ArticleSensors (Basel, Switzerland)2020
Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.
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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.
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Who cites it
15 citing papers in PubMed, 3 syntheses or guidelines pooled it, 41 citations in OpenAlex.
- The diagnosis of ASD with MRI: a systematic review and meta-analysis.Translational psychiatry · 2024Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Natural Image Reconstruction From fMRI Using Deep Learning: A Survey.Frontiers in neuroscience · 2021Pooled it
- Functional connectivity-based classification and subtyping of major depression for precision mental health: An ensemble graph neural network approach.PLOS digital health · 2026Article
- Personalized progression modelling and prediction in Parkinson's disease with a novel multi-modal graph approach.NPJ Parkinson's disease · 2024Article
- Improving the performance and interpretability on medical datasets using graphical ensemble feature selection.Bioinformatics (Oxford, England) · 2024Article
- A heterogeneous graph convolutional attention network method for classification of autism spectrum disorder.BMC bioinformatics · 2023Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Graph-Based Analysis of Brain Connectivity in Multiple Sclerosis Using Functional MRI: A Systematic Review.Brain sciences · 2023Review
- Explaining graph convolutional network predictions for clinicians-An explainable AI approach to Alzheimer's disease classification.Frontiers in artificial intelligence · 2023Article
- A multi-view convolutional neural network method combining attention mechanism for diagnosing autism spectrum disorder.PloS one · 2023Article
- Retained Primitive Reflexes and Potential for Intervention in Autistic Spectrum Disorders.Frontiers in neurology · 2022Review
- Facial Features Detection System To Identify Children With Autism Spectrum Disorder: Deep Learning Models.Computational and mathematical methods in medicine · 2022Article
- Brain imaging-based machine learning in autism spectrum disorder: methods and applications.Journal of neuroscience methods · 2021Review
- Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future.Sensors (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
With the advancement of brain imaging techniques and a variety of machine learning methods, significant progress has been made in brain disorder diagnosis, in particular Autism Spectrum Disorder. The development of machine learning models that can differentiate between healthy subjects and patients is of great importance. Recently, graph neural networks have found increasing application in domains where the population's structure is modeled as a graph. The application of graphs for analyzing brain imaging datasets helps to discover clusters of individuals with a specific diagnosis. However, the choice of the appropriate population graph becomes a challenge in practice, as no systematic way exists for defining it. To solve this problem, we propose a population graph-based multi-model ensemble, which improves the prediction, regardless of the choice of the underlying graph. First, we construct a set of population graphs using different combinations of imaging and phenotypic features and evaluate them using Graph Signal Processing tools. Subsequently, we utilize a neural network architecture to combine multiple graph-based models. The results demonstrate that the proposed model outperforms the state-of-the-art methods on Autism Brain Imaging Data Exchange (ABIDE) dataset.
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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.