32 citations · 120 across the 18 of their papers we have counts for
19 papers
A Novel Robot-Assisted Learning Pedagogy for Children with ASD
Laura Boccanfuso, Erin Barney, Marilena Mademtzi +7
Interaction paradigms used in robot-assisted autism intervention have historically employed robots as teachers, clinical assistants, or more-abled peers to promote a variety of soc…
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…
Learning Oculomotor Behaviors from Scanpath
Beibin Li, Nicholas Nuechterlein, Erin Barney +9
Identifying oculomotor behaviors relevant for eye-tracking applications is a critical but often challenging task. Aiming to automatically learn and extract knowledge from existing…
Estimating Reproducible Functional Networks Associated with Task Dynamics using Unsupervised LSTMs
Nicha C. Dvornek, Pamela Ventola, James S. Duncan
We propose a method for estimating more reproducible functional networks that are more strongly associated with dynamic task activity by using recurrent neural networks with long s…
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…
Multiple-shooting adjoint method for whole-brain dynamic causal modeling
Juntang Zhuang, Nicha Dvornek, Sekhar Tatikonda +3
Dynamic causal modeling (DCM) is a Bayesian framework to infer directed connections between compartments, and has been used to describe the interactions between underlying neural p…
Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis
Xiaoxiao Li, Yuan Zhou, Nicha C. Dvornek +4
Understanding how certain brain regions relate to a specific neurological disorder has been an important area of neuroimaging research. A promising approach to identify the salient…
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 Embedding Using Infomax for ASD Classification and Brain Functional Difference Detection
Xiaoxiao Li, Nicha C. Dvornek, Juntang Zhuang +2
Significant progress has been made using fMRI to characterize the brain changes that occur in ASD, a complex neuro-developmental disorder. However, due to the high dimensionality a…
Invertible Network for Classification and Biomarker Selection for ASD
Juntang Zhuang, Nicha C. Dvornek, Xiaoxiao Li +2
Determining biomarkers for autism spectrum disorder (ASD) is crucial to understanding its mechanisms. Recently deep learning methods have achieved success in the classification tas…
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…
A Facial Affect Analysis System for Autism Spectrum Disorder
Beibin Li, Sachin Mehta, Deepali Aneja +4
In this paper, we introduce an end-to-end machine learning-based system for classifying autism spectrum disorder (ASD) using facial attributes such as expressions, action units, ar…
Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery
Xiaoxiao Li, Nicha C. Dvornek, Yuan Zhou +3
Discovering imaging biomarkers for autism spectrum disorder (ASD) is critical to help explain ASD and predict or monitor treatment outcomes. Toward this end, deep learning classifi…
Prediction of severity and treatment outcome for ASD from fMRI
Juntang Zhuang, Nicha C. Dvornek, Xiaoxiao Li +2
Autism spectrum disorder (ASD) is a complex neurodevelopmental syndrome. Early diagnosis and precise treatment are essential for ASD patients. Although researchers have built many…
Prediction of treatment outcome for autism from structure of the brain based on sure independence screening
Juntang Zhuang, Nicha C. Dvornek, Qingyu Zhao +3
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder, and behavioral treatment interventions have shown promise for young children with ASD. However, there is li…
Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI
Xiaoxiao Li, Nicha C. Dvornek, Juntang Zhuang +2
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder. Finding the biomarkers associated with ASD is extremely helpful to understand the underlying roots of the d…
Prediction of Autism Treatment Response from Baseline fMRI using Random Forests and Tree Bagging
Nicha C. Dvornek, Daniel Yang, Archana Venkataraman +4
Treating children with autism spectrum disorders (ASD) with behavioral interventions, such as Pivotal Response Treatment (PRT), has shown promise in recent studies. However, decidi…