Explaining deep learning-based representations of resting state functional connectivity data: focusing on interpreting nonlinear patterns in autism spectrum disorder

Kim, Y., Ravid, O., Zheng, X., Kim, Y., Neria, Y., Lee, S., He, X., & Zhu, X. (2024). Explaining deep learning-based representations of resting state functional connectivity data: focusing on interpreting nonlinear patterns in autism spectrum disorder. Frontiers in Psychiatry, 15. https://doi.org/10.3389/fpsyt.2024.1397093
Authors:
Young-Geun Kim
Orren Ravid
Xinyuan Zheng
Yoojean Kim
Yuval Neria
Seonjoo Lee
Xiaofu He
Xi Zhu
Affiliated Authors:
Young-Geun Kim
Orren Ravid
Yoojean Kim
Yuval Neria
Seonjoo Lee
Xiaofu He
Xi Zhu
Columbia Affiliation:
Subjects:
Analysis of Brain Functional Connectivity Networks (OpenAlex Topic)
Brain-Computer Interfaces in Neuroscience and Medicine (OpenAlex Topic)
Neuronal Oscillations in Cortical Networks (OpenAlex Topic)
Author Keywords:
deep learning
variational autoencoder
resting state fmri
functional connectivity
autism spectrum disorder
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Publication Type:
Article
Unique ID:
10.3389/fpsyt.2024.1397093
PMID:
38832332
DOI:
10.3389/fpsyt.2024.1397093
Journal:
Frontiers in Psychiatry
Publication Date:
Data Source:
PubMed
Source Link:
View PubMed Record
View OpenAlex Record
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Record Created: