Machine learning in resting-state fMRI analysis
Abstract Machine learning techniques have gained prominence for the analysis of resting-
state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview …
state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview …
Fundamental functional differences between gyri and sulci: implications for brain function, cognition, and behavior
Folding of the cerebral cortex is a prominent characteristic of mammalian brains. Alterations
or deficits in cortical folding are strongly correlated with abnormal brain function, cognition …
or deficits in cortical folding are strongly correlated with abnormal brain function, cognition …
Modeling task fMRI data via deep convolutional autoencoder
Task-based functional magnetic resonance imaging (tfMRI) has been widely used to study
functional brain networks under task performance. Modeling tfMRI data is challenging due to …
functional brain networks under task performance. Modeling tfMRI data is challenging due to …
A generic framework for embedding human brain function with temporally correlated autoencoder
Learning an effective and compact representation of human brain function from high-
dimensional fMRI data is crucial for studying the brain's functional organization. Traditional …
dimensional fMRI data is crucial for studying the brain's functional organization. Traditional …
GCNs-net: a graph convolutional neural network approach for decoding time-resolved eeg motor imagery signals
Toward the development of effective and efficient brain–computer interface (BCI) systems,
precise decoding of brain activity measured by an electroencephalogram (EEG) is highly …
precise decoding of brain activity measured by an electroencephalogram (EEG) is highly …
Modeling spatio-temporal patterns of holistic functional brain networks via multi-head guided attention graph neural networks (Multi-Head GAGNNs)
Mounting evidence has demonstrated that complex brain function processes are realized by
the interaction of holistic functional brain networks which are spatially distributed across …
the interaction of holistic functional brain networks which are spatially distributed across …
Differentiable neural architecture search for optimal spatial/temporal brain function network decomposition
It has been a key topic to decompose the brain's spatial/temporal function networks from 4D
functional magnetic resonance imaging (fMRI) data. With the advantages of robust and …
functional magnetic resonance imaging (fMRI) data. With the advantages of robust and …
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 …
Characterizing and differentiating task-based and resting state fMRI signals via two-stage sparse representations
A relatively underexplored question in fMRI is whether there are intrinsic differences in terms
of signal composition patterns that can effectively characterize and differentiate task-based …
of signal composition patterns that can effectively characterize and differentiate task-based …
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 …