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Theoretically, the statuses in the Alzheimer\u2019s disease continuum are expected to share common features. Here, we explore to verify and refine candidature early statuses of Alzheimer\u2019s disease with features learned from deep learning. We train models on brain functional networks to accurately classify between amnestic and non-amnestic mild cognitive impairments and between healthy controls and mild cognitive impairments. The trained models are applied to Alzheimer\u2019s disease and subjective cognitive decline groups to suggest feature similarities among the statuses and identify informative subpopulations. The amnestic mild cognitive impairment vs non-amnestic mild cognitive impairments classifier believes that 71.8% of Alzheimer\u2019s disease are amnestic mild cognitive impairment. And 73.5% of subjective cognitive declines are labeled as mild cognitive impairments, 88.8% of which are further suggested as \u201camnestic mild cognitive impairment.\u201d Further multimodal analyses suggest that the amnestic mild cognitive impairment-like Alzheimer\u2019s disease, mild cognitive impairment-like subjective cognitive decline, and amnestic mild cognitive impairment-like subjective cognitive decline exhibit more Alzheimer\u2019s disease -related pathological changes (elaborated \u03b2-amyloid depositions, reduced glucose metabolism, and gray matter atrophy) than non-amnestic mild cognitive impairments -like Alzheimer\u2019s disease, healthy control-like subjective cognitive decline, and non-amnestic mild cognitive impairments -like subjective cognitive decline. The test\u2013retest reliability of the subpopulation identification is fair to good in general. The study indicates overall similarity among subjective cognitive decline, amnestic mild cognitive impairment, and Alzheimer\u2019s disease and implies their progression relationships. The results support \u201cdeep feature comparison\u201d as a potential beneficial framework to verify and refine early Alzheimer\u2019s disease status.<\/jats:p>","DOI":"10.1093\/cercor\/bhad381","type":"journal-article","created":{"date-parts":[[2023,10,14]],"date-time":"2023-10-14T03:19:52Z","timestamp":1697253592000},"page":"11486-11500","source":"Crossref","is-referenced-by-count":5,"title":["Verifying and refining early statuses in Alzheimer\u2019s disease progression: a possibility from deep feature comparison"],"prefix":"10.1093","volume":"33","author":[{"given":"Mianxin","family":"Liu","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering , State Key Laboratory of Advanced Medical Materials and Devices, , Shanghai 201210 , China"},{"name":"Shanghai Tech University , State Key Laboratory of Advanced Medical Materials and Devices, , Shanghai 201210 , China"},{"name":"Shanghai Artificial Intelligence Laboratory , Shanghai 200232 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People\u2019s Hospital , Shanghai 200233 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zixiao","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine , Center for Molecular Imaging and Translational Medicine, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, , Xiamen, Fujian 361102 , China"},{"name":"School of Public Health, Xiamen University , Center for Molecular Imaging and Translational Medicine, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, , Xiamen, Fujian 361102 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuhua","family":"Ren","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University , Shanghai 200040 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People\u2019s Hospital , Shanghai 200233 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihui","family":"Guan","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University , Shanghai 200040 , China"},{"name":"National Center for Neurological Disorders , Shanghai 201112 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qihao","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People\u2019s Hospital , Shanghai 200233 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University , Shanghai 200040 , China"},{"name":"National Center for Neurological Disorders , Shanghai 201112 , China"},{"name":"State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University , Shanghai , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University , Shanghai 200040 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering , State Key Laboratory of Advanced Medical Materials and Devices, , Shanghai 201210 , China"},{"name":"Shanghai Tech University , State Key Laboratory of Advanced Medical Materials and Devices, , Shanghai 201210 , China"},{"name":"Shanghai United Imaging Intelligence Co., Ltd. , Shanghai 200230 , China"},{"name":"Shanghai Clinical Research and Trial Center , Shanghai, 201210 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,10,13]]},"reference":[{"key":"2023121612173920700_ref1","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1126\/science.abm2461","article-title":"Contrastive machine learning reveals the structure of neuroanatomical variation within autism","volume":"376","author":"Aglinskas","year":"2022","journal-title":"Science"},{"key":"2023121612173920700_ref2","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.neuroimage.2007.07.007","article-title":"A fast diffeomorphic image registration 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