ArticleFrontiers in human neuroscience2018
The Diagnosis of Autism Spectrum Disorder Based on the Random Neural Network Cluster.
Article in Frontiers in human neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- How has DSM-5 Affected Autism Diagnosis? A 5-Year Follow-Up Systematic Literature Review and Meta-analysis.Journal of autism and developmental disorders · 2020Pooled it
- The Role of Artificial Intelligence for Early Diagnostic Tools of Autism Spectrum Disorder: A Systematic Review.Turkish archives of pediatrics · 2025Article
- Astrocytic gatekeeping of neural circuitry and synaptic balance in an autism mouse model: mechanistic insights beyondFrontiers in cell and developmental biology · 2025Article
- Altered intra- and inter-network brain functional connectivity associated with prolonged screen time in pre-school children with autism spectrum disorder.European journal of pediatrics · 2024Article
- Functional brain network alterations in the co-occurrence of autism spectrum disorder and attention deficit hyperactivity disorder.European child & adolescent psychiatry · 2024Article
- Discovery of eQTL Alleles Associated with Autism Spectrum Disorder: A Case-Control Study.Journal of autism and developmental disorders · 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
- Fusing Multiview Functional Brain Networks by Joint Embedding for Brain Disease Identification.Journal of personalized medicine · 2023Article
- Systematic Bibliometric and Visualized Analysis of Research Hotspots and Trends on Autism Spectrum Disorder Neuroimaging.Disease markers · 2022Article
- Brain imaging-based machine learning in autism spectrum disorder: methods and applications.Journal of neuroscience methods · 2021Review
- Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder.Computational and mathematical methods in medicine · 2020Article
- Machine Learning Methods for Diagnosing Autism Spectrum Disorder and Attention- Deficit/Hyperactivity Disorder Using Functional and Structural MRI: A Survey.Frontiers in neuroinformatics · 2020Review
- A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis.Frontiers in neuroscience · 2020Review
- A network clustering based feature selection strategy for classifying autism spectrum disorder.BMC medical genomics · 2019Article
- Application of Single-Nucleotide Polymorphisms in the Diagnosis of Autism Spectrum Disorders: A Preliminary Study with Artificial Neural Networks.Journal of molecular neuroscience : MN · 2019Article
- ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data.Frontiers in neuroinformatics · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As the autism spectrum disorder (ASD) is highly heritable, pervasive and prevalent, the clinical diagnosis of ASD is vital. In the existing literature, a single neural network (NN) is generally used to classify ASD patients from typical controls (TC) based on functional MRI data and the accuracy is not very high. Thus, the new method named as the random NN cluster, which consists of multiple NNs was proposed to classify ASD patients and TC in this article. Fifty ASD patients and 42 TC were selected from autism brain imaging data exchange (ABIDE) database. First, five different NNs were applied to build five types of random NN clusters. Second, the accuracies of the five types of random NN clusters were compared to select the highest one. The random Elman NN cluster had the highest accuracy, thus Elman NN was selected as the best base classifier. Then, we used the significant features between ASD patients and TC to find out abnormal brain regions which include the supplementary motor area, the median cingulate and paracingulate gyri, the fusiform gyrus (FG) and the insula (INS). The proposed method provides a new perspective to improve classification performance and it is meaningful for the 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 OpenQuestion graph.