External Validation of a Mathematical Model of Brain Health
Sadia, H.; Doyon, N.; Duchesne, S.. medRxiv (preprint), 2026-09-03
Topic: Brain proteins, microglia and lab models
BackgroundUnderstanding the mechanisms underlying brain aging and age-related pathological changes is essential for advancing brain health research. Our group previously developed a mechanistic mathematical model of healthy brain Chamberland et al. (2024) that integrates key biological processes involved in normal aging, from which Alzheimers disease (AD)-related changes may emerge naturally. ObjectivesTo characterize and validate this brain model by evaluating its sensitivity, calibrating its parameters, and assessing generalizability in independent populations. MethodsThe model represents the evolution of key biological processes associated with brain aging, including amyloid beta (A{beta}), tau pathologies, neuroinflammation, and neuronal death. After identifying the 30 most influential parameters, we calibrated the model using cognitively normal (CN) participants from the AD Neuroimaging Initiative (ADNI) database (n = 211) by minimizing a loss function composed of three outcomes (A{beta} plaques, tau tangles, and neuronal density). The calibrated model was then applied to the UK Biobank cohort (n = 35, 899) of normal controls (aged 44-82 years). The effects of sex and APOE were evaluated using stratified simulations. ResultsParameter calibration significantly reduced the prediction errors for A{beta} and tau. Neuronal density predictions showed strong agreement in the UK Biobank cohort. The variance decomposition identified APOE status as a major contributor to variability in A{beta}. ConclusionOur validated brain health model links mechanistic pathways with population data and reproduces neuronal density patterns in an independent cohort. These findings support its use as a framework for studying brain aging and investigating how Alzheimers disease-related pathological changes may emerge with aging.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Sadia, H., Doyon, N., & Duchesne, S. (2026). External Validation of a Mathematical Model of Brain Health. medRxiv (preprint). https://doi.org/10.64898/2026.09.01.26361929
Vancouver
Sadia H, Doyon N, Duchesne S. External Validation of a Mathematical Model of Brain Health. medRxiv (preprint). 2026. doi:10.64898/2026.09.01.26361929.
BibTeX
@article{sadia2026-3b48aef7,
author = {Sadia, H. and Doyon, N. and Duchesne, S.},
title = {{External Validation of a Mathematical Model of Brain Health}},
journal = {medRxiv (preprint)},
year = {2026},
doi = {10.64898/2026.09.01.26361929},
url = {https://doi.org/10.64898/2026.09.01.26361929}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
Key findings
- MethodsThe model represents the evolution of key biological processes associated with brain aging, including amyloid beta (A{beta}), tau pathologies, neuroinflammation, and neuronal death.
- ResultsParameter calibration significantly reduced the prediction errors for A{beta} and tau.
- These findings support its use as a framework for studying brain aging and investigating how Alzheimers disease-related pathological changes may emerge with aging.
Why it matters
This study calibrated and externally validated a mathematical model of brain aging that includes amyloid beta, tau, neuroinflammation and neuronal death, and found it predicted these features with reduced error. It is a modeling framework for studying brain aging and does not test any treatment, cure or prevention.
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Study details
- Participants211
- Evidence strengthModerate
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 27 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design, effect reported and randomised / blinded because the record does not state it. We have no data on impact for its field and year and senior author's track record, so those were left out: it earned 24 of the 90 points we could judge it on, which is 27 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (medRxiv (preprint)) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| 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 | 211 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 | 0 / 7 | Cited 0 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.0 citations a year over 2 year(s) since 2026 |
| 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 | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 0 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | OpenAlex's profile for last-listed author Alzheimer’s Disease Neuroimaging Initiative holds only 1 work(s), too few to judge a track record — left out of this score |
| Institution's track record | 1 / 3 | Université Laval: institution h-index 823 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Points | 24 / 90 | Scored on 18 of 20 parts. We have no data for the rest, so they are left out: 24 of the 90 points we could judge it on is 27 out of 100. |
| Total | 27 / 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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