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Comparison of multi-subject ICA methods for analysis of fMRI data
Erik Barry Erhardt
, Srinivas Rachakonda
,
Edward J. Bedrick
, Elena A. Allen
, Tülay Adali
, Vince D. Calhoun
Research output
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Contribution to journal
›
Article
›
peer-review
614
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Keyphrases
Back Reconstruction
50%
Computationally Intensive
16%
Connected Network
16%
Course Mapping
33%
Data Reduction Methods
16%
Dual Regression
16%
Estimation Time
16%
Evidence-based Recommendations
16%
Functional Magnetic Resonance Imaging
100%
Group Independent Component Analysis
33%
Group Level
16%
Imaging Task
16%
Independent Pattern
16%
International Cooperative Ataxia Rating Scale (ICARS)
100%
Level Reduction
16%
Mixed Component
16%
Mixed Functionals
16%
Multi-subject
100%
Noise Free
33%
Principal Coordinate Analysis (PCoA)
83%
Probabilistic Independent Component Analysis
16%
Probabilistic PCA
16%
Probabilistic Principal Component Analysis
16%
Reconstruction Approach
33%
Reduction Strategies
16%
Spatial Independent Component Analysis (sICA)
16%
Spatial Maps
83%
Subject Group
16%
Subject-independent
100%
Subject-level
50%
Subject-specific
83%
Temporal Regression
50%
Mathematics
Approximates
9%
Connected Network
9%
Data Reduction
9%
Independent Component
100%
Magnetic Resonance Imaging
100%
Principal Component Analysis
63%
Reduction Method
9%
Computer Science
Component Analysis
60%
Connected Network
10%
Data Reduction
10%
Imaging Signal
10%
Independent Component
10%
Independent Component Analysis
100%
Principal Components
60%
Biochemistry, Genetics and Molecular Biology
Magnetism
100%
Principal Component Analysis
100%
Reconstruction
50%
Earth and Planetary Sciences
Back Analysis
16%
Data Transmission
16%
Principal Component Analysis
100%
Economics, Econometrics and Finance
Principal Components
100%