Trial reportNeuroImage. Clinical2015
Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards.
Trial report in NeuroImage. Clinical, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 130 papers, 6 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
130 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Deep learning approach to predict autism spectrum disorder: a systematic review and meta-analysis.BMC psychiatry · 2024Pooled it
- rs-fMRI and machine learning for ASD diagnosis: a systematic review and meta-analysis.Scientific reports · 2022Pooled it
- Somatosensory Deficits After Stroke: Insights From MRI Studies.Frontiers in neurology · 2022Pooled it
- Machine learning with neuroimaging data to identify autism spectrum disorder: a systematic review and meta-analysis.Neuroradiology · 2021Pooled it
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Multivariate analyses applied to fetal, neonatal and pediatric MRI of neurodevelopmental disorders.NeuroImage. Clinical · 2015Pooled it
- Random support vector machine cluster analysis of resting-state fMRI in Alzheimer's disease.PloS one · 2018Trial
- Review
- 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
- Metaheuristic hyperparameter optimization of deep neural networks for demographic-aware autism spectrum disorder classification.Scientific reports · 2026Article
- A Dual-Stream Transformer with Self-Supervised Contrastive Training for fMRI-Based Autism Spectrum Disorder Classification.Brain sciences · 2026Article
- Algebraic Connectivity Reveals Modulated High-Order Functional Networks in Alzheimer's Disease.ArXiv · 2026Article
- Multi-Site Classification of Autism Spectrum Disorder Using Spatially Constrained ICA on Resting-State fMRI Networks.Brain sciences · 2026Article
- AI-driven neuroanalytic modeling for mental health: multichannel CNN-based autism spectrum disorder detection via facial pattern analysis.Frontiers in computational neuroscience · 2026Article
- Prefronto-thalamic hypoconnectivity in schizophrenia: Monkey to human translation of critical pathways for symptom-related functions.PCN reports : psychiatry and clinical neurosciences · 2025Article
- Predictors of Outcome in Autism Spectrum Disorders: A Perspective for Clinicians and Therapists.Indian journal of psychological medicine · 2025Article
- A Framework for Comparison and Interpretation of Machine Learning Classifiers to Predict Autism on the ABIDE Dataset.Human brain mapping · 2025Article
- The Current Research Landscape on the Machine Learning Application in Autism Spectrum Disorder: A Bibliometric Analysis From 1999 to 2023.Current neuropharmacology · 2025Article
- mGNN-bw: Multi-Scale Graph Neural Network Based on Biased Random Walk Path Aggregation for ASD Diagnosis.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025Article
- Self-Supervised Pre-training Tasks for an fMRI Time-series Transformer in Autism Detection.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
70 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
objectivesAutism spectrum disorders (ASD) are diagnosed based on early-manifesting clinical symptoms, including markedly impaired social communication. We assessed the viability of resting-state functional MRI (rs-fMRI) connectivity measures as diagnostic biomarkers for ASD and investigated which connectivity features are predictive of a diagnosis.
methodsRs-fMRI scans from 59 high functioning males with ASD and 59 age- and IQ-matched typically developing (TD) males were used to build a series of machine learning classifiers. Classification features were obtained using 3 sets of brain regions. Another set of classifiers was built from participants' scores on behavioral metrics. An additional age and IQ-matched cohort of 178 individuals (89 ASD; 89 TD) from the Autism Brain Imaging Data Exchange (ABIDE) open-access dataset (http://fcon_1000.projects.nitrc.org/indi/abide/) were included for replication.
resultsHigh classification accuracy was achieved through several rs-fMRI methods (peak accuracy 76.67%). However, classification via behavioral measures consistently surpassed rs-fMRI classifiers (peak accuracy 95.19%). The class probability estimates, P(ASD|fMRI data), from brain-based classifiers significantly correlated with scores on a measure of social functioning, the Social Responsiveness Scale (SRS), as did the most informative features from 2 of the 3 sets of brain-based features. The most informative connections predominantly originated from regions strongly associated with social functioning.
conclusionsWhile individuals can be classified as having ASD with statistically significant accuracy from their rs-fMRI scans alone, this method falls short of biomarker standards. Classification methods provided further evidence that ASD functional connectivity is characterized by dysfunction of large-scale functional networks, particularly those involved in social information processing.
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.