The Fickleness of Neuronal Activation: The Role of Neuronal Stochasticity in Cognitive Impairment in the Fischer 344 Transgenic Rat Model of Alzheimer's Disease
Bibliographic record
Abstract
BACKGROUND: Healthy brain function undergoes continuous remodeling of neuronal functional properties even in environmental and behavioral stable conditions, which likely leads to ongoing, activity-independent synaptic changes that contribute to the stochastic responsiveness of neurons to stimulation. Reduction in volatility of neuronal responses to repeated stimulation is likely associated with the impaired information processing in brain network. METHOD: We inserted the ultra-high density Neuropixels 1.0 electrode (10-mm long, single-shank, 384 recording channels, 70 x 24 μm cross-section) so as to span both primary somatosensory cortex and hippocampal CA1 and CA3 regions. The electrophysiological data were sampled at 30 kHz for quantifying activity at rest and during forepaw stimulation (60s, 3Hz, 10mA). Our guiding hypothesis is that a lower trial-dependent variation in the subset of neurons activated by a repetitive stimulus results more impaired cognition impairment in AD progression. RESULT: Our preliminary findings suggest that cognitively impaired rats exhibit a lower percentage of neurons responding to each stimulus, a reduced total number of responsive neurons, and decreased variability in oscillatory activity in the hippocampus compared to their cognitively maintained littermates, independent of their AD genotypes. CONCLUSION: The final results of this project have the potential to identify a novel target for presymptomatic AD interventions, and early stratification of AD patients by their susceptibility to dementia.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".