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Brain Micro-Structural and Functional Alterations for Cognitive Function Prediction in the End-Stage Renal Disease Patients Undergoing Maintenance Hemodialysis

蒙特利尔认知评估 医学 认知 终末期肾病 磁共振弥散成像 疾病 血液透析 内科学 磁共振成像 心脏病学 认知障碍 放射科 精神科
作者
Jiahui Zheng,Qingjie Sun,Xiangxiang Wu,Weiqiang Dou,Jiechang Pan,Zhuqing Jiao,Tongqiang Liu,Haifeng Shi
出处
期刊:Academic Radiology [Elsevier BV]
卷期号:30 (6): 1047-1055 被引量:6
标识
DOI:10.1016/j.acra.2022.06.019
摘要

The goal of this study was to investigate the relationship between altered brain micro-structure and function, and cognitive function in patients with end-stage renal disease (ESRD) undergoing maintenance hemodialysis. Specially, diffusion kurtosis imaging (DKI), the resting-state functional connectivity (FC) algorithm, and the least squares support vector regression machine (LSSVRM) were utilized to conduct our study.A total of 50 patients and 36 matched healthy controls were prospectively enrolled in our study. All subjects completed the Montreal cognitive assessment scale (MoCA) test. DKI and resting-state functional magnetic resonance imaging were measured. Relationship between DKI parameters, FC, and MoCA scores was evaluated. LSSVRM combined with the whale optimization algorithm (WOA) was used to predict cognitive function scores.In ESRD patients, altered DKI metrics were identified in 12 brain regions. Furthermore, we observed changes in FC values based on regions of interest (ROIs) in nine brain regions, involved in default mode network (DMN), frontoparietal network (FPN), and the limbic system. Significant correlations among DKI values, FC values, and MoCA scores were found. To some extent, altered FC showed significant correlations with changed DKI parameters. Furthermore, optimized prediction models were applied to more accurately predict the cognitive function associated with ESRD patients.Micro-structural and functional brain changes were found in ESRD patients, which may account for the onset of cognitive impairment in affected patients. These quantitative parameters combined with our optimized prediction model may be helpful to establish more reliable imaging markers to detect and monitor cognitive impairment associated with ESRD.
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