A scoping review of computational models of human cerebral metabolism
Bibliographic record
Abstract
BACKGROUND: Metabolism is progressively downregulated below healthy performance in Alzheimer's disease, at the same time as cellular and molecular damage demand increased energy. Understanding these findings will require a multifactorial causal framework, which we propose to test via mathematical modeling (Chamberland et al., 2024). Hence, we conducted a scoping review of mathematical models of human cerebral metabolism to guide our future implementation efforts. METHOD: Our scoping review was conducted following the 2020 PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines. We used the following keywords while searching the PubMed database for relevant entries, from inception date to March 2024: "mathematical", "model", "brain", and "glucose". Inclusion criteria required references to be in English, original works, targeting human cerebral metabolism in humans. Abstracts and full texts were screened by two independent reviewers (PFF and SD), with data extracted by PFF. Out of 299 screened studies, 25 remained for full-text screening, leading to 14 selected studies for data extraction and qualitative analysis (Figure 1). RESULT: Model replicability was overall adequate, with the inclusion of equations, parameters and initial conditions, which contributed to internal validity, while attention to the model's external validity was neglected (Figure 2). Only half of the selected studies referred to human measures in parametrization, and none performed quantitative validation with real-life findings. In line with our desired computational approach, 12 models used ordinary differential equations. Most models focused on short timeframes, with the longer applicable window being 12 hours. The theoretical focus of the models ranged from metabolite flow between neurons and astrocytes, to depictions of glucose carriers, metabolite signaling, biophysical, and electrochemical behaviors. CONCLUSION: There exist computational models that appear able to capture essential components of brain metabolism on a short timeframe. Overall, these models had high internal but poor external validity. To assess the trajectory of lifetime brain metabolism, adjustments will be required.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.123 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".