Neurofysiologische beoordeling van hersennetwerkactiviteit betrokken bij cognitieve verwerking bij diermodellen van de ziekte van Alzheimer
Bibliographic record
Abstract
The pathophysiological processes of Alzheimer's disease (AD) are thought to start 20 years before cognitive symptoms are observed for the first time. During the initial stage, known as the pre-symptomatic phase of AD, also depicted as the preclinical phase, small alterations in the brain, unnoticeable to the affected individual, start to occur. With disease progression, these small changes advance into irreversible brain damage. It is believed that the best chance of therapeutic success in AD will be early intervention. Biomarker research has become one of the main investigational areas of AD as they could play an instrumental role in unequivocally identifying the initial phase of AD. Accumulating evidence suggests that neuronal oscillations play an important role in driving brain network communications. Furthermore, oscillatory alterations are commonly observed in patients with AD. It is still unclear whether they are early driving mechanisms of cognitive dysfunction. If these neuronal network alterations can be identified at the preclinical phase of AD, they could be implemented as a disease diagnostic tool. Numerous animal models recapitulating the hallmarks of AD pathogenesis have been created to facilitate the understanding of the molecular mechanisms underlying the disease process. Among the most common models are transgenic animals that produce amyloid-β (Aβ) pathology due to the artificial overexpression of the human amyloid precursor protein (APP) with mutations linked to familial AD. More recent models include the App knock-in mice that produce robust Aβ amyloidosis with physiological App expression levels. The primary goal of this study was to investigate electrophysiological readouts in combination with cognitive tasks to characterize electrophysiological functional alterations at ages relevant for the preclinical AD phase. Two animal models were used, one that overexpresses mutated human APP (the McGill-R-Thy1-APP rat) and another one that expresses mutated humanized App at physiological levels (AppNL-G-F mice). We hypothesized that in these models, at an age that mimics the preclinical stage of AD, Aβ amyloidosis causes aberrant network activity, which reflects the early development of cognitive disturbances. Our results from the AppNL-G-F characterization study do not support the hypothesis of early alterations in cognition relevant oscillations due to Aβ amyloidosis. This study also indicated that APP overexpression, and not Aβ overproduction, might be responsible for the abnormal network activity in the McGill-R-Thy1-APP rat model. More in general, the experimental approach presented in this thesis provides a versatile methodology for assessment of complex neuronal network dynamics in models of AD as well as in models of other neurodegenerative diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".