10 citations · 18 across the 5 of their papers we have counts for
13 papers
Shared Space Transfer Learning for analyzing multi-site fMRI data
Muhammad Yousefnezhad, Alessandro Selvitella, Daoqiang Zhang +2
Multi-voxel pattern analysis (MVPA) learns predictive models from task-based functional magnetic resonance imaging (fMRI) data, for distinguishing when subjects are performing diff…
Deep Representational Similarity Learning for analyzing neural signatures in task-based fMRI dataset
Muhammad Yousefnezhad, Jeffrey Sawalha, Alessandro Selvitella +1
Similarity analysis is one of the crucial steps in most fMRI studies. Representational Similarity Analysis (RSA) can measure similarities of neural signatures generated by differen…
Supervised Hyperalignment for multi-subject fMRI data alignment
Muhammad Yousefnezhad, Alessandro Selvitella, Liangxiu Han +1
Hyperalignment has been widely employed in Multivariate Pattern (MVP) analysis to discover the cognitive states in the human brains based on multi-subject functional Magnetic Reson…
Gradient-based Representational Similarity Analysis with Searchlight for Analyzing fMRI Data
Xiaoliang Sheng, Muhammad Yousefnezhad, Tonglin Xu +2
Representational Similarity Analysis (RSA) aims to explore similarities between neural activities of different stimuli. Classical RSA techniques employ the inverse of the covarianc…
Multi-Objective Cognitive Model: a supervised approach for multi-subject fMRI analysis
Muhammad Yousefnezhad, Daoqiang Zhang
In order to decode the human brain, Multivariate Pattern (MVP) classification generates cognitive models by using functional Magnetic Resonance Imaging (fMRI) datasets. As a standa…
Gradient Hyperalignment for multi-subject fMRI data alignment
Tonglin Xu, Muhammad Yousefnezhad, Daoqiang Zhang
Multi-subject fMRI data analysis is an interesting and challenging problem in human brain decoding studies. The inherent anatomical and functional variability across subjects make…