Resting state fMRI functional connectivity-based classification using a convolutional neural network architecture
Machine learning techniques have become increasingly popular in the field of resting state
fMRI (functional magnetic resonance imaging) network based classification. However, the …
fMRI (functional magnetic resonance imaging) network based classification. However, the …
Characterizing functional connectivity differences in aging adults using machine learning on resting state fMRI data
The brain at rest consists of spatially distributed but functionally connected regions, called
intrinsic connectivity networks (ICNs). Resting state functional magnetic resonance imaging …
intrinsic connectivity networks (ICNs). Resting state functional magnetic resonance imaging …
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 …
Current methods and new directions in resting state fMRI
J Yang, S Gohel, B Vachha - Clinical imaging, 2020 - Elsevier
Resting state functional connectivity magnetic resonance imaging (rsfcMRI) has become a
key component of investigations of neurocognitive and psychiatric behaviors. Over the past …
key component of investigations of neurocognitive and psychiatric behaviors. Over the past …
Fcnet: a convolutional neural network for calculating functional connectivity from functional mri
Investigation of functional brain connectivity patterns using functional MRI has received
significant interest in the neuroimaging domain. Brain functional connectivity alterations …
significant interest in the neuroimaging domain. Brain functional connectivity alterations …
Ensemble learning with 3D convolutional neural networks for functional connectome-based prediction
The specificity and sensitivity of resting state functional MRI (rs-fMRI) measurements depend
on preprocessing choices, such as the parcellation scheme used to define regions of …
on preprocessing choices, such as the parcellation scheme used to define regions of …
3D convolutional neural networks for classification of functional connectomes
Resting-state functional MRI (rs-fMRI) scans hold the potential to serve as a diagnostic or
prognostic tool for a wide variety of conditions, such as autism, Alzheimer's disease, and …
prognostic tool for a wide variety of conditions, such as autism, Alzheimer's disease, and …
Benchmarking functional connectome-based predictive models for resting-state fMRI
Functional connectomes reveal biomarkers of individual psychological or clinical traits.
However, there is great variability in the analytic pipelines typically used to derive them from …
However, there is great variability in the analytic pipelines typically used to derive them from …
Resting-state fMRI in the human connectome project
Resting-state functional magnetic resonance imaging (rfMRI) allows one to study functional
connectivity in the brain by acquiring fMRI data while subjects lie inactive in the MRI …
connectivity in the brain by acquiring fMRI data while subjects lie inactive in the MRI …
Deep chronnectome learning via full bidirectional long short-term memory networks for MCI diagnosis
Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a
popular approach for disease diagnosis, where discriminating subjects with mild cognitive …
popular approach for disease diagnosis, where discriminating subjects with mild cognitive …