Cargando…
A technical review of canonical correlation analysis for neuroscience applications
Collecting comprehensive data sets of the same subject has become a standard in neuroscience research and uncovering multivariate relationships among collected data sets have gained significant attentions in recent years. Canonical correlation analysis (CCA) is one of the powerful multivariate tools...
Autores principales: | Zhuang, Xiaowei, Yang, Zhengshi, Cordes, Dietmar |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons, Inc.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7416047/ https://www.ncbi.nlm.nih.gov/pubmed/32592530 http://dx.doi.org/10.1002/hbm.25090 |
Ejemplares similares
-
Disentangling time series between brain tissues improves fMRI data quality using a time-dependent deep neural network
por: Yang, Zhengshi, et al.
Publicado: (2020) -
Single-scale time-dependent window-sizes in sliding-window dynamic functional connectivity analysis: A validation study
por: Zhuang, Xiaowei, et al.
Publicado: (2020) -
Robust Motion Regression of Resting-State Data Using a Convolutional Neural Network Model
por: Yang, Zhengshi, et al.
Publicado: (2019) -
The Smoothing Artifact of Spatially Constrained Canonical Correlation Analysis in Functional MRI
por: Cordes, Dietmar, et al.
Publicado: (2012) -
Performing Sparse Regularization and Dimension Reduction Simultaneously in Multimodal Data Fusion
por: Yang, Zhengshi, et al.
Publicado: (2019)