ArticleScientific reports2022
Reproducible neuroimaging features for diagnosis of autism spectrum disorder with machine learning.
Article in Scientific reports, 2022. 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
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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.
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Who cites it
18 citing papers in PubMed, 52 citations in OpenAlex.
- Efficacy and Safety of AltibrainCurrent pharmaceutical design · 2025Trial
- Equity, diversity, and inclusion: variations in clinical and academic radiology.Insights into imaging · 2026Article
- Generating Biologically Relevant Subtypes of Autism Spectrum Disorder with differential responses to Acute Oxytocin Administration in a Randomized Trial using Random Forest Models and K-means Clustering.medRxiv : the preprint server for health sciences · 2026Article
- Neuroplasticity mechanisms of NDBIs in autism: a review from brain connectivity to behavioral improvement.European journal of medical research · 2026Review
- Prediction of Verbal Abilities From Brain Connectivity Data Across the Lifespan Using a Machine Learning Approach.Human brain mapping · 2025Article
- Editorial: Women in radiology: neuroimaging and neurotechnology.Frontiers in radiology · 2025Article
- Deep learning-based feature selection for detection of autism spectrum disorder.Frontiers in artificial intelligence · 2025Article
- Alignment of auditory artificial networks with massive individual fMRI brain data leads to generalisable improvements in brain encoding and downstream tasks.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Unsupervised Dimensionality Reduction Techniques for the Assessment of ASD Biomarkers.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2025Article
- Predictive and Explainable Artificial Intelligence for Neuroimaging Applications.Diagnostics (Basel, Switzerland) · 2024Review
- Conduction Velocity, G-ratio, and Extracellular Water as Microstructural Characteristics of Autism Spectrum Disorder.bioRxiv : the preprint server for biology · 2024Article
- Challenges in multi-task learning for fMRI-based diagnosis: Benefits for psychiatric conditions and CNVs would likely require thousands of patients.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Conduction velocity, G-ratio, and extracellular water as microstructural characteristics of autism spectrum disorder.PloS one · 2024Article
- Delineating a Pathway for the Discovery of Functional Connectome Biomarkers of Autism.Advances in neurobiology · 2024Review
- Network-level enrichment provides a framework for biological interpretation of machine learning results.Network neuroscience (Cambridge, Mass.) · 2024Article
- Novel machine learning approaches for improving the reproducibility and reliability of functional and effective connectivity from functional MRI.Journal of neural engineering · 2023Article
- ASD-GResTM: Deep Learning Framework for ASD classification using Gramian Angular Field.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2023Article
- Brain functional connectivity mirrors genetic pleiotropy in psychiatric conditions.Brain : a journal of neurology · 2023Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Autism spectrum disorder (ASD) is the fourth most common neurodevelopmental disorder, with a prevalence of 1 in 160 children. Accurate diagnosis relies on experts, but such individuals are scarce. This has led to increasing interest in the development of machine learning (ML) models that can integrate neuroimaging features from functional and structural MRI (fMRI and sMRI) to help reveal central nervous system alterations characteristic of ASD. We optimized and compared the performance of 12 of the most popular and powerful ML models. Each was separately trained using 15 different combinations of fMRI and sMRI features and optimized with an unbiased model search. Deep learning models predicted ASD with the highest diagnostic accuracy and generalized well to other MRI datasets. Our model achieves state-of-the-art 80% area under the ROC curve (AUROC) in diagnosis on test data from the IMPAC dataset; and 86% and 79% AUROC on the external ABIDE I and ABIDE II datasets (with further improvement to 93% and 90% after supervised domain adaptation). The highest performing models identified reproducible putative biomarkers for accurate ASD diagnosis in accord with known ASD markers as well as novel cerebellar biomarkers. Such reproducibility lends credence to their tremendous potential for defining and using a set of truly generalizable ASD biomarkers that will advance scientific understanding of neuronal changes in 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.