Altered large-scale functional brain networks in neurological Wilson’s disease

被引:0
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作者
Rixing Jing
Yongsheng Han
Hewei Cheng
Yongzhu Han
Kai Wang
Daniel Weintraub
Yong Fan
机构
[1] Chinese Academy of Sciences,National Laboratory of Pattern Recognition, Institute of Automation
[2] University of Chinese Academy of Sciences,Institute of Neurology
[3] Anhui University of Chinese Medicine,Department of Biomedical Engineering, School of Bioinformatics
[4] Chongqing University of Posts and Telecommunications,Department of Neurology
[5] The First Affiliated Hospital of Anhui Medical University,Section of Geriatric Psychiatry, Perelman School of Medicine
[6] University of Pennsylvania,Department of Radiology, Perelman School of Medicine
[7] University of Pennsylvania,undefined
来源
关键词
Wilson’s disease; Large-scale functional brain networks; Functional magnetic resonance images; Machine learning; Biomarkers;
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学科分类号
摘要
Wilson’s disease patients with neurological symptoms have motor symptoms and cognitive deficits, including frontal executive, visuospatial processing, and memory impairments. Although the brain structural abnormalities associated with Wilson’s disease have been documented, it remains largely unknown how Wilson’s disease affects large-scale functional brain networks. In this study, we investigated functional brain networks in Wilson’s disease. Particularly, we analyzed resting state functional magnetic resonance images of 30 Wilson’s disease patients and 26 healthy controls. First, functional brain networks for each participant were extracted using an independent component analysis method. Then, a computationally efficient pattern classification method was developed to identify discriminative brain functional networks associated with Wilson’s disease. Experimental results indicated that Wilson’s disease patients, compared with healthy controls, had altered large-scale functional brain networks, including the dorsal anterior cingulate cortex and basal ganglia network, the middle frontal gyrus, the dorsal striatum, the inferior parietal lobule, the precuneus, the temporal pole, and the posterior lobe of cerebellum. Classification models built upon these networks distinguished between neurological WD patients and HCs with accuracy up to 86.9% (specificity: 86.7%, sensitivity: 89.7%). The classification scores were correlated with the United Wilson’s Disease Rating Scale measures and durations of disease of the patients. These results suggest that Wilson’s disease patients have multiple aberrant brain functional networks, and classification scores derived from these networks are associated with severity of clinical symptoms.
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页码:1445 / 1455
页数:10
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