Deep brain stimulation-induced normalization of hippocampal synchrony in a transgenic rat model of Alzheimer's disease
Bibliographic record
Abstract
Background and Aim: Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by disrupted neural network dynamics and neuronal loss.Deep brain stimulation (DBS) may restore network function and abate cognitive deficits.In a transgenic rat model of AD, we investigated the dependence of hippocampal neuronal activity on a range of DBS parameters, aiming to identify stimulation conditions that transiently restore impaired network function. Material and Methods:We used 16-month-old TgF344-AD and NTg rats under light anesthesia and performed simultaneous DBS and high-resolution intracerebral recordings in the hippocampus using a linear multielectrode array.DBS was delivered in bipolar mode, at varying frequencies, amplitudes and duration, while monitoring local field potentials (LFP) and spiking activity.Phase-amplitude coupling (PAC), neuronal power, and firing rates were analyzed prior to and following DBS.Linear mixed effects models were used to evaluate the influence of genotype, sex, and stimulation parameters on the electrophysiological markers.Results: With increasing DBS frequency and amplitude, hippocampal power and PAC rose in all rats, particularly within the delta-theta range.When compared to NTgs, TgAD rats showed attenuated power but increased PAC responses to DBS.Low frequency DBS induced higher entrainment in the post-relative to during-DBS period in all animals.Compared to their non-transgenic littermates, TgAD rats showed reduced entrainment responses.Conclusions: These findings demonstrate that hippocampal responses to DBS have a parameter-dependent profile that is differentially modulated by AD pathology.Our study provides a foundation for tailoring DBS parameters to compensate for distinct neuronal deficits in established AD, supporting the use of electrophysiological biomarkers to guide individualized neuromodulation strategies
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".