Shaking Up the Synapses: How Repeated Mild Traumatic Brain Injury Disrupts the fine balance of Synaptic Plasticity in the Juvenile Dentate Gyrus
Bibliographic record
Abstract
In Canada, the occurrence of traumatic brain injury is on the rise, with over 165,000 people affected every year. This condition can arise from a variety of causes, including motor vehicle collisions, falls, sports-related incidents, or assaults. Adolescents are particularly susceptible to multiple head injuries due to increased participation in sports and high-risk activities. Repeated mild traumatic injury (r-mTBI) can exacerbate symptom severity and impede neuropsychological recovery, with cognitive and psychiatric changes such as memory impairment that may persist for months or even years. It is thought that the hippocampus, which is vulnerable to injury may be responsible for impairments in memory after r-mTBI. To investigate the influence of memory impairment following r-mTBI, we employed the awake closed head injury (ACHI) model, which entailed delivering eight impacts throughout the day to male juvenile rats (PND 25 – 29). Hippocampal slices were prepared one or seven days after the last injury for in vitro electrophysiological recordings, and we examined the potential for long-term potentiation (LTP) and two distinct long-term depression pathways in the medial perforant path (MPP) of the dentate gyrus (DG). These findings demonstrated that r-mTBI did not disrupt 1 Hz - LTD but did result in significant impairment in long-term potentiation (LTP) and 10 Hz - long-term depression (LTD) in the juvenile male dentate gyrus (DG). These data are the first to describe the adverse impact of r-mTBI on e-CB-dependent LTD in the male DG, which could help link a novel pathway impairment to the memory deficits observed in people who have suffered concussions.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".