Impact of Optogenetic Activation of the Thalamic Reticular Nucleus on Sleep Architecture in Mice
Bibliographic record
Abstract
Abstract Alzheimer’s disease (AD) is a progressive neurodegenerative disorder affecting millions worldwide and is often accompanied by significant sleep disturbances, such as sleep fragmentation, early awakenings, decreased sleep efficiency, and insomnia. It has been suggested that the alterations in activity of the thalamic reticular nucleus (TRN) are closely associated with sleep disruptions in AD. Evidence suggests that activating neurons expressing gamma-aminobutyric acid (GABA) within the TRN may enhance sleep quality and potentially ameliorate neuropathology associated with AD. However, the precise mechanisms through which TRN influences sleep disruptions and AD pathophysiology remain poorly understood. In this study, we investigated whether activating GABAergic TRN neurons could alter sleep architecture in wild-type mice. Utilizing optogenetic stimulation, we observed that activation of these neurons did not significantly alter sleep state durations or delta wave power, a key indicator of Slow Wave Sleep (SWS). Furthermore, the application of a two-virus strategy inadvertently led to non-specific opsin expression beyond the targeted TRN area. We discuss the potential factors that contributed to these outcomes, providing directions for future investigations to better delineate the role of the TRN in sleep and AD.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".