ArticleFrontiers in neuroscience2018
Topological Properties of Resting-State fMRI Functional Networks Improve Machine Learning-Based Autism Classification.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 5 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
41 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- 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
- rs-fMRI and machine learning for ASD diagnosis: a systematic review and meta-analysis.Scientific reports · 2022Pooled it
- Machine learning with neuroimaging data to identify autism spectrum disorder: a systematic review and meta-analysis.Neuroradiology · 2021Pooled it
- Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review.Frontiers in neuroscience · 2019Pooled it
- AI and neuroimaging in autism spectrum disorder: advances in diagnosis, methodological challenges and future directions.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Review
- Age-stratified individual-specific subspace of autism spectrum disorder based on common orthogonal basis extraction algorithm improves the accuracy of clinical symptoms prediction.BMC psychiatry · 2026Article
- AI-driven neuroanalytic modeling for mental health: multichannel CNN-based autism spectrum disorder detection via facial pattern analysis.Frontiers in computational neuroscience · 2026Article
- 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
- Generalizable and transportable resting-state neural signatures characterized by functional networks, neurotransmitters, and clinical symptoms in autism.Molecular psychiatry · 2025Article
- A Framework for Comparison and Interpretation of Machine Learning Classifiers to Predict Autism on the ABIDE Dataset.Human brain mapping · 2025Article
- Blurred magnitude homology of functional connectome for ASD diagnosis.Frontiers in psychiatry · 2025Article
- Article
- Multi-Slice Generation sMRI and fMRI for Autism Spectrum Disorder Diagnosis Using 3D-CNN and Vision Transformers.Brain sciences · 2023Article
- ASDHeliyon · 2023Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Exploring interpretable graph convolutional networks for autism spectrum disorder diagnosis.International journal of computer assisted radiology and surgery · 2023Article
- Autism spectrum disorder recognition based on multi-view ensemble learning with multi-site fMRI.Cognitive neurodynamics · 2023Article
- The diagnostic performance of machine learning based on resting-state functional magnetic resonance imaging data for major depressive disorders: a systematic review and meta-analysis.Frontiers in neuroscience · 2023Review
- Prediction and Analysis of Autism Spectrum Disorder Using Machine Learning Techniques.Journal of healthcare engineering · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Automatic algorithms for disease diagnosis are being thoroughly researched for use in clinical settings. They usually rely on pre-identified biomarkers to highlight the existence of certain problems. However, finding such biomarkers for neurodevelopmental disorders such as Autism Spectrum Disorder (ASD) has challenged researchers for many years. With enough data and computational power, machine learning (ML) algorithms can be used to interpret the data and extract the best biomarkers from thousands of candidates. In this study, we used the fMRI data of 816 individuals enrolled in the Autism Brain Imaging Data Exchange (ABIDE) to introduce a new biomarker extraction pipeline for ASD that relies on the use of graph theoretical metrics of fMRI-based functional connectivity to inform a support vector machine (SVM). Furthermore, we split the dataset into 5 age groups to account for the effect of aging on functional connectivity. Our methodology achieved better results than most state-of-the-art investigations on this dataset with the best model for the >30 years age group achieving an accuracy, sensitivity, and specificity of 95, 97, and 95%, respectively. Our results suggest that measures of centrality provide the highest contribution to the classification power of the models.
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.