{"ID":2857122,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.13841","arxiv_id":"2510.13841","title":"Identifying Autism-Related Neurobiomarkers Using Hybrid Deep Learning Models","abstract":"Autism spectrum disorder (ASD) has been associated with structural alterations across cortical and subcortical regions. Quantitative neuroimaging enables large-scale analysis of these neuroanatomical patterns. This project used structural MRI (T1-weighted) data from the publicly available ABIDE I dataset (n = 1,112) to classify ASD and control participants using a hybrid model. A 3D convolutional neural network (CNN) was trained to learn neuroanatomical feature representations, which were then passed to a support vector machine (SVM) for final classification. Gradient-weighted class activation mapping (Grad-CAM) was applied to the CNN to visualize the brain regions that contributed most to the model predictions. The Grad-CAM difference maps showed strongest relevance along cortical boundary regions, with additional emphasis in midline frontal-temporal-parietal areas, which is broadly consistent with prior ASD neuroimaging findings.","short_abstract":"Autism spectrum disorder (ASD) has been associated with structural alterations across cortical and subcortical regions. Quantitative neuroimaging enables large-scale analysis of these neuroanatomical patterns. This project used structural MRI (T1-weighted) data from the publicly available ABIDE I dataset (n = 1,112) to...","url_abs":"https://arxiv.org/abs/2510.13841","url_pdf":"https://arxiv.org/pdf/2510.13841v2","authors":"[\"Ashley Chen\"]","published":"2025-10-11T13:43:46Z","proceeding":"q-bio.NC","tasks":"[\"q-bio.NC\",\"cs.LG\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}