Manipulation of neuronal activity in the entorhinal-hippocampal circuit affects intraneuronal amyloid-β levels
Bibliographic record
Abstract
Abstract One of the neuropathological hallmarks of Alzheimer’s disease (AD) is the accumulation of amyloid-β (Aβ) plaques, which is preceded by intraneuronal build-up of toxic, aggregated Aβ during disease progression. Aβ plaques are first deposited in the neocortex before appearing in the medial temporal lobe, and tau pathology with subsequent neurodegeneration in the latter anatomical region causes early memory impairments in patients. Current research suggests that early intraneuronal Aβ build-up may begin in superficial layers of lateral entorhinal cortex (LEC). To examine whether manipulation of neuronal activity of LEC layer II neurons affected intraneuronal Aβ levels in LEC and in downstream perforant path terminals in the hippocampus (HPC), we used a chemogenetic approach to selectively and chronically silence superficial LEC neurons in young and aged 3xTg AD mice and monitored its effect on intraneuronal Aβ levels in LEC and HPC. Chronic chemogenetic silencing of LEC neurons led to reduced early intraneuronal Aβ in LEC and in projection terminals in the HPC, compared with controls. Early intraneuronal Aβ levels in the downstream HPC correlated with activity levels in superficial layers of LEC, with the subiculum being the earliest subregion involved, and our findings give evidence to early AD neuropathology originating in select neuronal populations.
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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) | 0.000 | 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 | 0.001 |
| 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".