Fully exploratory network ICA (FENICA) on resting-state fMRI data.
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Comparison of functional network connectivity for passive-listening and active-response narrative comprehension in adolescentsAn improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity dataHearing without listening: functional connectivity reveals the engagement of multiple nonauditory networks during basic sound processing.The role of BOLD-fMRI in elucidating migraine pathophysiology.Capturing inter-subject variability with group independent component analysis of fMRI data: a simulation study.A highly parallelized framework for computationally intensive MR data analysis.A novel group ICA approach based on multi-scale individual component clustering. Application to a large sample of fMRI data.The relationship between eye movement and vision develops before birth.Behavioral relevance of the dynamics of the functional brain connectome.The spectral diversity of resting-state fluctuations in the human brainFunctional connectivity of the dorsal and median raphe nuclei at restEffects of cranial electrotherapy stimulation on resting state brain activityA group ICA based framework for evaluating resting fMRI markers when disease categories are unclear: application to schizophrenia, bipolar, and schizoaffective disorders.Time course based artifact identification for independent components of resting-state FMRI.A method for independent component graph analysis of resting-state fMRI.Artifact removal in the context of group ICA: A comparison of single-subject and group approaches.Spatially regularized machine learning for task and resting-state fMRI.Automatic selection of resting-state networks with functional magnetic resonance imagingFully exploratory network independent component analysis of the 1000 functional connectomes databaseA Novel Feature-Map Based ICA Model for Identifying the Individual, Intra/Inter-Group Brain Networks across Multiple fMRI Datasets.A SVM-based quantitative fMRI method for resting-state functional network detection.CUDAICA: GPU optimization of Infomax-ICA EEG analysisTesting independent component patterns by inter-subject or inter-session consistency.Fluctuations of Attentional Networks and Default Mode Network during the Resting State Reflect Variations in Cognitive States: Evidence from a Novel Resting-state Experience Sampling Method.
P2860
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P2860
Fully exploratory network ICA (FENICA) on resting-state fMRI data.
description
2010 nî lūn-bûn
@nan
2010 թուականի Օգոստոսին հրատարակուած գիտական յօդուած
@hyw
2010 թվականի օգոստոսին հրատարակված գիտական հոդված
@hy
2010年の論文
@ja
2010年論文
@yue
2010年論文
@zh-hant
2010年論文
@zh-hk
2010年論文
@zh-mo
2010年論文
@zh-tw
2010年论文
@wuu
name
Fully exploratory network ICA (FENICA) on resting-state fMRI data.
@ast
Fully exploratory network ICA (FENICA) on resting-state fMRI data.
@en
type
label
Fully exploratory network ICA (FENICA) on resting-state fMRI data.
@ast
Fully exploratory network ICA (FENICA) on resting-state fMRI data.
@en
prefLabel
Fully exploratory network ICA (FENICA) on resting-state fMRI data.
@ast
Fully exploratory network ICA (FENICA) on resting-state fMRI data.
@en
P2093
P50
P1476
Fully exploratory network ICA (FENICA) on resting-state fMRI data
@en
P2093
C H Kasess
F Fischmeister
P304
P356
10.1016/J.JNEUMETH.2010.07.028
P50
P577
2010-08-03T00:00:00Z