ArticleBrain connectivity2017
Distributed Intrinsic Functional Connectivity Patterns Predict Diagnostic Status in Large Autism Cohort.
Article in Brain connectivity, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.
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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
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Who cites it
22 citing papers in PubMed, 3 syntheses or guidelines pooled it, 39 citations in OpenAlex.
- 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
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Optimizing functional connectivity scanning conditions for predicting autistic traits.medRxiv : the preprint server for health sciences · 2026Article
- Optimizing functional connectivity scanning conditions for predicting autistic traits.Nature. Mental health · 2026Article
- Prediction of postoperative nausea and vomiting in patients undergoing sedated gastrointestinal endoscopy based on machine learning.Annals of medicine · 2025Article
- Generalizable and transportable resting-state neural signatures characterized by functional networks, neurotransmitters, and clinical symptoms in autism.Molecular psychiatry · 2025Article
- Delineating a Pathway for the Discovery of Functional Connectome Biomarkers of Autism.Advances in neurobiology · 2024Review
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey.Biomedicines · 2023Article
- Assessing Predictive Ability of Dynamic Time Warping Functional Connectivity for ASD Classification.International journal of biomedical imaging · 2023Article
- Prediction and Analysis of Autism Spectrum Disorder Using Machine Learning Techniques.Journal of healthcare engineering · 2023Article
- 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
- Functional Connectome-Based Predictive Modeling in Autism.Biological psychiatry · 2022Review
- Resting-state functional connectivity identifies individuals and predicts age in 8-to-26-month-olds.Developmental cognitive neuroscience · 2022Article
- Transcriptomic analysis of isolated and pooled human postmortem cerebellar Purkinje cells in autism spectrum disorders.Frontiers in genetics · 2022Article
- Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review.Frontiers in molecular neuroscience · 2022Review
- Brain imaging-based machine learning in autism spectrum disorder: methods and applications.Journal of neuroscience methods · 2021Review
- Performance of machine learning classification models of autism using resting-state fMRI is contingent on sample heterogeneity.Neural computing & applications · 2021Article
- Functional Connectivities Are More Informative Than Anatomical Variables in Diagnostic Classification of Autism.Brain connectivity · 2019Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Diagnosis of autism spectrum disorder (ASD) currently relies on behavioral observations because brain markers are unknown. Machine learning approaches can identify patterns in imaging data that predict diagnostic status, but most studies using functional connectivity MRI (fcMRI) data achieved only modest accuracies of 60-80%. We used conditional random forest (CRF), an ensemble learning technique protected against bias from feature correlation (which exists in fcMRI matrices). We selected 252 low-motion resting-state functional MRI scans from the Autism Brain Imaging Data Exchange, including 126 typically developing (TD) and 126 ASD participants, matched for age, nonverbal IQ, and head motion. A matrix of functional connectivities between 220 functionally defined regions of interest was used for diagnostic classification. In several runs, we achieved accuracies of 92-99% for classifiers with >300 features (most informative connections). Features, including pericentral somatosensory and motor regions, were disproportionately informative. Findings differed partially from a previous study in the same sample that used feature selection with random forest (which is biased by feature correlations). External validation in a smaller in-house data set, however, achieved only 67-71% accuracy. The large number of features in optimal models can be attributed to etiological heterogeneity under the clinical ASD umbrella. Lower accuracy in external validation is expected due to differences in unknown composition of ASD variants across samples. High accuracy in the main data set is unlikely due to noise overfitting, but rather indicates optimized characterization of a given cohort.
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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.