{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:30:47Z","timestamp":1786581047039,"version":"build-2736575974"},"posted":{"date-parts":[[2026,8,10]]},"group-title":"Neurology","reference-count":80,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T00:00:00Z","timestamp":1786320000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009053","name":"Wellcome Trust\/DBT India Alliance","doi-asserted-by":"crossref","award":["IA\/E\/22\/1\/506763"],"award-info":[{"award-number":["IA\/E\/22\/1\/506763"]}],"id":[{"id":"10.13039\/501100009053","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001412","name":"Council of Scientific and Industrial Research","doi-asserted-by":"crossref","award":["25WS(013)\/2023-24\/EMR-II\/ASPIRE"],"award-info":[{"award-number":["25WS(013)\/2023-24\/EMR-II\/ASPIRE"]}],"id":[{"id":"10.13039\/501100001412","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001843","name":"Science and Engineering Research Board","doi-asserted-by":"crossref","award":["SRG\/2023\/001406"],"award-info":[{"award-number":["SRG\/2023\/001406"]}],"id":[{"id":"10.13039\/501100001843","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Pratiksha Trust, Bangalore","award":["FG\/PTCH-23-1004"],"award-info":[{"award-number":["FG\/PTCH-23-1004"]}]},{"award":["IE\/RERE-22-0583"],"award-info":[{"award-number":["IE\/RERE-22-0583"]}],"id":[{"id":"https:\/\/ror.org\/04dese585","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,8,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>\n                  Accurate long-term forecasting of cognitive trajectories across the Alzheimer\u2019s disease continuum is essential for early intervention, personalized prognosis, patient stratification, and clinical trial enrichment. Despite the promising predictive performance of recent longitudinal forecasting methods, they remain largely data-driven, struggle with irregularly sampled, incomplete longitudinal data and often neglect established disease biology, leading to biologically implausible trajectories. To address this, we propose a biologically constrained continuous-time framework for long-horizon cognition forecasting from limited baseline observations. The proposed method models the complete amy-loid\u2013tau\u2013vascular\u2013neurodegeneration\u2013cognition (ATVNC) cascade using hierarchical Neural ODEs with biologically motivated monotonicity constraints. Each pathological stream is governed by a dedicated Neural ODE initialized from irregular longitudinal observations using a GRU-D encoder, capturing intrinsic disease evolution while being modulated by directed upstream pathological influences. A bounded cognition readout ensures physiologically valid cognitive score (MoCA) predictions, while teacher\u2013student knowledge distillation improves learning from sparse lon-gitudinal supervision. Evaluated on the ADNI dataset, the proposed framework achieves a long-horizon extrapolation MAE of 2.06 on 188 held-out participants while eliminating biologically implausible trajectory violations. It further demonstrates robust zero-shot cross-cohort generalization on OASIS-3 (MAE 2.68 on 300 participants), with fine-tuning improving MAE to 1.90. The model also supports prognostic enrichment for Alzheimer\u2019s clinical trials, achieving up to 2.70 \u00d7 enrichment over the cohort base rate. These results demonstrate that embedding biological disease mechanisms within continuous-time deep learning improves the accuracy, biological plausibility, and clinical utility of long-horizon cognitive forecasting. The code is publicly available at:\n                  <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/PonDeepika\/BEACON\">https:\/\/github.com\/PonDeepika\/BEACON<\/jats:ext-link>\n                  .\n                <\/jats:p>","DOI":"10.64898\/2026.08.07.26359964","type":"posted-content","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T15:55:12Z","timestamp":1786377312000},"source":"Crossref","is-referenced-by-count":0,"title":["A Biologically Constrained Continuous-Time Framework for Long-Horizon Cognition Forecasting in Alzheimer\u2019s Disease"],"prefix":"10.64898","author":[{"given":"Pon","family":"Deepika","sequence":"first","affiliation":[{"name":"Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, 560012, Karnataka, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suresh","family":"Sunkari","sequence":"additional","affiliation":[{"name":"Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, 560012, Karnataka, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sai Koundinya","family":"Upadhyayula","sequence":"additional","affiliation":[{"name":"Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, 560012, Karnataka, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9451-4779","authenticated-orcid":false,"given":"Vaanathi","family":"Sundaresan","sequence":"additional","affiliation":[{"name":"Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, 560012, Karnataka, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026081217201556000_2026.08.07.26359964v1.1","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1186\/s13195-025-01827-2","article-title":"Longitudinal structural mri-based deep learning and radiomics features for predicting alzheimer\u2019s disease progression","volume":"17","year":"2025","journal-title":"Alzheimer\u2019s Research & Therapy"},{"key":"2026081217201556000_2026.08.07.26359964v1.2","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s13195-017-0283-5","article-title":"On the path to 2025: understanding the alzheimer\u2019s disease continuum","volume":"9","year":"2017","journal-title":"Alzheimer\u2019s research & therapy"},{"key":"2026081217201556000_2026.08.07.26359964v1.3","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btae264"},{"key":"2026081217201556000_2026.08.07.26359964v1.4","doi-asserted-by":"crossref","first-page":"103383","DOI":"10.1016\/j.nicl.2023.103383","article-title":"The association between white matter hyperintensities and amyloid and tau deposition","volume":"38","year":"2023","journal-title":"NeuroImage: Clinical"},{"key":"2026081217201556000_2026.08.07.26359964v1.5","doi-asserted-by":"crossref","first-page":"101645","DOI":"10.1016\/j.nicl.2018.101645","article-title":"Automated classification of alzheimer\u2019s disease and mild cognitive impairment using a single mri and deep neural networks","volume":"21","year":"2019","journal-title":"NeuroImage: Clinical"},{"key":"2026081217201556000_2026.08.07.26359964v1.6","doi-asserted-by":"crossref","unstructured":"Baytas, I.M. , Xiao, C. , Zhang, X. , Wang, F. , Jain, A.K. , et al., 2017. 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