Identifying complex brain networks using penalized regression methods.
about
The Functional Segregation and Integration Model: Mixture Model Representations of Consistent and Variable Group-Level Connectivity in fMRISparse EEG/MEG source estimation via a group lasso.Multiway Array Decomposition of EEG Spectrum: Implications of Its Stability for the Exploration of Large-Scale Brain Networks.Hierarchical vector auto-regressive models and their applications to multi-subject effective connectivity.Complexity in neurology and psychiatry.
P2860
Identifying complex brain networks using penalized regression methods.
description
2008 nî lūn-bûn
@nan
2008 թուականի Յուլիսին հրատարակուած գիտական յօդուած
@hyw
2008 թվականի հուլիսին հրատարակված գիտական հոդված
@hy
2008年の論文
@ja
2008年論文
@yue
2008年論文
@zh-hant
2008年論文
@zh-hk
2008年論文
@zh-mo
2008年論文
@zh-tw
2008年论文
@wuu
name
Identifying complex brain networks using penalized regression methods.
@ast
Identifying complex brain networks using penalized regression methods.
@en
Identifying complex brain networks using penalized regression methods.
@nl
type
label
Identifying complex brain networks using penalized regression methods.
@ast
Identifying complex brain networks using penalized regression methods.
@en
Identifying complex brain networks using penalized regression methods.
@nl
prefLabel
Identifying complex brain networks using penalized regression methods.
@ast
Identifying complex brain networks using penalized regression methods.
@en
Identifying complex brain networks using penalized regression methods.
@nl
P2093
P2860
P1476
Identifying complex brain networks using penalized regression methods.
@en
P2093
Eduardo Martínez-Montes
José M Sánchez-Bornot
Mayrim Vega-Hernández
Pedro A Valdés-Sosa
P2860
P2888
P304
P356
10.1007/S10867-008-9077-0
P577
2008-07-05T00:00:00Z
P5875
P6179
1016794548