Amnesia after repeated head impact is caused by impaired synaptic plasticity in the memory engram
Bibliographic record
Abstract
Subconcussive head impacts are associated with the development of acute and chronic cognitive deficits. We recently reported that high-frequency head impact (HFHI) causes chronic cognitive deficits in mice through synaptic changes. To better understand the mechanisms underlying HFHI-induced memory decline, we used TRAP2/Ai32 transgenic mice to enable visualization and manipulation of memory engrams. We labeled the fear memory engram in male and female mice exposed to an aversive experience and subjected them to sham or HFHI. Upon subsequent exposure to natural memory recall cues, sham, but not HFHI, mice successfully retrieved fearful memories. In sham mice the hippocampal engram neurons exhibited synaptic plasticity, evident in amplified AMPA:NMDA ratio, enhanced AMPA-weighted tau, and increased dendritic spine volume compared with nonengram neurons. In contrast, although HFHI mice retained a comparable number of hippocampal engram neurons, these neurons did not undergo synaptic plasticity. This lack of plasticity coincided with impaired activation of the engram network, leading to retrograde amnesia in HFHI mice. We validated that the memory deficits induced by HFHI stem from synaptic plasticity impairments by artificially activating the engram using optogenetics and found that stimulated memory recall was identical in both sham and HFHI mice. Our work shows that chronic cognitive impairment after HFHI is a result of deficiencies in synaptic plasticity instead of a loss in neuronal infrastructure, and we can reinstate a forgotten memory in the amnestic brain by stimulating the memory engram. Targeting synaptic plasticity may have therapeutic potential for treating memory impairments caused by repeated head impacts.
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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 | 0.000 | 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".