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This study aimed to examine the relationship between gray matter atrophy and neurotransmitter distributions and to build machine-learning models using gray matter\u2013neurotransmitter co-localization as features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Among 262 participants from the Alzheimer\u2019s Disease Neuroimaging Initiative (140 with Alzheimer\u2019s disease, 50 with mild cognitive impairment, and 72 controls), we used structural magnetic resonance imaging and voxel-based morphometry (family-wise error\u202f&lt;\u202f0.05), and JuSpace toolbox was used to assess the spatial correlation between gray matter atrophy and 13 neurotransmitter maps. We applied a train\/validation\/fixed test split (the test set was never used for selection or training); features were screened by univariate regression and least absolute shrinkage and selection operator regression, and models trained with nested 10-fold cross-validation were evaluated by the area under the receiver operating characteristic curve.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Both Alzheimer\u2019s disease and mild cognitive impairment showed gray matter loss in temporal, frontal, and cingulate areas. Atrophy was correlated with serotonergic, dopaminergic, and glutamatergic systems (false-discovery rate\u202f&lt;\u202f0.05). In mild cognitive impairment, reduced metabotropic glutamate receptor 5\/\u03bc-opioid receptor\u2013gray matter correlation was associated with higher depression scores (r\u2009=\u2009\u22120.44, p\u2009=\u20090.001; r\u2009=\u2009\u22120.44, p\u2009=\u20090.001). The Random Forest model achieved an area under the receiver operating characteristic curve of 0.821, and Shapley additive explanations analysis confirmed key feature contributions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Neurotransmitter-linked gray matter changes contribute to the pathology of Alzheimer\u2019s disease and mild cognitive impairment. The machine-learning model accurately differentiates these conditions, suggesting its utility for early diagnosis and disease staging.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/03000605251409886","type":"journal-article","created":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T01:53:05Z","timestamp":1768355585000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Neurotransmitter-based machine-learning model for distinguishing Alzheimer\u2019s disease and mild cognitive impairment"],"prefix":"10.1177","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7549-0816","authenticated-orcid":false,"given":"Jiaxi","family":"Zhao","sequence":"first","affiliation":[{"name":"The Affiliated Hospital of Southwest Medical University"},{"name":"The Department of Neurosurgery, The General Hospital of Western Theater Command PLA, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhichuang","family":"Qu","sequence":"additional","affiliation":[{"name":"The Department of Neurosurgery, Meishan People\u2019s Hospital, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Li","sequence":"additional","affiliation":[{"name":"The Affiliated Hospital of Southwest Medical University"},{"name":"The Department of Neurosurgery, The General Hospital of Western Theater Command PLA, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lanling","family":"Zhou","sequence":"additional","affiliation":[{"name":"The Department of Neurosurgery, The General Hospital of Western Theater Command PLA, China"},{"name":"Southwest Jiaotong University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Hu","sequence":"additional","affiliation":[{"name":"The Department of Neurosurgery, The General Hospital of Western Theater Command PLA, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sixun","family":"Yu","sequence":"additional","affiliation":[{"name":"The Affiliated Hospital of Southwest Medical University"},{"name":"The Department of Neurosurgery, The General Hospital of Western Theater Command PLA, China"},{"name":"Southwest Jiaotong University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Chen","sequence":"additional","affiliation":[{"name":"The Department of Neurosurgery, The General Hospital of Western Theater Command PLA, China"},{"name":"Southwest Jiaotong University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haifeng","family":"Shu","sequence":"additional","affiliation":[{"name":"The Affiliated Hospital of Southwest Medical University"},{"name":"The Department of Neurosurgery, The General Hospital of Western Theater Command PLA, China"},{"name":"Southwest Jiaotong University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"For The Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,1,13]]},"reference":[{"key":"e_1_3_13_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrneurol.2011.2"},{"key":"e_1_3_13_3_2","first-page":"289","article-title":"The molecular biology of senile plaques and neurofibrillary tangles in Alzheimer\u2019s disease","volume":"47","author":"Armstrong RA.","year":"2009","unstructured":"Armstrong RA. 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