Manifold learning for fMRI time-varying functional connectivity.

Gonzalez-Castillo, Javier; Fernandez, Isabel S; Lam, Ka Chun; Handwerker, Daniel A; Pereira, Francisco; Bandettini, Peter A

Gonzalez-Castillo, Javier; Fernandez, Isabel S; Lam, Ka Chun; Handwerker, Daniel A; Pereira, Francisco; Bandettini, Peter A.

Afiliación

Gonzalez-Castillo J; Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD, United States.
Fernandez IS; Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD, United States.
Lam KC; Machine Learning Group, National Institute of Mental Health, Bethesda, MD, United States.
Handwerker DA; Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD, United States.
Pereira F; Machine Learning Group, National Institute of Mental Health, Bethesda, MD, United States.
Bandettini PA; Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD, United States.

Front Hum Neurosci ; 17: 1134012, 2023.

Article en En | MEDLINE | ID: mdl-37497043

Palabras clave

Laplacian Eigenmaps (LE); T-SNE; Uniform Manifold Approximation and Projection (UMAP); data visualization; fMRI; manifold learning; time-varying functional connectivity

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Hum Neurosci Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google