Automatic recognition of resting state fMRI networks with dictionary learning

Keyphrases

• Activation Patterns 33%
• Aging Population 33%
• Automated Recognition 33%
• Automatic Identification 33%
• Automatic Recognition 100%
• Challenging Tasks 33%
• Clinical Population 33%
• Deep Sparse 33%
• Dictionary Leaning 33%
• Dictionary Learning 100%
• Functional Magnetic Resonance Imaging 33%
• Hierarchical Features 33%
• Invasiveness 33%
• Kullback-Leibler Divergence 33%
• Level Decomposition 33%
• Maximum Likelihood Estimation 33%
• Noise Source 33%
• Pediatric Population 33%
• Recognition Algorithm 33%
• Resting-state Functional Connectivity (rs-FC) 33%
• Resting-state Functional Magnetic Resonance Imaging (rs-fMRI) 100%
• Separation Strategy 33%
• Source Separation 33%
• Sparse Matrix Factorization 33%
• Spatial Clustering 33%
• Spatial Features 33%
• Spatial Overlapping 33%
• Suitable for Aging 33%
• Task-free 33%
• Visual Inspection 33%
• Whole-brain fMRI 33%

Computer Science

• Dictionary Learning 100%
• Experimental Result 50%
• Functional State 100%
• Leibler Divergence 50%
• Level Decomposition 50%
• Likelihood Estimate 50%
• maximum-likelihood 50%
• Neuroimaging Technique 50%
• Recognition Algorithm 50%
• Source Separation 50%
• Sparse Matrix Factorization 50%
• Visual Inspection 50%

Neuroscience

• Functional Magnetic Resonance Imaging 100%
• Functional Neuroimaging 25%