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A Biologically Constrained Continuous-Time Framework for Long-Horizon Cognition Forecasting in Alzheimer’s Disease
Deepika P, Sunkari S, Upadhyayula SK, the Alzheimer’s Disease Neuroimaging Initiative, Sundaresan V.. medRxiv, 2026-08-10
Topic: Brain imaging, connectivity and assessment
Accurate long-term forecasting of cognitive trajectories across the Alzheimer’s 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–tau–vascular–neurodegeneration–cognition (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–student 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’s clinical trials, achieving up to 2.70 × 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: https://github.com/PonDeepika/BEACON .
Key findings
- 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.
Study details
- Study designlongitudinal study
- Participants300
- Evidence strengthModerate
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 47 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for effect reported and randomised / blinded because the record does not state it. We have no data on times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 29 of the 62 points we could judge it on, which is 47 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (medRxiv) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 7 / 12 | A longitudinal study following the same people over time |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 2 / 4 | 300 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Points | 29 / 62 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 29 of the 62 points we could judge it on is 47 out of 100. |
| Total | 47 / 100 | Capped to 16 because this is not a peer-reviewed article. |
What each of these 20 components measures, and what this score is not →
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APA 7th
Deepika, P., Sunkari, S., Upadhyayula, S. K., the Alzheimer’s Disease Neuroimaging Initiative, & Sundaresan, V. (2026). A Biologically Constrained Continuous-Time Framework for Long-Horizon Cognition Forecasting in Alzheimer’s Disease. medRxiv. https://doi.org/10.64898/2026.08.07.26359964
Vancouver
Deepika P, Sunkari S, Upadhyayula SK, the Alzheimer’s Disease Neuroimaging Initiative, Sundaresan V. A Biologically Constrained Continuous-Time Framework for Long-Horizon Cognition Forecasting in Alzheimer’s Disease. medRxiv. 2026. doi:10.64898/2026.08.07.26359964.
BibTeX
@article{deepika2026-ae3097e3,
author = {Deepika, P. and Sunkari, S. and Upadhyayula, S. K. and {the Alzheimer’s Disease Neuroimaging Initiative} and Sundaresan, V.},
title = {{A Biologically Constrained Continuous-Time Framework for Long-Horizon Cognition Forecasting in Alzheimer’s Disease}},
journal = {medRxiv},
year = {2026},
doi = {10.64898/2026.08.07.26359964},
url = {https://doi.org/10.64898/2026.08.07.26359964}
}