Modelling imaging biomarkers of Alzheimer's disease using animal models
Bibliographic record
Abstract
Alzheimer's disease is a progressive neurodegenerative disorder characterized by brain amyloid-beta aggregating into plaques, intraneuronal neurofibrillary tangles and neuronal losses, eventually leading to cognitive decline and dementia. The complex interplay between these pathophysiological hallmarks is still not yet fully understood, and therapeutic approaches based on the current conceptualization of Alzheimer pathology have yet to yield potent disease-modifying treatments. To further our understanding of these pathological interactions and to guide therapeutic targets, two tools can be particularly helpful: imaging biomarkers, which allow the in vivo quantification of pathophysiological build-up and neurodegeneration even in the absence of clear clinical symptoms; and animal models, which can be used to study the specific expression of certain aspects of the pathology in a controlled environment.The combination of animal models with multimodal neuroimaging techniques provide a unique platform that can be used to test predictions generated by theoretical disease models and to validate new avenues for potential biomarkers and drug discovery. Here, we performed several studies highlighting the translational power of such approaches to further research on Alzheimer's disease pathophysiology.First, with a longitudinal and multimodal study using the McGill-R-Thy1-APP transgenic rat model of amyloid-beta pathology, we showed that even in the absence of neurofibrillary tangles or widespread neuronal death, amyloid-beta can induce marked neurodegeneration as measured with several PET and MRI markers as well as memory losses. Second, using the same transgenic model, we showed a beneficial effect of hippocampal microglial activation on memory and resting-state connectivity. Finally, using an immunolesioned rat model, we validated the use of [18F]FEOBV as a sensitive PET radiotracer able to measure specific losses of cholinergic synapses, and then confirmed these findings with [18F]FEOBV autoradiography in brain tissue of patients with Alzheimer's disease.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".