Altered resting-state cerebellar-cerebral functional connectivity in patients with end-stage renal disease

Fang, Jie; Miao, Yingying; Zou, Fan; Liu, Yarui; Zuo, Jiangle; Qi, Xiangming; Wang, Haibao

doi:10.1080/0886022x.2023.2238829

Cited by 2 publications

References 46 publications

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Structural–Functional Coupling of Network Communication Predicts Mild Cognitive Impairment in Patients with End Stage Renal Disease: A Longitudinal Machine Learning Study

Yu,

Pang,

et al. 2024

Preprint

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Purpose Cognitive decline is a common brain injury associated with End-Stage Renal Disease (ESRD). The purpose of this study is to investigate the alterations in cognitive impairment structure and functional brain network topology in patients with ESRD. Methods This study analyzed predialysis diffusion and functional MRI data on 90 ESRD patients. Patients were diagnosed into mild cognitive impairment based on MoCA and MMSE scores after a two-year follow-up. By utilizing the topological properties of functional networks, structural networks, and structural functional coupling networks, we developed a machine learning model to predict cognitive impairment in patients. Results Machine learning models utilizing multimodal brain network topology properties show the highest accuracy in predicting MCI in ESRD patients, with an AUC of 0.928 in the training set and 0.903 in the test set. The SHAP analysis indicates that the main cognitive impairment pattern in ESRD patients involves a decrease in hippocampal SFC. Conclusion This study demonstrated that structural and functional damage to the hippocampus was the main mechanism for ESRD leading to cognitive decline. Furthermore, our results have provided a novel and reliable SFC-based machine learning model to precisely predict the cognitive changes in ESRD patients.

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Structural–Functional Coupling of Network Communication Predicts Mild Cognitive Impairment in Patients with End Stage Renal Disease: A Longitudinal Machine Learning Study

Yu,

Pang,

et al. 2024

Preprint

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Altered brain texture features in end-stage renal disease patients: a voxel-based 3D brain texture analysis study

Fang,

Xu,

Zhou

et al. 2024

Front. Neurosci.

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IntroductionCognitive impairment in patients with end-stage renal disease (ESRD) is associated with brain structural damage. However, no prior studies have investigated the relationship between brain texture features and the cognitive function in ESRD patients. This study aimed to investigate changes in brain texture features in ESRD patients and their relationships with cognitive function using voxel-based 3D brain texture analysis (TA), and further predict individual cognitive-related brain damage in ESRD patients.MethodsForty-seven ESRD patients and 45 control subjects underwent whole-brain high-resolution 3D T1-weighted imaging scans and neuropsychological assessments. The voxel-based 3D brain TA was performed to examine inter-group differences in brain texture features. Additionally, within the ESRD group, the relationships of altered texture features with neuropsychological function and clinical indicators were analyzed. Finally, receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive ability of brain texture features for cognitive-related brain damage in ESRD patients.ResultsCompared to the control group, the ESRD group exhibited altered texture features in several brain regions, including the insula, temporal lobe, striatum, cerebellum, and fusiform gyrus (p < 0.05, Gaussian random-field correction). Some of these altered texture features were associated with scores from the Digit Symbol Substitution Test and the Trail Making Test Parts A (p < 0.05), and showed significant correlations with serum creatinine and calcium levels within the ESRD group (p < 0.05). Notably, ROC curve analysis revealed that the texture features in the right insula and left middle temporal gyrus could accurately predict cognitive-related brain damage in ESRD patients, with the area under the curve values exceeding 0.90.ConclusionAberrant brain texture features may be involved in the neuropathological mechanism of cognitive decline, and have high accuracy in predicting cognitive-related brain damage in ESRD patients. TA offers a novel neuroimaging marker to explore the neuropathological mechanisms of cognitive impairment in ESRD patients, and may be a valuable tool to predict cognitive decline.

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Altered resting-state cerebellar-cerebral functional connectivity in patients with end-stage renal disease

Cited by 2 publications

References 46 publications

Structural–Functional Coupling of Network Communication Predicts Mild Cognitive Impairment in Patients with End Stage Renal Disease: A Longitudinal Machine Learning Study

Structural–Functional Coupling of Network Communication Predicts Mild Cognitive Impairment in Patients with End Stage Renal Disease: A Longitudinal Machine Learning Study

Altered brain texture features in end-stage renal disease patients: a voxel-based 3D brain texture analysis study

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