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Algorithmically, we first introduce a novel cumulative distribution divergence score (CDDS) feature selection method based on the multi-atlas functional connectivity (FC), which quantifies statistical distributional differences between positive and negative samples, effectively addressing the limitations of the traditional\n                    <jats:italic>F<\/jats:italic>\n                    -score in overlapping or weakly discriminative features. Building upon this, we adapt existing deep learning (DL) paradigms to construct a deep neural architecture (variational autoencoder-denoising autoencoder (VAEA)), combining a variational autoencoder (VAE), a denoising autoencoder, and a multilayer perceptron. The combination of these modules is non-trivial: the novel CDDS first purifies the high-dimensional feature space, enabling the VAEA to learn robust latent probabilistic representations via unsupervised pre-training without suffering from the curse of dimensionality. This is followed by the supervised fine-tuning to optimize parameters. We further incorporate demographic factors including age, full-scale intelligence quotient, and gender via a late-fusion strategy to prevent them from being overshadowed by imaging features, thereby enhancing the discriminability. FC features from three atlases including AAL, CC400, and Dosenbach160 are weighted and integrated to achieve the effective multi-source fusion.\n                    <jats:italic>Main results.<\/jats:italic>\n                    We conduct 10-fold cross-validations on the Autism Brain Imaging Data Exchange (ABIDE) dataset, and the proposed APN achieves 81.63% accuracy, 81.89% sensitivity and 81.46% specificity, significantly outperforming state-of-the-art methods. Interpretability analysis identifies hippocampus, amygdala, and cingulate gyrus as strongly ASD-associated regions, offering neural mechanistic insights.\n                    <jats:italic>Significance.<\/jats:italic>\n                    This study provides an efficient, objective, and interpretable multimodal DL framework for ASD assisted diagnosis, with substantial clinical application and neuroscience value.\n                  <\/jats:p>","DOI":"10.1088\/2057-1976\/aeb128","type":"journal-article","created":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T22:47:50Z","timestamp":1791413270000},"page":"055055","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A multi-atlas ensemble learning framework for autism spectrum disorder diagnosis"],"prefix":"10.1088","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9804-6928","authenticated-orcid":true,"given":"Shengbin","family":"Liang","sequence":"first","affiliation":[{"name":"Henan University"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8655-402X","authenticated-orcid":false,"given":"Zhe","family":"Zhang","sequence":"additional","affiliation":[{"name":"Henan University"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0428-0057","authenticated-orcid":true,"given":"Wencai","family":"Du","sequence":"additional","affiliation":[{"name":"University of Saint Joseph"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5337-4282","authenticated-orcid":true,"given":"Feng","family":"Gu","sequence":"additional","affiliation":[{"name":"College of Staten Island, The City University of New York"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]}],"member":"266","published-online":{"date-parts":[[2026,10,16]]},"reference":[{"key":"bpexaeb128bib1","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1109\/TNSRE.2023.3344995","type":"journal-article","article-title":"Deep causality variational autoencoder network for identifying the potential biomarkers of brain disorders","volume":"32","author":"Alfakih","year":"2024","journal-title":"IEEE Trans. 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