ArticleFrontiers in human neuroscience2021
Neural Biomarkers Distinguish Severe From Mild Autism Spectrum Disorder Among High-Functioning Individuals.
Article in Frontiers in human neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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Who cites it
7 citing papers in PubMed.
- From heterogeneity to translation: data‑driven subtyping of autism in a multilevel framework.Translational psychiatry · 2026Review
- Functional and Metabolic Abnormalities in Primary Visual Cortex and Frontal Eye Fields and Candidate Biomarkers of Acute Acquired Comitant Esotropia.Investigative ophthalmology & visual science · 2026Article
- Diffusion wavelets on connectome: Localizing the sources of diffusion mediating structure-function mapping using graph diffusion wavelets.Network neuroscience (Cambridge, Mass.) · 2025Article
- Attention-based fusion of multiple graphheat networks for structural to functional brain mapping.Scientific reports · 2024Article
- Current Approaches in Computational Psychiatry for the Data-Driven Identification of Brain-Based Subtypes.Biological psychiatry · 2023Review
- Ways to Address Perinatal Mast Cell Activation and Focal Brain Inflammation, including Response to SARS-CoV-2, in Autism Spectrum Disorder.Journal of personalized medicine · 2021Review
- Predictors of functional ability in autism spectrum disorder: A scoping review.Tzu chi medical journalReview
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Several previous studies have reported atypicality in resting-state functional connectivity (FC) in autism spectrum disorder (ASD), yet the relatively small effect sizes prevent us from using these characteristics for diagnostic purposes. Here, canonical correlation analysis (CCA) and hierarchical clustering were used to partition the high-functioning ASD group (i.e., the ASD discovery group) into subgroups. A support vector machine (SVM) model was trained through the 10-fold strategy to predict Autism Diagnostic Observation Schedule (ADOS) scores within the ASD discovery group (
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Registered trials
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