External Validation of a Mathematical Model of Brain Health

Abstract

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.

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Cite

BibTeX RIS

BibTeX

@misc{sadia2026external,
  title = {{External Validation of a Mathematical Model of Brain Health}},
  author = {H. Sadia and N. Doyon and S. Duchesne},
  howpublished = {medRxiv},
  year = {2026},
  month = sep,
  doi = {10.64898/2026.09.01.26361929},
  url = {https://www.medrxiv.org/content/10.64898/2026.09.01.26361929v1},
  note = {CC BY-NC-ND},
}

RIS

TY  - UNPB
TI  - External Validation of a Mathematical Model of Brain Health
AU  - H. Sadia
AU  - N. Doyon
AU  - S. Duchesne
PY  - 2026
DA  - 2026/09/01
PB  - medRxiv
DO  - 10.64898/2026.09.01.26361929
UR  - https://www.medrxiv.org/content/10.64898/2026.09.01.26361929v1
AB  - 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.
ER  -

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