Work

Hierarchical High-Order Functional Connectivity Networks and Selective Feature Fusion for MCI Classification

Year: 2017

Type: article

Source: Neuroinformatics

DOI: 10.1007/s12021-017-9330-4

Authors: the Alzheimer’s Disease Neuroimaging Initiative, Xiaobo Chen, Han Zhang, Seong-Whan Lee, Dinggang Shen

Institutions: University of North Carolina at Chapel Hill, Korea University

Language: English

Topic: Functional Brain Connectivity Studies

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Natural Science Foundation of China, Jiangsu University, National Institutes of Health, Senior Talent Foundation of Jiangsu University, National Institute on Aging, National Institute of Biomedical Imaging and Bioengineering

Awards: G1600120695, G1920764813, Analyzing Large-Scale Neuroimaging Data in Alzheimer's Disease, G3067560958, G3139536118, Assessing Large-scale Brain Connectivities in Mild Cognitive Impairment, Quantifying Brain Abnormality by Multimodality Neuroimage Analysis, Diagnosis of Alzheimer's Disease Using Dynamic High-Order Brain Networks, Longitudinal Mapping of Human Brain Development in the First Years of Life, Imaging Genomics Based Brain Disease Prediction, G6748413423, G6867685102, Development of Robust Brain Measurement Tools Informed by Ultrahigh Field 7T MRI, G8591159148, G8760360333

OpenAlex ID: W2618983688

FWCI: 3.298

Cites: 57

Cited by: 41

Related to: 10