[PDF][PDF] Comparison of pre-normalization methods on the accuracy and reliability of group ICA results
Spatial independent component analysis (ICA) has emerged as a robust technique to
identify functionally connected networks in resting-state and task-modulated fMRI data …
identify functionally connected networks in resting-state and task-modulated fMRI data …
[HTML][HTML] Investigating differences in brain functional networks using hierarchical covariate-adjusted independent component analysis
R Shi, Y Guo - The annals of applied statistics, 2016 - ncbi.nlm.nih.gov
Human brains perform tasks via complex functional networks consisting of separated brain
regions. A popular approach to characterize brain functional networks in fMRI studies is …
regions. A popular approach to characterize brain functional networks in fMRI studies is …
Spatial and temporal reproducibility-based ranking of the independent components of BOLD fMRI data
Independent component analysis (ICA) decomposes fMRI data into spatially independent
maps and their corresponding time courses. However, distinguishing the neurobiologically …
maps and their corresponding time courses. However, distinguishing the neurobiologically …
A novel approach for fMRI data analysis based on the combination of sparse approximation and affinity propagation clustering
Clustering analysis has been widely used to detect the functional connectivity from
functional magnetic resonance imaging (fMRI) data. However, it has some limitations such …
functional magnetic resonance imaging (fMRI) data. However, it has some limitations such …
Validating the performance of one-time decomposition for fMRI analysis using ICA with automatic target generation process
Independent component analysis (ICA) has been proven to be effective for functional
magnetic resonance imaging (fMRI) data analysis. However, ICA decomposition requires to …
magnetic resonance imaging (fMRI) data analysis. However, ICA decomposition requires to …
Disjoint subspaces for common and distinct component analysis: Application to the fusion of multi-task FMRI data
Background Data-driven methods such as independent component analysis (ICA) makes
very few assumptions on the data and the relationships of multiple datasets, and hence, are …
very few assumptions on the data and the relationships of multiple datasets, and hence, are …
Ranking and averaging independent component analysis by reproducibility (RAICAR)
Independent component analysis (ICA) is a data‐driven approach that has exhibited great
utility for functional magnetic resonance imaging (fMRI). Standard ICA implementations …
utility for functional magnetic resonance imaging (fMRI). Standard ICA implementations …
Latency (in) sensitive ICA: group independent component analysis of fMRI data in the temporal frequency domain
Independent component analysis (ICA), a data-driven approach utilizing high-order
statistical moments to find maximally independent sources, has found fruitful application in …
statistical moments to find maximally independent sources, has found fruitful application in …
A method to compare the discriminatory power of data-driven methods: Application to ICA and IVA
Background The widespread application of data-driven factorization-based methods, such
as independent component analysis (ICA), to functional magnetic resonance imaging data …
as independent component analysis (ICA), to functional magnetic resonance imaging data …
Constrained independent component analysis based on entropy bound minimization for subgroup identification from multi-subject fMRI data
Identification of subgroups of subjects homogeneous functional networks is a key step for
precision medicine. Independent vector analysis (IVA) is shown to be effective for this task …
precision medicine. Independent vector analysis (IVA) is shown to be effective for this task …