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For: Feng W, Liu G, Zeng K, Zeng M, Liu Y. A review of methods for classification and recognition of ASD using fMRI data. J Neurosci Methods 2021;:109456. [PMID: 34954253 DOI: 10.1016/j.jneumeth.2021.109456] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
Number Citing Articles
1 He X, Zhao X, Sun Y, Geng P, Zhang X. Application of TBSS-based machine learning models in the diagnosis of pediatric autism. Front Neurol 2023;13. [DOI: 10.3389/fneur.2022.1078147] [Reference Citation Analysis]
2 Kurkin S, Smirnov N, Pitsik E, Kabir MS, Martynova O, Sysoeva O, Portnova G, Hramov A. Features of the resting-state functional brain network of children with autism spectrum disorder: EEG source-level analysis. Eur Phys J Spec Top 2022. [DOI: 10.1140/epjs/s11734-022-00717-0] [Reference Citation Analysis]
3 Shoeibi A, Ghassemi N, Khodatars M, Moridian P, Khosravi A, Zare A, Gorriz JM, Chale-chale AH, Khadem A, Rajendra Acharya U. Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression. Cogn Neurodyn 2022. [DOI: 10.1007/s11571-022-09897-w] [Reference Citation Analysis]
4 Tang S, Nie L, Liu X, Chen Z, Zhou Y, Pan Z, He L. Application of Quantitative Magnetic Resonance Imaging in the Diagnosis of Autism in Children. Front Med 2022;9:818404. [DOI: 10.3389/fmed.2022.818404] [Reference Citation Analysis]
5 Li W, Wang S, Liu G. Transformer-based Model for fMRI Data: ABIDE Results. 2022 7th International Conference on Computer and Communication Systems (ICCCS) 2022. [DOI: 10.1109/icccs55155.2022.9845999] [Reference Citation Analysis]