Twinned neuroimaging analysis contributes to improving the classification of young people with autism spectrum disorder
Author:
Funder
Canadian Institutes of Health Research
Publisher
Springer Science and Business Media LLC
Link
https://www.nature.com/articles/s41598-024-71174-z.pdf
Reference68 articles.
1. Ahammed, M. S. et al. DarkASDNet: Classification of ASD on functional MRI using deep neural network. Front. Neuroinform. 15, 635657. https://doi.org/10.3389/fninf.2021.635657 (2021).
2. Han, J., Jiang, G., Ouyang, G. & Li, X. A multimodal approach for identifying autism spectrum disorders in children. IEEE Trans. Neural Syst. Rehabil. Eng. 30, 2003–2011. https://doi.org/10.1109/TNSRE.2022.3192431 (2022).
3. Kong, Y. et al. Classification of autism spectrum disorder by combining brain connectivity and deep neural network classifier. Neurocomputing 324, 63–68. https://doi.org/10.1016/j.neucom.2018.04.080 (2019).
4. Maenner, M. J. et al. Prevalence and characteristics of autism spectrum disorder among children aged 8 years: Autism and developmental disabilities monitoring network, 11 sites, United States, 2020. MMWR Surveill Summ 72(2), 1–14. https://doi.org/10.15585/mmwr.ss7202a1 (2023).
5. Ha, S., Sohn, I. J., Kim, N., Sim, H. J. & Cheon, K. A. Characteristics of brains in autism spectrum disorder: Structure, function and connectivity across the lifespan. Exp. Neurobiol. 24(4), 273–84. https://doi.org/10.5607/en.2015.24.4.273 (2015).
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