Cargando…
Construction of embedded fMRI resting-state functional connectivity networks using manifold learning
We construct embedded functional connectivity networks (FCN) from benchmark resting-state functional magnetic resonance imaging (rsfMRI) data acquired from patients with schizophrenia and healthy controls based on linear and nonlinear manifold learning algorithms, namely, Multidimensional Scaling, I...
Autores principales: | Gallos, Ioannis K., Galaris, Evangelos, Siettos, Constantinos I. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8286923/ https://www.ncbi.nlm.nih.gov/pubmed/34367362 http://dx.doi.org/10.1007/s11571-020-09645-y |
Ejemplares similares
-
ISOMAP and machine learning algorithms for the construction of embedded functional connectivity networks of anatomically separated brain regions from resting state fMRI data of patients with Schizophrenia
por: Gallos, Ioannis K, et al.
Publicado: (2021) -
Construction of functional brain connectivity networks from fMRI data with driving and modulatory inputs: an extended conditional Granger causality approach
por: Almpanis, Evangelos, et al.
Publicado: (2020) -
Manifold learning for fMRI time-varying functional connectivity
por: Gonzalez-Castillo, Javier, et al.
Publicado: (2023) -
Network Connectivity in Epilepsy: Resting State fMRI and EEG–fMRI Contributions
por: Centeno, Maria, et al.
Publicado: (2014) -
Dynamic effective connectivity in resting state fMRI
por: Park, Hae-Jeong, et al.
Publicado: (2018)