32 citations · 120 across the 18 of their papers we have counts for
6 papers · 1 filter
Learning Robust and Task-Invariant Functional Representation from fMRI through Siamese Self-Supervised Learning
Jiyao Wang, Peiyu Duan, Nicha C. Dvornek +4
Functional magnetic resonance imaging (fMRI) is a powerful tool for investigating human brain function. However, the high cost of data acquisition and the inherent subjectivity of…
STNAGNN: Data-driven Spatio-temporal Brain Connectivity beyond FC
Jiyao Wang, Nicha C. Dvornek, Peiyu Duan +3
In recent years, graph neural networks (GNNs) have been widely applied in the analysis of brain fMRI, yet defining the connectivity between ROIs remains a challenge in noisy fMRI d…
Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity
Nicha C. Dvornek, Xiaoxiao Li, Juntang Zhuang +2
Heterogeneous presentation of a neurological disorder suggests potential differences in the underlying pathophysiological changes that occur in the brain. We propose to model heter…
Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results
Xiaoxiao Li, Yufeng Gu, Nicha Dvornek +3
Deep learning models have shown their advantage in many different tasks, including neuroimage analysis. However, to effectively train a high-quality deep learning model, the aggreg…
Sparsely Grouped Input Variables for Neural Networks
Beibin Li, Nicholas Nuechterlein, Erin Barney +4
In genomic analysis, biomarker discovery, image recognition, and other systems involving machine learning, input variables can often be organized into different groups by their sou…
Graph Neural Network for Interpreting Task-fMRI Biomarkers
Xiaoxiao Li, Nicha C. Dvornek, Yuan Zhou +3
Finding the biomarkers associated with ASD is helpful for understanding the underlying roots of the disorder and can lead to earlier diagnosis and more targeted treatment. A promis…