Dynamic Functional Connectivity and Graph Convolution Network for Alzheimer's Disease Classification
Author:
Affiliation:
1. Tianjin University
Funder
National Key Research & Development Program of China
National Natural Science Foundation of China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3444884.3444885
Reference10 articles.
1. S. Rathore M. Habes M.A. Iftikhar A. Shacklett C. Davatzikos. 2017. A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages. Neuroimage. 155 (2017) 530-548. DOI:https://doi.org/10.1016/j.neuroimage.2017.03.057. S. Rathore M. Habes M.A. Iftikhar A. Shacklett C. Davatzikos. 2017. A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages. Neuroimage. 155 (2017) 530-548. DOI:https://doi.org/10.1016/j.neuroimage.2017.03.057.
2. Forecasting the global burden of Alzheimer's disease
3. Resting brain dynamics at different timescales capture distinct aspects of human behavior;Liegeois R.;Nat Commun.,2019
4. X.G. Song A. Elazab Y.X. Zhang. 2020. Classification of Mild Cognitive Impairment Based on a Combined High-Order Network and Graph Convolutional Network. Ieee Access. 8 (2020) 42816-42827. DOI:https://doi.org/10.1109/access.2020.2974997. X.G. Song A. Elazab Y.X. Zhang. 2020. Classification of Mild Cognitive Impairment Based on a Combined High-Order Network and Graph Convolutional Network. Ieee Access. 8 (2020) 42816-42827. DOI:https://doi.org/10.1109/access.2020.2974997.
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