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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 …
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 …
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 …
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 …
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 …
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) …