Brain functional network modeling and analysis based on fMRI: a systematic review
Z Wang, J ** functional
brain networks from functional magnetic resonance imaging (fMRI) data compared with …
brain networks from functional magnetic resonance imaging (fMRI) data compared with …
Automatic recognition of fMRI-derived functional networks using 3-D convolutional neural networks
Current functional magnetic resonance imaging (fMRI) data modeling techniques, such as
independent component analysis and sparse coding methods, can effectively reconstruct …
independent component analysis and sparse coding methods, can effectively reconstruct …
Recognizing brain states using deep sparse recurrent neural network
Brain activity is a dynamic combination of different sensory responses and thus brain
activity/state is continuously changing over time. However, the brain's dynamical functional …
activity/state is continuously changing over time. However, the brain's dynamical functional …
Exponentially convergent algorithms for supervised matrix factorization
Supervised matrix factorization (SMF) is a classical machine learning method that
simultaneously seeks feature extraction and classification tasks, which are not necessarily a …
simultaneously seeks feature extraction and classification tasks, which are not necessarily a …
Experimental comparisons of sparse dictionary learning and independent component analysis for brain network inference from fMRI data
In this work, we conduct comprehensive comparisons between four variants of independent
component analysis (ICA) methods and three variants of sparse dictionary learning (SDL) …
component analysis (ICA) methods and three variants of sparse dictionary learning (SDL) …