{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T11:25:09Z","timestamp":1782818709149,"version":"3.54.5"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"High-Performance Computing Center of Central South University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Alzheimer\u2019s disease is the most common major neurocognitive disorder. Although currently, no cure exists, understanding the neurobiological substrate underlying Alzheimer\u2019s disease progression will facilitate early diagnosis and treatment, slow disease progression, and improve prognosis. In this study, we aimed to understand the morphological changes underlying Alzheimer\u2019s disease progression using structural magnetic resonance imaging data from cognitively normal individuals, individuals with mild cognitive impairment, and Alzheimer\u2019s disease via a contrastive variational autoencoder model. We used contrastive variational autoencoder to generate synthetic data to boost the downstream classification performance. Due to the ability to parse out the nonclinical factors such as age and gender, contrastive variational autoencoder facilitated a purer comparison between different Alzheimer\u2019s disease stages to identify the pathological changes specific to Alzheimer\u2019s disease progression. We showed that brain morphological changes across Alzheimer\u2019s disease stages were significantly associated with individuals\u2019 neurofilament light chain concentration, a potential biomarker for Alzheimer\u2019s disease, highlighting the biological plausibility of our results.<\/jats:p>","DOI":"10.1093\/cercor\/bhae393","type":"journal-article","created":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T06:05:06Z","timestamp":1728021906000},"source":"Crossref","is-referenced-by-count":5,"title":["Uncovering neural substrates across Alzheimer\u2019s disease stages using contrastive variational autoencoder"],"prefix":"10.1093","volume":"34","author":[{"given":"Yan","family":"Tang","sequence":"first","affiliation":[{"name":"School of Electronic Information, Central South University , Changsha, 410148,","place":["China"]},{"name":"School of Computer Science and Engineering , Central South University, Changsha, 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