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Found 15 papers, 4 papers with code
STNAGNN: Spatiotemporal Node Attention Graph Neural Network for Task-based fMRI Analysis
Task-based fMRI uses actions or stimuli to trigger task-specific brain responses and measures them using BOLD contrast.
Learning Oculomotor Behaviors from Scanpath
Identifying oculomotor behaviors relevant for eye-tracking applications is a critical but often challenging task.
Estimating Reproducible Functional Networks Associated with Task Dynamics using Unsupervised LSTMs
We demonstrate that the functional networks learned by the LSTM model are more strongly associated with the task activity and dynamics compared to other approaches.
Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity
Heterogeneous presentation of a neurological disorder suggests potential differences in the underlying pathophysiological changes that occur in the brain.
Multiple-shooting adjoint method for whole-brain dynamic causal modeling
Furthermore, MSA uses the adjoint method for accurate gradient estimation in the ODE; since the adjoint method is generic, MSA is a generic method for both linear and non-linear systems, and does not require re-derivation of the algorithm as in EM.
Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis
We propose an interpretable GNN framework with a novel salient region selection mechanism to determine neurological brain biomarkers associated with disorders.
Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results
However, to effectively train a high-quality deep learning model, the aggregation of a significant amount of patient information is required.
Sparsely Grouped Input Variables for Neural Networks
In genomic analysis, biomarker discovery, image recognition, and other systems involving machine learning, input variables can often be organized into different groups by their source or semantic category.
Graph Embedding Using Infomax for ASD Classification and Brain Functional Difference Detection
Here, we model the whole brain fMRI as a graph, which preserves geometrical and temporal information and use a Graph Neural Network (GNN) to learn from the graph-structured fMRI data.
Invertible Network for Classification and Biomarker Selection for ASD
Recently deep learning methods have achieved success in the classification task of ASD using fMRI data.
Graph Neural Network for Interpreting Task-fMRI Biomarkers
Our pipeline can be generalized to other graph feature importance interpretation problems.
A Facial Affect Analysis System for Autism Spectrum Disorder
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, arousal, and valence.
Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery
Cooperative game theory is advantageous here because it directly considers the interaction between features and can be applied to any machine learning method, making it a novel, more accurate way of determining instance-wise biomarker importance from deep learning models.
Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI
Therefore, in this work, we address the problem of interpreting reliable biomarkers associated with identifying ASD; specifically, we propose a 2-stage method that classifies ASD and control subjects using fMRI images and interprets the saliency features activated by the classifier.
Prediction of Autism Treatment Response from Baseline fMRI using Random Forests and Tree Bagging
We propose predicting patient response to PRT from baseline task-based fMRI by the novel application of a random forest and tree bagging strategy.
