An Unsupervised Feature Learning Approach for Elucidating Hidden Dynamics in rs-fMRI Functional Network Connectivity-Reference-Cited by-同舟云学术

An Unsupervised Feature Learning Approach for Elucidating Hidden Dynamics in rs-fMRI Functional Network Connectivity

Published:2022-07-11 Issue: Volume: Page:
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Container-title:2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
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Author:

Ellis Charles A.¹,Sendi Mohammad S.E.¹,Miller Robyn L.¹,Calhoun Vince D.¹

Affiliation:

1. Georgia State University, Georgia Institute of Technology, Emory University,Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS),Atlanta,GA,USA,30303

Funder

NIH

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9870821/9870822/09871548.pdf?arnumber=9871548

Reference13 articles.

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2. Neuropsychological profile in adult schizophrenia measured with the CMINDS

3. Model Class Reliance: Variable Importance Measures for any Machine Learning Model Class, from the ‘Rashomon’ Perspective;fisher;Arxiv Prepr,2018

4. Disrupted Dynamic Functional Network Connectivity Among Cognitive Control Networks in the Progression of Alzheimer's Disease

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