ArticleHuman brain mapping2017
Multiple functional networks modeling for autism spectrum disorder diagnosis.
Article in Human brain mapping, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 4 of them syntheses that pooled it.
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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
25 citing papers in PubMed, 4 syntheses or guidelines pooled it, 49 citations in OpenAlex.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- rs-fMRI and machine learning for ASD diagnosis: a systematic review and meta-analysis.Scientific reports · 2022Pooled it
- Machine learning with neuroimaging data to identify autism spectrum disorder: a systematic review and meta-analysis.Neuroradiology · 2021Pooled it
- Deep learning applications for the classification of psychiatric disorders using neuroimaging data: Systematic review and meta-analysis.NeuroImage. Clinical · 2021Pooled it
- QG-WRN: A Quantum-Enhanced Graph Convolutional Wide Residual Network for ASD Diagnosis via Neuroimaging Sensing Technology.Sensors (Basel, Switzerland) · 2026Article
- Prefronto-thalamic hypoconnectivity in schizophrenia: Monkey to human translation of critical pathways for symptom-related functions.PCN reports : psychiatry and clinical neurosciences · 2025Article
- Article
- The Transition From Homogeneous to Heterogeneous Machine Learning in Neuropsychiatric Research.Biological psychiatry global open science · 2025Review
- Autism spectrum disorder diagnosis based on deep unrolling-based spatial constraint representation.Medical & biological engineering & computing · 2023Article
- Confounding Effects on the Performance of Machine Learning Analysis of Static Functional Connectivity Computed from rs-fMRI Multi-site Data.Neuroinformatics · 2023Article
- Learning to Fuse Multiple Brain Functional Networks for Automated Autism Identification.Biology · 2023Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey.Biomedicines · 2023Article
- Understanding the Role of Connectivity Dynamics of Resting-State Functional MRI in the Diagnosis of Autism Spectrum Disorder: A Comprehensive Study.Bioengineering (Basel, Switzerland) · 2023Article
- Reproducible neuroimaging features for diagnosis of autism spectrum disorder with machine learning.Scientific reports · 2022Article
- Article
- Brain imaging-based machine learning in autism spectrum disorder: methods and applications.Journal of neuroscience methods · 2021Review
- Co-activation pattern alterations in autism spectrum disorder-A volume-wise hierarchical clustering fMRI study.Brain and behavior · 2021Article
- Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder.Sensors (Basel, Switzerland) · 2020Article
- Towards a Multivariate Biomarker-Based Diagnosis of Autism Spectrum Disorder: Review and Discussion of Recent Advancements.Seminars in pediatric neurology · 2020Review
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite countless studies on autism spectrum disorder (ASD), diagnosis relies on specific behavioral criteria and neuroimaging biomarkers for the disorder are still relatively scarce and irrelevant for diagnostic workup. Many researchers have focused on functional networks of brain activities using resting-state functional magnetic resonance imaging (rsfMRI) to diagnose brain diseases, including ASD. Although some existing methods are able to reveal the abnormalities in functional networks, they are either highly dependent on prior assumptions for modeling these networks or do not focus on latent functional connectivities (FCs) by considering discriminative relations among FCs in a nonlinear way. In this article, we propose a novel framework to model multiple networks of rsfMRI with data-driven approaches. Specifically, we construct large-scale functional networks with hierarchical clustering and find discriminative connectivity patterns between ASD and normal controls (NC). We then learn features and classifiers for each cluster through discriminative restricted Boltzmann machines (DRBMs). In the testing phase, each DRBM determines whether a test sample is ASD or NC, based on which we make a final decision with a majority voting strategy. We assess the diagnostic performance of the proposed method using public datasets and describe the effectiveness of our method by comparing it to competing methods. We also rigorously analyze FCs learned by DRBMs on each cluster and discover dominant FCs that play a major role in discriminating between ASD and NC. Hum Brain Mapp 38:5804-5821, 2017. © 2017 Wiley Periodicals, Inc.
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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.