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Found 10 papers, 6 papers with code
Soft Matching Distance: A metric on neural representations that captures single-neuron tuning
Common measures of neural representational (dis)similarity are designed to be insensitive to rotations and reflections of the neural activation space.
NeuroGen: activation optimized image synthesis for discovery neuroscience
NeuroGen combines an fMRI-trained neural encoding model of human vision with a deep generative network to synthesize images predicted to achieve a target pattern of macro-scale brain activation.
Neural encoding with visual attention
Using concurrent eye-tracking and functional Magnetic Resonance Imaging (fMRI) recordings from a large cohort of human subjects watching movies, we first demonstrate that leveraging gaze information, in the form of attentional masking, can significantly improve brain response prediction accuracy in a neural encoding model.
From Connectomic to Task-evoked Fingerprints: Individualized Prediction of Task Contrasts fromResting-state Functional Connectivity
Resting-state functional MRI (rsfMRI) yields functional connectomes that can serve as cognitive fingerprints of individuals.
From Connectomic to Task-evoked Fingerprints: Individualized Prediction of Task Contrasts from Resting-state Functional Connectivity
Resting-state functional MRI (rsfMRI) yields functional connectomes that can serve as cognitive fingerprints of individuals.
A shared neural encoding model for the prediction of subject-specific fMRI response
The increasing popularity of naturalistic paradigms in fMRI (such as movie watching) demands novel strategies for multi-subject data analysis, such as use of neural encoding models.
Detecting abnormalities in resting-state dynamics: An unsupervised learning approach
Resting-state functional MRI (rs-fMRI) is a rich imaging modality that captures spontaneous brain activity patterns, revealing clues about the connectomic organization of the human brain.
Machine learning in resting-state fMRI analysis
Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI.
Ensemble learning with 3D convolutional neural networks for connectome-based prediction
The specificty and sensitivity of resting state functional MRI (rs-fMRI) measurements depend on pre-processing choices, such as the parcellation scheme used to define regions of interest (ROIs).
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 stroke.
