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Parissa Fereydouni-Forouzandeh1,2, Nicolas Doyon1,2,3, Simon Duchesne1,2,3

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Summary
This summary is machine-generated.

Mathematical models of human brain metabolism show promise but require adjustments for external validity. Current models capture short-term processes but need enhancement to assess lifetime brain metabolism trajectories.

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Systems Biology

Background:

  • Alzheimer's disease is characterized by downregulated brain metabolism despite increased energy demands.
  • Understanding this metabolic dysfunction requires a multifactorial causal framework.
  • Mathematical modeling offers a promising approach to investigate these complex relationships.

Purpose of the Study:

  • To conduct a scoping review of existing mathematical models of human cerebral metabolism.
  • To identify suitable models for future implementation in Alzheimer's disease research.
  • To guide the development of computational frameworks for studying brain metabolism.

Main Methods:

  • Systematic scoping review following PRISMA 2020 guidelines.
  • PubMed database search using keywords: "mathematical", "model", "brain", "glucose".
  • Screening of 299 studies, with 14 selected for qualitative analysis based on inclusion criteria.

Main Results:

  • Selected models demonstrated adequate internal validity (equations, parameters provided) but neglected external validity.
  • Only 50% of models used human measures for parametrization; none performed quantitative validation with real-world data.
  • Most models utilized ordinary differential equations, focused on short timeframes (≤12 hours), and explored various metabolic pathways.

Conclusions:

  • Existing computational models can capture essential short-term brain metabolism components.
  • Models exhibit high internal but poor external validity, limiting their application to real-life scenarios.
  • Adjustments are necessary to improve external validity and assess lifetime brain metabolism trajectories.