash;2.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.765\u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlood phosphorus (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.94 (1.44\u0026ndash;2.37) \u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.80 (1.61\u0026ndash;2.04) \u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18 (0.85\u0026ndash;1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParathyroid hormone (ng/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e295.45(136.70-453.72) \u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e288.50(128.27-405.82) \u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.15(17.47\u0026ndash;42.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKt/V (ml-s-1/1.73m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.259\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eResults Of Malff In Brain Regions Among The Three Groups\u003c/h3\u003e\n\u003cp\u003eIn the MHD-CI group, mALFF values were significantly higher in the left fusiform gyrus, left parahippocampal gyrus, right hippocampus, left caudate nucleus, and right caudate nucleus and lower values in the left postcentral gyrus than in the control group. Compared with the MHD-NCI, the MHD-CI group had significantly higher mALFF values in the right caudate nucleus and significantly lower mALFF values in the left posterior central gyrus. The details are presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSignificant differences in mALFF values among groups. mALFF: mean low frequency amplitude; MHD-CI: maintenance hemodialysis patients with cognitive impairment; MHD-NCI: maintenance hemodialysis patients without cognitive impairment; HC: healthy controls. MNI, Montreal Neurological Institute.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCondition\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBrain Regions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMNI\u003c/p\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVoxel size\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZ value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMHD-CI\u0026thinsp;\u0026gt;\u0026thinsp;MHD-NCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eright caudate nucleus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMHD-CI\u0026thinsp;\u0026gt;\u0026thinsp;HC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft fusiform gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eleft parahippocampal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRight hippocampus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft caudate nucleus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eright caudate nucleus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMHD-CI\u0026thinsp;\u0026lt;\u0026thinsp;MHD-NCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft postcentral gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMHD-CI\u0026thinsp;\u0026lt;\u0026thinsp;HC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft postcentral gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMHD-NCI\u0026thinsp;\u0026gt;\u0026thinsp;HC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRight hippocampus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eResults Of Mfalff In Brain Regions Among The Three Groups\u003c/h3\u003e\n\u003cp\u003eThe left medial superior frontal gyrus mfALFF was significantly lower in the MHD-CI and MHD-NCI groups than in the control group, and the left inferior temporal gyrus mfALFF values were significantly lower in the MHD-CI group than in the MHD-NCI group, and No significant differences were seen in the other paired groups. The details are presented in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSignificant differences in mfALFF values among groups. MHD-CI: maintenance hemodialysis patients with cognitive impairment; MHD-NCI: maintenance hemodialysis patients without cognitive impairment; HC: healthy controls. mfALFF: mean ratio low frequency amplitude; MNI, Montreal Neurological Institute.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCondition\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBrain Regions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMNI\u003c/p\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVoxel size\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZ value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMHD-CI\u0026thinsp;\u0026lt;\u0026thinsp;MHD-NCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft inferior temporal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMHD-CI\u0026thinsp;\u0026lt;\u0026thinsp;HC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft medial superior frontal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMHD-NCI\u0026thinsp;\u0026lt;\u0026thinsp;HC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft medial superior frontal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eResults Of Mreho In Brain Regions Among The Three Groups\u003c/h3\u003e\n\u003cp\u003eCompared with the control group, the MHD-CI group had significantly lower left superior occipital gyrus values and significantly higher left rolandic operculum mReHo values, while no significant differences were seen in the other paired groups. The details are presented in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSignificant differences in mReHo values among groups. MHD-CI: maintenance hemodialysis patients with cognitive impairment; HC: healthy controls. mReHo: mean regional concordance; MNI, Montreal Neurological Institute.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCondition\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBrain Regions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMNI\u003c/p\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVoxel size\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZ value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMHD-CI\u0026thinsp;\u0026lt;\u0026thinsp;HC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft supraoccipital gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMHD-CI\u0026thinsp;\u0026gt;\u0026thinsp;HC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft rolandic operculum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eCorrelation Analysis\u003c/h3\u003e\n\u003cp\u003eIn MHD patients, pearson correlation analysis revealed that altered mALFF/mfALFF values correlated with statistically significant clinical indicators. The mALFF values in the left postcentral gyrus of MHD patients were significantly positively correlated with hemoglobin levels (r\u0026thinsp;=\u0026thinsp;0.551, p\u0026thinsp;=\u0026thinsp;0.000) and MOCA scores (r\u0026thinsp;=\u0026thinsp;0.457, p\u0026thinsp;=\u0026thinsp;0.001), negatively correlated with urea nitrogen (r = \u0026minus;\u0026thinsp;0.519, p\u0026thinsp;=\u0026thinsp;0.000); left temporal inferior gyrus mfALFF values were significantly negatively correlated with urea nitrogen levels (r = \u0026minus;\u0026thinsp;0.523, p\u0026thinsp;=\u0026thinsp;0.000) and positively correlated with MOCA scores (r\u0026thinsp;=\u0026thinsp;0.295, p\u0026thinsp;=\u0026thinsp;0.038). The right caudate nucleus mALFF values were negatively correlated with MOCA scores (r = -0.455, p\u0026thinsp;=\u0026thinsp;0.001), but were not significantly associated with clinical indicators. Details are shown in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eBack Propagation Neural Network Model\u003c/h3\u003e\n\u003cp\u003eCognition function predictors were designed using BPNN model in four schemes based on altered spontaneous brain activity and clinical indicators in MHD patients. Scheme I (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea) was designed based on the hemoglobin levels and blood urea nitrogen levels which related to MOCA scores, the mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R-squared (R\u003csup\u003e2\u003c/sup\u003e) values between the actual scores and predicted scores were 0.7995, 2.5285, 2.0548, 9.55%, and 0.6345, respectively. In scheme II (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb), Hb, BUN, and mALFF values in the right caudate nucleus were set as the model inputs, the MSE, RMSE, MAE, MAPE, and R\u003csup\u003e2\u003c/sup\u003e values between the actual scores and predicted scores were 0.7444, 2.3539, 1.7747,8.69%, and 0.6832, respectively. In scheme III (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ec), Hb, BUN, and mALFF values in the left postcentral gyrus were set as the model inputs, the MSE, RMSE, MAE, MAPE, and R\u003csup\u003e2\u003c/sup\u003e values between the actual scores and predicted scores were 0.5835, 1.8451, 1.5153, 6.66%, and 0.8054, respectively. In scheme IV (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ed), Hb, BUN, mALFF values in the left postcentral gyrus and right caudate nucleus were set as the model inputs, the MSE, RMSE, MAE, MAPE, and R\u003csup\u003e2\u003c/sup\u003e values between the actual scores and predicted scores were 0.5840, 1.8468, 1.7424, 7.78%, and 0.8050, respectively. Details are shown in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe number of patients on maintenance hemodialysis is increasing, and the incidence of cognitive dysfunction in patients with MHD is increasing, which not only affects the quality of life and treatment of patients, but also brings a heavy burden to families and society. Comparing ALFF/fALFF/ReHo values across groups helps to demonstrate comprehensive functional changes in MHD-CI patients and identify brain regions and influencing factors associated with cognitive function in MHD-CI patients. Through the analysis of patients' cognitive function risk factors, it is possible to screen high-risk groups and implement early intervention, and establish BPNN prediction models to guide clinical practice.\u003c/p\u003e \u003cp\u003eCompared with HC, we found that MHD-CI patients had abnormal spontaneous brain activity in several brain regions. The frontal lobe and occipital lobe are part of the default mode network (DMN) and had connections with the medial temporal lobe system such as hippocampus and parahippocampal gyrus[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], several rs-fMRI studies reported reduced spontaneous brain activity in the DMN in ESRD patients[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The enhanced spontaneous brain activity in the hippocampus and parahippocampal gyrus and caudate nucleus is thought to be a compensatory mechanism for impaired cognitive function in the brain. Continuous hyperfunction accelerates neurodegeneration and impaired cognitive function. The postcentral gyrus which located in the parietal lobe of the cerebral cortex is part of the somatosensory-motor network; reduced spontaneous brain activity suggests abnormal somatosensory regulation. Notably, compared with the HC group, only mfALFF in the left medial superior frontal gyrus was decreased, and mALFF in the right hippocampus was elevated in the MHD-NCI group, while no significant abnormal spontaneous brain activity was seen in the remaining regions. These findings suggest that MHD-CI patients tend to have more severe neurophysiological alterations, and compensatory effects. Therefore, we compared various brain areas and clinical indicators between the MHD-CI and MHD-NCI groups to identify the brain areas and risk factors associated with cognitive function.\u003c/p\u003e \u003cp\u003eCompared to the MHD-NCI group, only the left inferior temporal gyrus mfALFF values were decreased, the left postcentral gyrus mALFF values were decreased, and the right caudate nucleus mALFF values were increased in the MHD-CI group. The various brain areas were similar but slightly smaller compared to the comparison between the MHD-CI and control groups, further exploration of abnormally active brain areas associated with cognitive function in patients with MHD-CI. Previous studies found reduced mReHo values in the temporal lobe bilaterally in MHD patients and a positive correlation with cognitive function.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] The temporal lobe is associated with the frontal lobes, occipital lobes, and parietal lobes through U-shaped fiber structures and is associated with visual and language comprehension and emotion regulation.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] Anderson et al. suggested that the neurobiological features of CI are hypoperfusion and hypometabolism of the temporoparietal cortex and atrophy of the medial temporal lobe.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] In the present study, we found reduced mfALFF in the left inferior temporal gyrus of MHD-CI patients, suggesting that abnormal spontaneous brain activity in this brain region might be involved in the underlying neurophysiological mechanisms of CI in MHD-CI patients. While the postcentral gyrus is located in the parietal lobe of the cerebral cortex and is part of the somatosensory center which is involved in daily activities and controls early movement.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] Ma et al. found that patients with ESRD have altered topological properties of sensorimotor network nodes.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] Papoiu et al. found that abnormal activation of the postcentral gyrus correlated with the degree of chronic limb pruritus in ESRD patients.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] In the present study, we found that ALFF was reduced in the left postcentral gyrus, suggesting that MHD-CI patients may also have impaired somatosensory modulation. The present study also found high mALFF values in the right caudate nucleus, a region involved in controlling voluntary movement, learning, and memory.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] The involvement of the right caudate nucleus in cortico-striato-thalamic circuits is relevant to cognitive function.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] The visual area of the inferior temporal gyrus has been found to terminate primarily in the caudate nucleus.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] Therefore, its abnormal brain activity might be considered a compensatory effect of impaired integrity of other brain regions.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] The continued activation of the caudate nucleus might contribute to its dysfunctional integration. In general agreement with previous studies, we found that the DMN, somatomotor network, and limbic network integrity were more impaired in MHD-CI patients than in controls. In contrast, no significant difference was found between the default network brain activity abnormalities compared with MHD-NCI patients. This finding suggests that patients with MHD undergoing maintenance hemodialysis still have spontaneous brain activity abnormalities in several brain regions based on impaired integrity of the default network, providing objective imaging support for studying the neurophysiological mechanisms of cognitive dysfunction in MHD patients.\u003c/p\u003e \u003cp\u003eWe found that patients with MHD-CI had more severe anemia and higher urea nitrogen levels than patients with MHD-NCI, with no significant differences between the remaining clinical indicators. The mALFF value of the left postcentral gyrus and the right caudate nucleus were correlated with hemoglobin level, indicating that anemia may be a risk factor for CI. Anemia decreases the oxygen-carrying capacity of the blood and reduces the oxygenation of the brain.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] It causes decreased cerebral blood volume and thus hypoxia, which aggravates the dysregulation of iron metabolism associated with oxidative stress in the brain, leading to neurodegenerative changes.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] Reza-Zaldivar et al. found varying degrees of improvement in motor and cognitive function after treatment with recombinant human erythropoietin in rats with chronic kidney disease.[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] Their findings suggest that correction of anemia might improve cognitive function. The present study found that mALFF values in the left postcentral gyrus and mfALFF values in the left inferior temporal gyrus were negatively correlated with urea nitrogen levels. Other studies showed that serum urea nitrogen is cleared faster than intracranial urea nitrogen, leading to brain edema due to changes in osmotic pressure on both sides of the blood-brain barrier, further altering cognitive function.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] Tsuruya et al. suggested that urotoxins induce oxidative stress, neuronal death, and frontotemporal lobe volume atrophy, affecting cognitive function. Chen et al. found that abnormal brain activity in ESRD patients was associated with urea nitrogen.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] These findings suggest that a high urea nitrogen level might be a risk factor for CI, explaining why there were more abnormal brain activity brain areas in the MHD-CI group than in the MHD-NCI and control groups.These findings might help explore the neurophysiological mechanisms of MHD-CI patients and guide the early intervention in specific brain regions.\u003c/p\u003e \u003cp\u003eIn our study, we combines quantifiable influencing factors to build different the BPNN prediction model, reflect different prediction precision of combination forecast model and the evaluation results. Compared with model one, the other three models add neuroimaging markers, and the prediction accuracy is significantly improved. The mALFF values in the right caudate nucleus and left postcentral were added to models 2 and 3, respectively, and the correlation coefficient between these two neuroimaging markers and MOCA scores were roughly similar, but the diagnostic efficacy of model 3 was significantly higher than that of model 2, which showed that spontaneous changes in brain activity in the left postcentral gyrus played a key role in the construction of cognitive function models. We combined two neuroimaging markers with clinical indicators to predict cognitive function, and found that the diagnostic efficacy of model 4 was not better than that of model 3, which further confirmed our conjecture. The main reason considered in this study is that the postcentral gyrus is located in the cerebral cortex to control somatic movement, and several studies have shown that in addition to the default network, the somatic motor network of patients with end-stage renal disease is also affected, and this study found that the left postcentral gyrus is positively correlated with cognitive function and hemoglobin level, which may make the input index more correlative, thereby improving the prediction accuracy of the model. The right caudate nucleus is negatively correlated with the MOCA score, and considering that its change may be compensatory, it is not possible to increase the accuracy of the model after joining. The inferior temporal gyrus, which belongs to the default network, is not significantly related to clinical indicators and MOCA scores, and most studies on BPNN suggest that the inclusion of non-significantly related indicators will reduce the accuracy of the model, so this study did not include them in the model input layer. In sum through the modeling, we found the mean ALFF in the regions with significant between-group difference can serve as neuroimaging markers for the cognition impairment assessment of MHD patients. The modeling results in prediction can effectively validate it. The established models could be useful tools in the clinical diagnosis and the pathophysiology mechanism exploration of cognitive impairment for MHD patients.\u003c/p\u003e \u003cp\u003eThere are limitations to the present study. First, this was a cross-sectional study, and it was impossible to determine causal relationships between changes in spontaneous brain activity and clinical indicators.Second, a full set of cognitive function scales was not performed, the neuropsychological scales will be refined for future assessments. Finally, building on current research, more large-sample, multi-center trials and predictive models need to be evaluated.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, we found that MHD-CI patients had abnormal spontaneous brain activity in several brain regions, including the DMN, somatomotor network, and limbic network, the left inferior temporal gyrus and left postcentral gyrus might be the critical regions affecting cognitive function in MHD patients, and correction of anemia and adjustment of urea nitrogen levels might help prevent CI in MHD patients. Based on quantifiable influencing factors, we construct different BPNN prediction models, indicating that the diagnostic efficacy of the model which inputs were hemoglobin, urea nitrogen and mALFF value in the left central posterior gyrus is optimal. Combined with rs-fMRI not only reveals the neurophysiological mechanism of cognitive impairment, but also can serves as a neuroimaging marker for the diagnosis and evaluation of cognitive impairment in patients with MHD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Robert, PhD, Sciworks Editing Beijing for his assistance with language support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthor contributions included conception and study design (SQ and ZJH), data collection or acquisition (SQ, ZJH and WXX), statistical analysis (SQ and ZYT), interpretation of results (SQ and LTQ), drafting the manuscript work or revising it critically for important intellectual content (LTQ and SHF) and approval of final version to be published and agreement to be accountable for the integrity and accuracy of all aspects of the work (All authors).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due the fact that they constitute an excerpt of research in progress but are\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eavailable from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by LGY2020035 from the\u0026nbsp;High-level Health Personnel \u0026ldquo;Six One Project\u0026rdquo; Talent Project of Jiangsu Provence and Grant 20220159 from Changzhou Sci\u0026amp;Tech Program.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of the Second People\u0026apos;s Hospital of Changzhou City (KY032-01).\u0026nbsp;All methods were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent of publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSan A, Hiremagalur B, Muircroft W, Grealish L: \u003cstrong\u003eScreening of Cognitive Impairment in the Dialysis Population: A Scoping Review\u003c/strong\u003e. \u003cem\u003eDement Geriatr Cogn Disord \u003c/em\u003e2017, \u003cstrong\u003e44\u003c/strong\u003e(3-4):182-195.\u003c/li\u003e\n\u003cli\u003eA.S. 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\u003cstrong\u003e35\u003c/strong\u003e(3):147-154.\u003c/li\u003e\n\u003cli\u003eSM S: \u003cstrong\u003eCerebral edema after rapid dialysis is not caused by an increase in brain organic osmolytes\u003c/strong\u003e. \u003cem\u003eJ Am Soc Nephrol\u003c/em\u003e, \u003cstrong\u003e1995 Dec;6(6):1600-6\u003c/strong\u003e.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"resting-state magnetic resonance imaging, maintenance hemodialysis, cognitive dysfunction, low-frequency amplitude, ratiometric low-frequency amplitude, regional homogeneity, Back propagation neural network ","lastPublishedDoi":"10.21203/rs.3.rs-2159328/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2159328/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eTrial registration\u003c/strong\u003e:The study was approved by the Ethics Committee of the Second People's Hospital of Changzhou City (KY032-01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOBJECTIVE: \u003c/strong\u003eTo\u003cstrong\u003e \u003c/strong\u003emeasure changes in spontaneous brain activity in maintenance hemodialysis patients (MHD) with cognitive impairment (CI) base on resting-state functional magnetic resonance imaging (rs-fMRI) and predict cognitive function in maintenance hemodialysis patients by combining spontaneous brain activity and clinical indicators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMETHODS: \u003c/strong\u003eWe selected 50 patients undergoing maintenance hemodialysis at the Second People's Hospital of Changzhou City from September 2020 to December 2021; 28 healthy volunteers were recruited during the same period, and all subjects underwent neuropsychological testing and rs-fMRI. MHD patients were divided into MHD-CI group and MHD-NCI group according to neuropsychological testing score. Data analysis was performed after image preprocessing to explore spontaneous brain activity changes in differential brain regions of MHD-CI patients and to analyze the correlation between spontaneous brain activity and clinical variables. Back propagation neural network (BPNN) was used to predict cognitive function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRESULTS:\u003c/strong\u003e Compared with the MHD-NCI group,\u003cstrong\u003e \u003c/strong\u003ethe patients with MHD-CI had more severe anemia and higher urea nitrogen levels, the lower mALFF values in the left postcentral gyrus, lower mfALFF values in the left inferior temporal gyrus, and greater mALFF values in the right caudate nucleus (p \u0026lt; 0.05). Correlation analysis showed that the mALFF values in the left postcentral gyrus of MHD patients were significantly positively correlated with hemoglobin levels (r = 0.551, p = 0.000) and MOCA scores (r = 0.457, p = 0.001), negatively correlated with urea nitrogen (r = –0.519, p = 0.000). left temporal inferior gyrus mfALFF values were significantly negatively correlated with urea nitrogen levels (r = –0.523, p = 0.000) and positively correlated with MOCA scores (r = 0.295, p = 0.038). The right caudate nucleus mALFF values were negatively correlated with MOCA scores (r = -0.455, p = 0.001). Based on quantifiable influencing factors, we construct different BPNN prediction models, indicating that the diagnostic efficacy of the model which inputs were hemoglobin, urea nitrogen and mALFF value in the left central posterior gyrus is optimal(R\u003csup\u003e2\u003c/sup\u003e=0.8054).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONCLUSION\u003c/strong\u003e: In summary, the left inferior temporal gyrus and left postcentral gyrus might be the critical regions affecting cognitive function in MHD-CI patients, and correction of anemia and adjustment of urea nitrogen levels might help prevent CI in MHD patients. Combined with rs-fMRI not only reveals the neurophysiological mechanism of cognitive impairment, but also can serves as a neuroimaging marker for the diagnosis and evaluation of cognitive impairment in patients with MHD.\u003c/p\u003e","manuscriptTitle":"Altered spontaneous brain activities in maintenance hemodialysis patients with cognitive dysfunction and the construction of cognitive function prediction models","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-10-26 19:15:24","doi":"10.21203/rs.3.rs-2159328/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b127dd3a-1895-4615-bd2b-da856ea677fa","owner":[],"postedDate":"October 26th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-02-28T20:14:20+00:00","versionOfRecord":[],"versionCreatedAt":"2022-10-26 19:15:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2159328","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2159328","identity":"rs-2159328","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}