ArticleNeuroImage2022
A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD.
Article in NeuroImage, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 2 of them syntheses that pooled it.
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
50 citing papers in PubMed, 2 syntheses or guidelines pooled it, 130 citations in OpenAlex.
- Artificial intelligence in ADHD: a global perspective on research hotspots, trends and clinical applications.Frontiers in human neuroscience · 2025Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Individualized fMRI connectivity defines signatures of antidepressant and placebo responses in major depression.Molecular psychiatry · 2023Trial
- ADGPT: A Prior-Guided and GPT-Empowered Framework for MRI-Based Alzheimer's Disease Diagnosis.Journal of imaging informatics in medicine · 2026Article
- Graph Neural Networks in Neuroimaging: Current Status and Biostatistical Considerations for Clinical Deployment.Annals of biomedical engineering · 2026Review
- Robust Multi-Site ADHD Classification via GraphSAGE-Based Functional Connectivity Modeling from rs-fMRI.Bioengineering (Basel, Switzerland) · 2026Article
- Multimodal Representation Learning for Parsing Biological Heterogeneity in Psychiatric Neuroimaging.Biological psychiatry · 2026Review
- ProMix-DGNet: A Process-Aware Spatiotemporal Network for Sintering System Prediction.Sensors (Basel, Switzerland) · 2026Article
- A functional system-informed graph neural network framework to quantify interpretable brain dysfunction in ASD.Neural networks : the official journal of the International Neural Network Society · 2026Article
- Analysis and prediction of schizophrenia patients based on high-order graph attention generative adversarial networks.Scientific reports · 2026Article
- Intelligent diagnosis of major depressive disorder with edge convolution and contrastive learning.PloS one · 2026Article
- BiSCoT: Behavior-Informed Subgroup-Consistent Connectome Template for Interpretable Brain Network Analysis.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- Bayesian Graphical Models for Multiscale Inference in Medical Image-Based Joint Degeneration Analysis.Diagnostics (Basel, Switzerland) · 2025Review
- Deep graph learning of multimodal brain networks defines treatment-predictive signatures in major depression.Molecular psychiatry · 2025Article
- ADHD diagnostics and severity assessment using topological manifold learning of resting-state functional magnetic resonance imaging (rs-fMRI).Neuroimage. Reports · 2025Article
- Automatic diagnosis of autism spectrum disorders in children through resting-state functional magnetic resonance imaging with machine vision.Quantitative imaging in medicine and surgery · 2025Article
- Retinal fundus imaging as biomarker for ADHD using machine learning for screening and visual attention stratification.NPJ digital medicine · 2025Article
- A Lesion-aware Edge-based Graph Neural Network for Predicting Language Ability in Patients with Post-stroke Aphasia.Machine learning in clinical neuroimaging : 7th international workshop, MLCN 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, proceedings. MLCN (Workshop) (7th : 2024 : Marrakesh, Morocco) · 2025Article
- Graph Neural Networks in Brain Connectivity Studies: Methods, Challenges, and Future Directions.Brain sciences · 2024Review
- Predicting adolescent psychopathology from early life factors: A machine learning tutorial.Global epidemiology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
The pathological mechanism of attention deficit hyperactivity disorder (ADHD) is incompletely specified, which leads to difficulty in precise diagnosis. Functional magnetic resonance imaging (fMRI) has emerged as a common neuroimaging technique for studying the brain functional connectome. Most existing methods that have either ignored or simply utilized graph structure, do not fully leverage the potentially important topological information which may be useful in characterizing brain disorders. There is a crucial need for designing novel and efficient approaches which can capture such information. To this end, we propose a new dynamic graph convolutional network (dGCN), which is trained with sparse brain regional connections from dynamically calculated graph features. We also develop a novel convolutional readout layer to improve graph representation. Our extensive experimental analysis demonstrates significantly improved performance of dGCN for ADHD diagnosis compared with existing machine learning and deep learning methods. Visualizations of the salient regions of interest (ROIs) and connectivity based on informative features learned by our model show that the identified functional abnormalities mainly involve brain regions in temporal pole, gyrus rectus, and cerebellar gyri from temporal lobe, frontal lobe, and cerebellum, respectively. A positive correlation was further observed between the identified connectomic abnormalities and ADHD symptom severity. The proposed dGCN model shows great promise in providing a functional network-based precision diagnosis of ADHD and is also broadly applicable to brain connectome-based study of mental disorders.
Indexed as
Identifiers
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.