Multilevel dynamic generalized structured component analysis for brain connectivity analysis in functional neuroimaging data

DOI10.1007/s11336-015-9440-6zbMath1345.62151OpenAlexW4240222740WikidataQ30895836 ScholiaQ30895836MaRDI QIDQ316795

Kwanghee Jung, Todd S. Woodward, Heungsun Hwang, Yoshio Takane

Publication date: 27 September 2016

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11336-015-9440-6

zbMATH Keywords

multilevel analysisstructural equation modelingalternating least squares (ALS) algorithmbrain connectivity analysisfunctional neuroimaginggeneralized structured component analysismulti-subject datatime series data

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Biomedical imaging and signal processing (92C55) Applications of statistics to psychology (62P15)

Uses Software

• GeSCA

Cites Work

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• Dynamic GSCA (generalized structured component analysis) with applications to the analysis of effective connectivity in functional neuroimaging data
• Generalized structured component analysis with latent interactions
• Additive structure in qualitative data: An alternating least squares method with optimal scaling features
• Generalized structured component analysis
• Principal component analysis with external information on both subjects and variables
• A survey of the sources of noise in fMRI
• Constrained Principal Component Analysis and Related Techniques
• Constrained principal component analysis: A comprehensive theory

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