ArticleFrontiers in human neuroscience2021
Large-Scale Brain Functional Network Integration for Discrimination of Autism Using a 3-D Deep Learning Model.
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 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The diagnosis of ASD with MRI: a systematic review and meta-analysis.Translational psychiatry · 2024Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- [Classification of systemic lupus erythematosus resting-state functional magnetic resonance imaging data based on the end-to-end model].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- A functional system-informed graph neural network framework to quantify interpretable brain dysfunction in ASD.Neural networks : the official journal of the International Neural Network Society · 2026Article
- Multi-Site Classification of Autism Spectrum Disorder Using Spatially Constrained ICA on Resting-State fMRI Networks.Brain sciences · 2026Article
- Language networks in Autism Spectrum Disorder: A systematic review of connectivity-based fMRI studies.Review journal of autism and developmental disorders · 2025Article
- Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study.Applied sciences (Basel, Switzerland) · 2024Article
- Investigation of Electrical Signals in the Brain of People with Autism Using Effective Connectivity Network.Journal of medical signals and sensors · 2024Article
- Delineating a Pathway for the Discovery of Functional Connectome Biomarkers of Autism.Advances in neurobiology · 2024Review
- Multi-Slice Generation sMRI and fMRI for Autism Spectrum Disorder Diagnosis Using 3D-CNN and Vision Transformers.Brain sciences · 2023Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Deep Learning Classification of Treatment Response in Diabetic Painful Neuropathy: A Combined Machine Learning and Magnetic Resonance Neuroimaging Methodological Study.Neuroinformatics · 2023Article
- RETRACTED: Empirical Study of Autism Spectrum Disorder Diagnosis Using Facial Images by Improved Transfer Learning Approach.Bioengineering (Basel, Switzerland) · 2022Article
- Deep learning in neuroimaging data analysis: Applications, challenges, and solutions.Frontiers in neuroimaging · 2022Review
- Autism Spectrum Disorder Studies Using fMRI Data and Machine Learning: A Review.Frontiers in neuroscience · 2021Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
goalBrain functional networks (BFNs) constructed using resting-state functional magnetic resonance imaging (fMRI) have proven to be an effective way to understand aberrant functional connectivity in autism spectrum disorder (ASD) patients. It is still challenging to utilize these features as potential biomarkers for discrimination of ASD. The purpose of this work is to classify ASD and normal controls (NCs) using BFNs derived from rs-fMRI.
methodsA deep learning framework was proposed that integrated convolutional neural network (CNN) and channel-wise attention mechanism to model both intra- and inter-BFN associations simultaneously for ASD diagnosis. We investigate the effects of each BFN on performance and performed inter-network connectivity analysis between each pair of BFNs. We compared the performance of our CNN model with some state-of-the-art algorithms using functional connectivity features.
resultsWe collected 79 ASD patients and 105 NCs from the ABIDE-I dataset. The mean accuracy of our classification algorithm was 77.74% for classification of ASD versus NCs.
conclusionThe proposed model is able to integrate information from multiple BFNs to improve detection accuracy of ASD. SIGNIFICANCE: These findings suggest that large-scale BFNs is promising to serve as reliable biomarkers for diagnosis of ASD.
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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.