ArticleFrontiers in neuroscience2026
Machine-learning classification of children and adolescents with ASD using resting-state frequency-specific intrinsic activity.
Article in Frontiers in neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
No grant is acknowledged in the PubMed record.
Abstract
Background: Autism spectrum disorder (ASD) is characterized by heterogeneous developmental trajectories, yet it remains unclear whether frequency-specific resting-state functional magnetic resonance imaging (rs-fMRI) features can distinguish age-defined developmental stages within the condition. Methods: We analyzed rs-fMRI data from 251 participants with ASD, comprising 146 children and 105 adolescents aggregated from ten sites in the Autism Brain Imaging Data Exchange (ABIDE). ALFF and ReHo were computed across three frequency bands: Conventional (0.01-0.08 Hz), slow-4 (0.027-0.073 Hz), and slow-5 (0.01-0.027 Hz). Region-of-interest features were extracted using the 246-region Brainnetome Atlas. To ensure rigorous generalization, participants were divided into a stratified training set (80%, Results: The slow-4 and Conventional-band features showed higher held-out ASD test-set performance than slow-5 features. The best single-metric model by area under the receiver operating characteristic curve (AUC) was slow-4 ReHo Logistic Regression, which achieved an AUC of 0.811 and accuracy of 0.745. The exploratory combined model using slow-4 ALFF and ReHo features achieved the highest overall AUC of 0.819 (accuracy = 0.725). SHAP analysis identified distributed model-contributing regions in the slow-4 ReHo model, including the inferior parietal lobule, lateral occipital cortex, middle and inferior frontal gyri, basal ganglia, and thalamus. Conclusion: Frequency-specific resting-state features, particularly local synchronization in the slow-4 band, capture developmental-stage-related variation within ASD. The involvement of frontoparietal, visual, and subcortical networks suggests that developmental heterogeneity in ASD is supported by distributed reorganization of intrinsic brain activity. These findings highlight the potential of frequency-specific rs-fMRI metrics as candidate markers for characterizing neurodevelopmental stages in ASD, warranting further validation in longitudinal and independent cohorts.
Indexed as
Identifiers
What Socratic 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 Socratic graph.