Search Results for author:
Found 11 papers, 7 papers with code
Modulating human brain responses via optimal natural image selection and synthetic image generation
Furthermore, aTLfaces and FBA1 had higher activation in response to maximal synthetic images compared to maximal natural images.
Decoding natural image stimuli from fMRI data with a surface-based convolutional network
In this work, we propose a novel approach for this task, which we call Cortex2Image, to decode visual stimuli with high semantic fidelity and rich fine-grained detail.
Personalized visual encoding model construction with small data
Our approach shows the potential to use previously collected, deeply sampled data to efficiently create accurate, personalized encoding models and, subsequently, personalized optimal synthetic images for new individuals scanned under different experimental conditions.
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 from Resting-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 fromResting-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.
