Early Electrophysiological Aberrations in the Hippocampus of the TgF344-AD Rat Model as a Potential Biomarker for Alzheimer’s Disease Prognosis
Bibliographic record
Abstract
Abstract The hippocampus is thought to guide navigation and has an essential contribution to learning and memory. Hippocampus is one of the brain regions impaired in Alzheimer’s disease (AD), a neurodegenerative disease with progressive memory impairments and cognitive decline. Although successful treatments for AD are still not available, developing new strategies to detect AD at early stages before clinical manifestation is crucial for timely interventions. Here, we investigated in the TgF344-AD rat model the classification of AD-transgenic rats versus Wild-type littermates (WT) from electrophysiological activity recorded in the hippocampus of freely moving subjects at an early, pre-symptomatic stage of the disease (6 months old). To this end, recorded signals were filtered in two separate frequency regimes namely low frequency LFP signals and high frequency spiking activity and passed to machine learning (ML) classifiers to identify the genotype of the rats (TG vs. WT). For the low frequency analysis, we first filtered the signals and extracted the power spectra in different frequency bands known to carry differential information in the hippocampus (delta, theta, slow- and fast-gamma) while for the high frequency analysis, we extracted spike-trains of neurons and calculated different distance metrics between them, including Van Rossum (VR), Inter Spike Interval (ISI), and Event Synchronization (ES). These measures were then used as features for classification with different ML classifiers. We found that both low and high frequency signals were able to classify the rat genotype with a high accuracy with specific signals such as the gamma band power, providing an important fraction of information. In addition, when we combined information from both low and high frequency the classification was boosted indicating that independent information is present across the two bands. The results of this study offer a better insight into how different regions of the hippocampus are affected in earlier stages of AD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".