ARCHIVES

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data and derivatives, Frontiers in Neuroinformatics, 7, Available at: https://doi.org/10.3389/conf.fninf.2013.09.00041
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falls short of biomarker standards. NeuroImage Clinical, 7, 359–366. https://doi.org/10.1016/j.nicl.2014.12.013
3. Yahata, N., Morimoto, J., Hashimoto, R., Lisi, G., Shibata, K., Kawakubo, Y., Kuwabara, H., Kuroda, M., Yamada, T., Megumi, F., Imamizu,
H., Náñez, J. E., Sr, Takahashi, H., Okamoto, Y., Kasai, K., Kato, N., Sasaki, Y., Watanabe, T., & Kawato, M. (2016). A small number of
abnormal brain connections predicts adult autism spectrum disorder. Nature Communications, 7(1). https://doi.org/10.1038/ncomms11254
4. Dong, W., Li, Y., Zeng, W., Chen, L., Yan, H., Siok, W. T., & Wang, N. (2024, December 31). STARFormer: A Novel Spatio-Temporal
Aggregation Reorganization Transformer of FMRI for Brain Disorder Diagnosis. arXiv.org. https://arxiv.org/abs/2501.00378
5. Ingalhalikar M., Shinde S., Karmarkar A., et al. (2021), Functional Connectivity-Based Prediction of Autism on Site harmonized ABIDE
Dataset. IEEE Transactions on Biomedical Engineering, 68(12), 3628–3637, Available at: https://doi.org/10.1109/tbme.2021.3080259
6. Heinsfeld, A. S., Franco, A. R., Craddock, R. C., Buchweitz, A., & Meneguzzi, F. (2017). Identification of autism spectrum disorder using
deep learning and the ABIDE dataset. NeuroImage Clinical, 17, 16–23. https://doi.org/10.1016/j.nicl.2017.08.017
7. Kumar, B. S., & Jayaraj, D. (2023), Zealous Particle Swarm Optimization Based Reliable Multi-Layer Perceptron Neural Networks For
Autism Spectrum Disorder Classification, Journal of Theoretical and Applied Information Technology, 101 (1), Available at:
https://www.jatit.org/volumes/Vol101No1/26Vol101No1.pdf
8. Z. Sherkatghanad, M. Akhondzadeh, S. Salari, et al. (2020), Automated detection of autism spectrum disorder using a convolutional neural
network, Frontiers in Neuroscience, 13, Available at: https://doi.org/10.3389/fnins.2019.01325
9. L. Qiu, & J. Zhai, (2024), A hybrid CNN-SVM model for enhanced autism diagnosis, PLoS ONE, 19(5), e0302236, Available at:
https://doi.org/10.1371/journal.pone.0302236
10. S. Gupta, M.R.I Bhuiyan, S.S. Chowa, et al. (2024), Enhancing Autism Spectrum Disorder Classification with Lightweight Quantized CNNs
and Federated Learning on ABIDE-1 Dataset, Mathematics, 12(18), 2886, Available at: https://doi.org/10.3390/math12182886
11. Y. Wang, J. Liu, Y. Xiang, et al. (2021), MAGE: Automatic diagnosis of autism spectrum disorders using multi-atlas graph convolutional
networks and ensemble learning, Neurocomputing, 469, 346–353, Available at: https://doi.org/10.1016/j.neucom.2020.06.152
12. S. Liu, S. Wang, C. Sun, et al. (2024), DeepGCN based on variable multi‐graph and multimodal data for ASD diagnosis, CAAI Transactions
on Intelligence Technology, 9(4), 879–893, Available at: https://doi.org/10.1049/cit2.12340
13. C. Wang, Z. Xiao, Y. Xu, et al. (2024), A novel approach for ASD recognition based on graph attention networks, Frontiers in Computational
Neuroscience, 18, Available at: https://doi.org/10.3389/fncom.2024.1388083
14. S. Liu, B. Liang, S. Wang, et al. (2023), NF-GAT: a node Feature-Based Graph attention Network for ASD classification, IEEE Open
Journal of Engineering in Medicine and Biology, 5, 428–433, https://doi.org/10.1109/ojemb.2023.3267612
15. Rapin, I., & Tuchman, R. F. (2008). Autism: Definition, Neurobiology, screening, diagnosis, Pediatric Clinics of North America, 55(5),
1129–1146, Available at: https://doi.org/10.1016/j.pcl.2008.07.005
16. Cherkassky, V. L., Kana, R. K., Keller, T. A., et al. (2006). Functional connectivity in a baseline resting-state network in autism, Neuroreport,
17(16), 1687–1690, Available at: https://doi.org/10.1097/01.wnr.0000239956.45448.4c