ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2020
Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- Graph Learning in Bioinformatics: A Survey of Graph Neural Network Architectures, Biological Graph Construction and Bioinformatics Applications.Biomolecules · 2026Review
- NeuroTree: Hierarchical Functional Brain Pathway Decoding for Mental Health Disorders.Proceedings of machine learning research · 2025Article
- MASKED MOMENTUM CONTRASTIVE DYNAMIC TRANSFORMER FOR SELF-SUPERVISED FUNCTIONAL CONNECTIVITY REPRESENTATION LEARNING.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- Article
- Deep learning-based feature selection for detection of autism spectrum disorder.Frontiers in artificial intelligence · 2025Article
- An objective quantitative diagnosis of depression using a local-to-global multimodal fusion graph neural network.Patterns (New York, N.Y.) · 2024Article
- Article
- Graph neural network and machine learning analysis of functional neuroimaging for understanding schizophrenia.BMC neuroscience · 2024Article
- Applications of interpretable deep learning in neuroimaging: A comprehensive review.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook.Brain sciences · 2023Review
- Classification of Autism Spectrum Disorder Using rs-fMRI data and Graph Convolutional Networks.Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data · 2022Article
- Brain Connectivity Based Graph Convolutional Networks and Its Application to Infant Age Prediction.IEEE transactions on medical imaging · 2022Article
- Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review.Healthcare (Basel, Switzerland) · 2022Review
- AcneGrader: An ensemble pruning of the deep learning base models to grade acne.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2022Article
- Decoding Task-Based fMRI Data with Graph Neural Networks, Considering Individual Differences.Brain sciences · 2022Article
- CNNG: A Convolutional Neural Networks With Gated Recurrent Units for Autism Spectrum Disorder Classification.Frontiers in aging neuroscience · 2022Article
- BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis.Medical image analysis · 2021Article
- Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future.Sensors (Basel, Switzerland) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
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 regions is using Graph Neural Networks (GNNs), which can be used to analyze graph structured data, e.g. brain networks constructed by functional magnetic resonance imaging (fMRI). We propose an interpretable GNN framework with a novel salient region selection mechanism to determine neurological brain biomarkers associated with disorders. Specifically, we design novel regularized pooling layers that highlight salient regions of interests (ROIs) so that we can infer which ROIs are important to identify a certain disease based on the node pooling scores calculated by the pooling layers. Our proposed framework, Pooling Regularized-GNN (PR-GNN), encourages reasonable ROI-selection and provides flexibility to preserve either individual- or group-level patterns. We apply the PR-GNN framework on a Biopoint Autism Spectral Disorder (ASD) fMRI dataset. We investigate different choices of the hyperparameters and show that PR-GNN outperforms baseline methods in terms of classification accuracy. The salient ROI detection results show high correspondence with the previous neuroimaging-derived biomarkers for ASD.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.