Integration of temporal and spatial properties of dynamic connectivity networks for automatic diagnosis of brain disease

Keyphrases

• Activity Patterns 11%
• Alzheimer's Disease 11%
• Alzheimer's Disease Neuroimaging Initiative 11%
• Automatic Diagnosis 100%
• Behavioral Aspects 11%
• Brain Activity 11%
• Brain Disease Diagnosis 11%
• Brain Diseases 100%
• Brain Disorders 11%
• Brain Regions 55%
• Changing Profile 11%
• Classification Performance 11%
• Cognitive Aspects 11%
• Dynamic Change 22%
• Dynamic Connectivity 100%
• Functional Connectivity 66%
• Functional Magnetic Resonance Imaging 11%
• Functional Networks 22%
• Functional Sequence 11%
• Imaging Time 11%
• Interaction Patterns 11%
• Learning Framework 11%
• Learning Kernels 11%
• Learning Methods 11%
• Learning Techniques 11%
• Manifold Regularization 11%
• Mild Cognitive Impairment 11%
• Multi-task Feature Learning 11%
• Multiple Kernel Learning 11%
• Network Connectivity 100%
• Networked Learning 11%
• Neural Activity 11%
• Non-overlapping 11%
• Overlap Length 11%
• Prodrome 11%
• Resting-state Functional Magnetic Resonance Imaging (rs-fMRI) 44%
• Spatial Properties 100%
• Spatial Variability 66%
• Spatial-temporal 11%
• Spatiotemporal Interaction 11%
• State-of-the-art Techniques 11%
• Static Representation 11%
• Temporal Properties 100%
• Temporal Variability 44%

Neuroscience

• Alzheimer's Disease 25%
• Brain Disease 100%
• Functional Connectivity 100%
• Functional Magnetic Resonance Imaging 50%
• Mild Cognitive Impairment 12%