Cargando…
A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI Analysis
Joint blind source separation (JBSS) has wide applications in modeling latent structures across multiple related datasets. However, JBSS is computationally prohibitive with high-dimensional data, limiting the number of datasets that can be included in a tractable analysis. Furthermore, JBSS may not...
Autores principales: | Sun, Mingyu, Gabrielson, Ben, Akhonda, Mohammad Abu Baker Siddique, Yang, Hanlu, Laport, Francisco, Calhoun, Vince, Adali, Tülay |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256022/ https://www.ncbi.nlm.nih.gov/pubmed/37300060 http://dx.doi.org/10.3390/s23115333 |
Ejemplares similares
-
Multimodal subspace independent vector analysis captures latent subspace structures in large multimodal neuroimaging studies
por: Li, Xinhui, et al.
Publicado: (2023) -
Association of Neuroimaging Data with Behavioral Variables: A Class of Multivariate Methods and Their Comparison Using Multi-Task FMRI Data
por: Akhonda, M. A. B. S., et al.
Publicado: (2022) -
Identification of Homogeneous Subgroups from Resting-State fMRI Data
por: Yang, Hanlu, et al.
Publicado: (2023) -
Group Study of Simulated Driving fMRI Data by Multiset Canonical Correlation Analysis
por: Li, Yi-Ou, et al.
Publicado: (2011) -
Preserving subject variability in group fMRI analysis: performance evaluation of GICA vs. IVA
por: Michael, Andrew M., et al.
Publicado: (2014)