{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T10:05:29Z","timestamp":1789553129437,"version":"build-2803163510"},"reference-count":21,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brain Sci.","Brain Sciences"],"abstract":"<jats:p>Background: This study aimed to quantitatively assess structural changes in the hippocampus, amygdala, entorhinal cortex, lateral ventricles, precuneus, and posterior cingulate using deep-learning-based FastSurfer morphometry and evaluate their contribution to classification across the Alzheimer\u2019s disease (AD) spectrum. Methods: Three-dimensional T1-weighted MRI data from the AD Neuroimaging Initiative (ADNI) were analyzed using FastSurfer. The study included 791 participants (425 female and 366 male): 365 cognitively normal (CN), 273 with mild cognitive impairment (MCI), and 153 with AD. Morphometric measures included hippocampal and amygdala volumes, lateral ventricular volume, entorhinal cortical thickness and surface area, and precuneus and posterior cingulate thickness and folding index. Volumetric measures were normalized to estimated total intracranial volume (eTIV). Principal morphometric comparisons were additionally adjusted for age, sex, and education. Six machine-learning classifiers were evaluated using participant-level training (n = 632) and held-out test (n = 159) sets, with an additional MMSE-ablation analysis. Results: Hippocampal and amygdala volumes were significantly lower and lateral ventricular volume was significantly higher in AD than in CN (all p &lt; 0.001), with these differences persisting after eTIV normalization and remaining significant after adjustment for age, sex, and education (all adjusted p &lt; 0.001). MCI generally showed intermediate volumetric values between CN and AD. Mini-Mental State Examination (MMSE) scores correlated positively with total hippocampal (r = 0.495) and amygdala (r = 0.462) volumes and negatively with lateral ventricular volume (r = \u22120.256) (all p &lt; 0.001). For three-class CN\/MCI\/AD classification, Logistic Regression with ElasticNet achieved a mean macro-F1 score of 0.706 \u00b1 0.017 in five-fold cross-validation and a held-out macro-F1 score of 0.705 with MMSE included. Excluding MMSE reduced the held-out macro-F1 score to 0.531 and ROC-AUC to 0.735, compared with 0.857 when MMSE was included. Performance was higher for binary CN-versus-AD classification, with a held-out test macro-F1 score of 0.919 and ROC-AUC of 0.990. Conclusions: FastSurfer-based morphometry demonstrated medial temporal atrophy and lateral ventricular enlargement across the CN\u2013MCI\u2013AD spectrum, with the principal volumetric differences remaining robust after adjustment for age, sex, and education. The intermediate morphometric profile of MCI and the associations between medial temporal volumes and cognitive performance support the relevance of these structural measures. Machine-learning performance improved substantially when MMSE was incorporated, indicating that the combined models reflect integrated morphometric, demographic, genetic, and cognitive information rather than morphometry alone. External validation in independent cohorts is required before clinical application.<\/jats:p>","DOI":"10.3390\/brainsci16090980","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T09:06:37Z","timestamp":1789549597000},"page":"980","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FastSurfer-Based Brain Morphometry and Machine-Learning Classification Across the Alzheimer\u2019s Disease Spectrum"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-7560-3447","authenticated-orcid":false,"given":"Sinem Nur","family":"Altun","sequence":"first","affiliation":[{"name":"Department of Anatomy, Faculty of Medicine, Istanbul Atlas University, 34403 Istanbul, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7083-966X","authenticated-orcid":false,"given":"Hurriyet","family":"Cetinok","sequence":"additional","affiliation":[{"name":"Department of Anatomy, Faculty of Medicine, Istanbul Atlas University, 34403 Istanbul, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,16]]},"reference":[{"key":"ref_1","unstructured":"Lui, F., and Tsao, J.W. (2024). Alzheimer Disease. StatPearls, StatPearls Publishing."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-319-12679-1_1","article-title":"Neuroanatomy and pathology of sporadic Alzheimer\u2019s disease","volume":"215","author":"Braak","year":"2015","journal-title":"Adv. Anat. Embryol. Cell Biol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1038\/nrneurol.2009.215","article-title":"The clinical use of structural MRI in Alzheimer disease","volume":"6","author":"Frisoni","year":"2010","journal-title":"Nat. Rev. Neurol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.jalz.2018.02.018","article-title":"NIA-AA Research Framework: Toward a biological definition of Alzheimer\u2019s disease","volume":"14","author":"Jack","year":"2018","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"117012","DOI":"10.1016\/j.neuroimage.2020.117012","article-title":"FastSurfer-A fast and accurate deep learning based neuroimaging pipeline","volume":"219","author":"Henschel","year":"2020","journal-title":"Neuroimage"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"17106","DOI":"10.1038\/s41598-022-20674-x","article-title":"Generalizable deep learning model for early Alzheimer\u2019s disease detection from structural MRIs","volume":"12","author":"Liu","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_7","first-page":"470","article-title":"Differential diagnosis of mild cognitive impairment and Alzheimer\u2019s disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry","volume":"13","author":"Igel","year":"2016","journal-title":"Neuroimage Clin."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.jalz.2010.03.007","article-title":"The Alzheimer\u2019s disease neuroimaging initiative: Progress report and future plans","volume":"6","author":"Weiner","year":"2010","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.jalz.2010.03.004","article-title":"Update on the magnetic resonance imaging core of the Alzheimer\u2019s disease neuroimaging initiative","volume":"6","author":"Jack","year":"2010","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_10","unstructured":"(2026, August 15). Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI). ADNI Data. Available online: https:\/\/adni.loni.usc.edu."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.pscychresns.2011.06.014","article-title":"Amygdala atrophy is prominent in early Alzheimer\u2019s disease and relates to symptom severity","volume":"194","author":"Poulin","year":"2011","journal-title":"Psychiatry Res. Neuroimaging"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"275","DOI":"10.3233\/JAD-200200","article-title":"Brain Structure in Bilingual Compared to Monolingual Individuals with Alzheimer\u2019s Disease: Proof of Concept","volume":"76","author":"Raji","year":"2020","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"bio056796","DOI":"10.1242\/bio.056796","article-title":"Molecular mechanisms of neurodegeneration in the entorhinal cortex that underlie its selective vulnerability during the pathogenesis of Alzheimer\u2019s disease","volume":"10","author":"Olajide","year":"2021","journal-title":"Biol. Open"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1186\/s13195-025-01768-w","article-title":"Hippocampal subfields: Volume, neuropathological vulnerability and cognitive decline in Alzheimer\u2019s and Parkinson\u2019s disease","volume":"17","author":"Bouwman","year":"2025","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1177\/13872877251400747","article-title":"Surface-based but not voxel-based morphometry reveals structural abnormalities in individuals with subjective cognitive decline","volume":"109","author":"Serra","year":"2026","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"100463","DOI":"10.1016\/j.tjpad.2025.100463","article-title":"Longitudinal changes in subcortical functional connectivity during Alzheimer\u2019s disease progression","volume":"13","author":"Kim","year":"2026","journal-title":"J. Prev. Alzheimer\u2019s Dis."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1177\/13872877261450574","article-title":"Comparison of hippocampus, entorhinal cortex, and amygdala volumes in individuals with Alzheimer\u2019s disease, mild cognitive impairment, and healthy individuals","volume":"112","author":"Senol","year":"2026","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1017\/S1041610206004303","article-title":"Hippocampal atrophy correlates with the severity of cognitive decline","volume":"19","author":"Yavuz","year":"2007","journal-title":"Int. Psychogeriatr."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1111\/cns.12317","article-title":"Correlation of hippocampal volume and cognitive performances in patients with either mild cognitive impairment or Alzheimer\u2019s disease","volume":"21","author":"Peng","year":"2015","journal-title":"CNS Neurosci. Ther."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5151","DOI":"10.1002\/alz.13112","article-title":"Improved multimodal prediction of progression from MCI to Alzheimer\u2019s disease combining genetics with quantitative brain MRI and cognitive measures","volume":"19","author":"Reas","year":"2023","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"B\u00f6hle, M., Eitel, F., Weygandt, M., and Ritter, K. (2019). Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer\u2019s Disease Classification. Front. Aging Neurosci., 11.","DOI":"10.3389\/fnagi.2019.00194"}],"container-title":["Brain Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3425\/16\/9\/980\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T09:30:59Z","timestamp":1789551059000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3425\/16\/9\/980"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,16]]},"references-count":21,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["brainsci16090980"],"URL":"https:\/\/doi.org\/10.3390\/brainsci16090980","relation":{},"ISSN":["2076-3425"],"issn-type":[{"value":"2076-3425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,16]]}}}