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A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer's disease.

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Title: A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer's disease.
Authors: Zhang, Ying1 (AUTHOR), Xue, Le2 (AUTHOR), Zhang, Shuoyan1 (AUTHOR), Yang, Jiacheng3 (AUTHOR), Zhang, Qi1 (AUTHOR), Wang, Min3 (AUTHOR), Wang, Luyao3 (AUTHOR), Zhang, Mingkai4 (AUTHOR) 596612147@qq.com, Jiang, Jiehui3 (AUTHOR) jiangjiehui@shu.edu.cn, Li, Yunxia5 (AUTHOR) doctorliyunxia@163.com, for the Alzheimer's Disease Neuroimaging Initiative (AUTHOR), Weiner, Michael W. (AUTHOR), Aisen, Paul (AUTHOR), Petersen, Ronald (AUTHOR), Jack, Clifford R. (AUTHOR), Jagust, William (AUTHOR), Trojanowski, John Q. (AUTHOR), Toga, Arthur W. (AUTHOR), Beckett, Laurel (AUTHOR), Green, Robert C. (AUTHOR)
Source: Alzheimer's Research & Therapy. 3/14/2024, Vol. 16 Issue 1, p1-16. 16p.
Database: Academic Search Ultimate
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  Data: A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer's disease.
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  Data: <searchLink fieldCode="JN" term="%22Alzheimer's+Research+%26+Therapy%22">Alzheimer's Research & Therapy</searchLink>. 3/14/2024, Vol. 16 Issue 1, p1-16. 16p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=176032534
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              M: 03
              Text: 3/14/2024
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              Y: 2024
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