Early classification of Alzheimer's disease phenotype based on hippocampal electrophysiology in the TgF344-AD rat model
Bibliographic record
Abstract
The hippocampus plays a vital role in navigation, learning, and memory, and is affected in Alzheimer's disease (AD). This study investigated the classification of AD-transgenic rats versus wild-type littermates using electrophysiological activity recorded from the hippocampus at an early, presymptomatic stage of the disease (6 months old) in the TgF344-AD rat model. The recorded signals were filtered into low frequency (LFP) and high frequency (spiking activity) signals, and machine learning classifiers were employed to identify the rat genotype (TG vs. WT). By analyzing specific frequency bands in the low frequency signals and calculating distance metrics between spike trains in the high frequency signals, accurate classification was achieved. Gamma band power emerged as a valuable signal for classification, and combining information from both low and high frequency signals improved the accuracy further. These findings provide valuable insights into the early stage effects of AD on different regions of the hippocampus.
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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 | 0.000 |
| 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 | 0.000 |
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".