4.5 Article

Group linear non-Gaussian component analysis with applications to neuroimaging

期刊

出版社

ELSEVIER
DOI: 10.1016/j.csda.2022.107454

关键词

Big data; Functional magnetic resonance imaging (fMRI); Group inference; Independent component analysis (ICA); Matrix decomposition; Principal component analysis; Resting-state fMRI

资金

  1. Autism Speaks
  2. National Institute of Mental Health [R01 MH078160, U54 HD079123, K01 MH109766]
  3. National Institute of Neurological Disorders and Stroke [R01 NS048527]
  4. National Institutes of Health/National Center for Research Resources Clinical and Translational Science Award [UL1 TR 000424-06]
  5. National Institute of Biomedical Imaging and Bioengineering [P54 EB15909]
  6. Cornell University Institute of Biotechnology
  7. New York State Division of Science, Technology and Innovation (NYSTAR)
  8. Xerox PARC Faculty Research Award
  9. National Science Foundation [1455172, 1934985, 1940124, 1940276, 2114143]
  10. Cornell University, Atkinson Center for a Sustainable Future
  11. USAID [7200AA18CA00014]
  12. Direct For Computer & Info Scie & Enginr
  13. Division of Computing and Communication Foundations [1934985] Funding Source: National Science Foundation
  14. Direct For Mathematical & Physical Scien
  15. Division Of Mathematical Sciences [1455172, 2114143] Funding Source: National Science Foundation
  16. Office of Advanced Cyberinfrastructure (OAC)
  17. Direct For Computer & Info Scie & Enginr [1940276, 1940124] Funding Source: National Science Foundation

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Independent component analysis (ICA) and linear non-Gaussian component analysis (LNGCA) are important methods for identifying biomarkers in neurological disorders using fMRI. LNGCA outperforms principal component analysis in preserving low-variance features. A novel group LNGCA model is proposed to extract group and individual components, revealing differences in brain network engagement between autism spectrum disorder and typical development.
Independent component analysis (ICA) is an unsupervised learning method popular in functional magnetic resonance imaging (fMRI). Group ICA has been used to search for biomarkers in neurological disorders including autism spectrum disorder and dementia. However, current methods use a principal component analysis (PCA) step that may remove low-variance features. Linear non-Gaussian component analysis (LNGCA) enables simultaneous dimension reduction and feature estimation including low-variance features in single-subject fMRI. A group LNGCA model is proposed to extract group components shared by more than one subject. Unlike group ICA methods, this novel approach also estimates individual (subject-specific) components orthogonal to the group components. To determine the total number of components in each subject, a parametric resampling test is proposed that samples spatially correlated Gaussian noise to match the spatial dependence observed in data. In simulations, estimated group components achieve higher accuracy compared to group ICA. The method is applied to a resting-state fMRI study on autism spectrum disorder in 342 children (252 typically developing, 90 with autism), where the group signals include resting-state networks. The discovered group components appear to exhibit different levels of temporal engagement in autism versus typically developing children, as revealed using group LNGCA. This novel approach to matrix decomposition is a promising direction for feature detection in neuroimaging. (C) 2022 The Author(s). Published by Elsevier B.V.

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