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Despite significant clinical research, understanding how microscopic protein dynamics translate into macroscopic tissue degeneration remains a major challenge. In this work, we present a three-dimensional, finite element-based computational framework to model disease progression by combining multi-protein transport and brain tissue deformation within anatomically realistic geometries. The propagation of toxic tau and amyloid-\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\beta $$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    proteins is described using reaction\u2013diffusion equations of the Fisher-Kolmogorov type, incorporating prion-like growth mechanisms and anisotropic transport along white matter fibre tracts. Brain atrophy is represented through a hyperelastic constitutive model driven by protein-dependent volume loss. Subject-specific simulations are achieved through an automated preprocessing pipeline that generates finite element meshes and reconstructs axonal orientation fields from medical imaging data. The model reproduces key morphological patterns observed in Alzheimer\u2019s disease and shows good quantitative agreement with longitudinal imaging measurements. Overall, the proposed framework offers an extensible computational platform for studying Alzheimer\u2019s disease progression across subject-specific brain geometries. The models developed, including the image processing framework () and the coupled bio-chemo-mechanical  finite element implementation, are made freely available to download at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/mechmat.web.ox.ac.uk\/codes\" ext-link-type=\"uri\">https:\/\/mechmat.web.ox.ac.uk\/codes<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1007\/s00366-026-02313-5","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T02:08:15Z","timestamp":1775095695000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A computational framework to predict the spreading of Alzheimer\u2019s disease"],"prefix":"10.1007","volume":"42","author":[{"given":"Ana","family":"Vazquez-Palomo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Covadonga","family":"Beteg\u00f3n","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes","family":"Weickenmeier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emilio","family":"Mart\u00ednez-Pa\u00f1eda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,2]]},"reference":[{"issue":"10284","key":"2313_CR1","doi-asserted-by":"crossref","first-page":"1577","DOI":"10.1016\/S0140-6736(20)32205-4","volume":"397","author":"P Scheltens","year":"2021","unstructured":"Scheltens P, De Strooper B, Kivipelto M, Holstege H, Ch\u00e9telat G, Teunissen CE, Cummings J, van der Flier WM (2021) Alzheimer\u2019s disease. 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