Brain connectome gradient dysfunction in patients with end-stage renal disease and its association with clinical phenotype and cognitive deficits.
Authors
Affiliations (8)
Affiliations (8)
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, The Second Affiliated Hospital of Air Force Medical University, Xi'an, Shaanxi, China.
- Department of Medical Imaging, Nuclear Industry 215 Hospital of Shaanxi Province, Xianyang, Shaanxi, China.
- Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
- Department of Medical Laboratory, Xidan Group Hospital, Xi'an, Shaanxi, China.
- Department of Nephrology, Nuclear Industry 215 Hospital of Shaanxi Province, Xianyang, Shaanxi, China.
- Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China. [email protected].
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, The Second Affiliated Hospital of Air Force Medical University, Xi'an, Shaanxi, China. [email protected].
- Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China. [email protected].
Abstract
A cortical hierarchical architecture is vital for encoding and integrating sensorimotor-to-cognitive information. However, whether this gradient structure is disrupted in end-stage renal disease (ESRD) patients and how this disruption provides valuable information for potential clinical symptoms remain unknown. We prospectively enrolled 77 ESRD patients and 48 healthy controls. Using resting-state functional magnetic resonance imaging, we studied ESRD-related hierarchical alterations. The Neurosynth platform and machine-learning models with 10-fold cross-validation were applied. ESRD patients had abnormal gradient metrics in core regions of the default mode network, sensorimotor network, and frontoparietal network. These changes correlated with creatinine, depression, and cognitive functions. A logistic regression classifier achieved a maximum performance of 84.8% accuracy and 0.901 area under the ROC curve (AUC). Our results highlight hierarchical imbalances in ESRD patients that correlate with diverse cognitive deficits, which may be used as potential neuroimaging markers for clinical symptoms.