ArticleNeural computing & applications2021
Performance of machine learning classification models of autism using resting-state fMRI is contingent on sample heterogeneity.
Article in Neural computing & applications, 2021. 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
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
18 citing papers in PubMed.
- A Neuroinformatics Framework for Evaluating Functional Connectivity Metrics in Small-Sample Resting-State fMRI: An Age-Stratified Autism Study.Neuroinformatics · 2026Article
- Multi-Site Classification of Autism Spectrum Disorder Using Spatially Constrained ICA on Resting-State fMRI Networks.Brain sciences · 2026Article
- High-precision machine learning identifies a reproducible functional connectivity signature of autism spectrum diagnosis in a subset of individuals.GigaScience · 2025Article
- Unsupervised Dimensionality Reduction Techniques for the Assessment of ASD Biomarkers.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2025Article
- Extracting interpretable signatures of whole-brain dynamics through systematic comparison.PLoS computational biology · 2024Article
- Extracting interpretable signatures of whole-brain dynamics through systematic comparison.bioRxiv : the preprint server for biology · 2024Article
- Identification of Autism Spectrum Disorder Using Topological Data Analysis.Journal of imaging informatics in medicine · 2024Article
- The Dynamical Biomarkers in Functional Connectivity of Autism Spectrum Disorder Based on Dynamic Graph Embedding.Interdisciplinary sciences, computational life sciences · 2024Article
- Multi-Slice Generation sMRI and fMRI for Autism Spectrum Disorder Diagnosis Using 3D-CNN and Vision Transformers.Brain sciences · 2023Article
- Confounding Effects on the Performance of Machine Learning Analysis of Static Functional Connectivity Computed from rs-fMRI Multi-site Data.Neuroinformatics · 2023Article
- Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey.Biomedicines · 2023Article
- A multi-view convolutional neural network method combining attention mechanism for diagnosing autism spectrum disorder.PloS one · 2023Article
- Autism Spectrum Disorder Detection by Hybrid Convolutional Recurrent Neural Networks from Structural and Resting State Functional MRI Images.Autism research and treatment · 2023Article
- Functional Connectome-Based Predictive Modeling in Autism.Biological psychiatry · 2022Review
- 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
- A review of resting-state fMRI and its use to examine psychiatric disorders.Psychoradiology · 2021Review
- Autism Spectrum Disorder Studies Using fMRI Data and Machine Learning: A Review.Frontiers in neuroscience · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Autism spectrum disorders (ASDs) are heterogeneous neurodevelopmental conditions. In fMRI studies, including most machine learning studies seeking to distinguish ASD from typical developing (TD) samples, cohorts differing in gender and symptom severity composition are often treated statistically as one "ASD group". Using resting-state functional connectivity (FC) data, we implemented random forest to build diagnostic classifiers in 4 ASD samples including a total of 656 participants (N
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.