ArticleScientific reports2023
Diagnosis of autism spectrum disorder based on functional brain networks and machine learning.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Smart sensing the spectrum: cutting-edge biosensor and biomarker approaches for early autism detection.Metabolic brain disease · 2026Review
- MACAFNet transformer-based multi-atlas fusion framework for autism spectrum disorder classification using functional connectivity.Scientific reports · 2026Article
- A Multi-Atlas Dynamic Connectivity Transformer Fused with 4D Spatiotemporal Modeling for Autism Spectrum Disorder Recognition.Brain sciences · 2026Article
- Evaluating topological and graph-theoretical approaches to extract complex multimodal brain connectivity patterns in multiple sclerosis.Health information science and systems · 2025Article
- Evidence of neurocognitive and resting state functional connectivity differences in carriers of NRXN1 deletions.Journal of neurodevelopmental disorders · 2025Article
- Deep learning diagnosis plus kinematic severity assessments of neurodivergent disorders.Scientific reports · 2025Article
- Spectral feature modeling with graph signal processing for brain connectivity in autism spectrum disorder.Scientific reports · 2025Article
- Machine learning and complex network analysis of drug effects on neuronal microelectrode biosensor data.Scientific reports · 2025Article
- Harnessing multi-output machine learning approach and dynamical observables from network structure to optimize COVID-19 intervention strategies.Biology methods & protocols · 2025Article
- Case Report: Low-intensity transcranial focused ultrasound stimulation improves social interaction and stereotyped behavior in a boy with autism spectrum disorder.Frontiers in psychiatry · 2025Article
- Challenges in the Differential Classification of Individual Diagnoses from Co-Occurring Autism and ADHD Using Survey Data.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2024Article
- Twinned neuroimaging analysis contributes to improving the classification of young people with autism spectrum disorder.Scientific reports · 2024Article
- Article
- Neurodevelopmental disorders: 2024 update.Free neuropathology · 2024Article
- An Umbrella Review of the Fusion of fMRI and AI in Autism.Diagnostics (Basel, Switzerland) · 2023Review
- A face image classification method of autistic children based on the two-phase transfer learning.Frontiers in psychology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Autism is a multifaceted neurodevelopmental condition whose accurate diagnosis may be challenging because the associated symptoms and severity vary considerably. The wrong diagnosis can affect families and the educational system, raising the risk of depression, eating disorders, and self-harm. Recently, many works have proposed new methods for the diagnosis of autism based on machine learning and brain data. However, these works focus on only one pairwise statistical metric, ignoring the brain network organization. In this paper, we propose a method for the automatic diagnosis of autism based on functional brain imaging data recorded from 500 subjects, where 242 present autism spectrum disorder considering the regions of interest throughout Bootstrap Analysis of Stable Cluster map. Our method can distinguish the control group from autism spectrum disorder patients with high accuracy. Indeed the best performance provides an AUC near 1.0, which is higher than that found in the literature. We verify that the left ventral posterior cingulate cortex region is less connected to an area in the cerebellum of patients with this neurodevelopment disorder, which agrees with previous studies. The functional brain networks of autism spectrum disorder patients show more segregation, less distribution of information across the network, and less connectivity compared to the control cases. Our workflow provides medical interpretability and can be used on other fMRI and EEG data, including small data sets.
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.