Environmental Enrichment for Stroke and Traumatic Brain Injury: Mechanisms and Translational Implications
Bibliographic record
Abstract
Acquired brain injuries, such as ischemic stroke, intracerebral hemorrhage (ICH), and traumatic brain injury (TBI), can cause severe neurologic damage and even death. Unfortunately, currently, there are no effective and safe treatments to reduce the high disability and mortality rates associated with these brain injuries. However, environmental enrichment (EE) is an emerging approach to treating and rehabilitating acquired brain injuries by promoting motor, sensory, and social stimulation. Multiple preclinical studies have shown that EE benefits functional recovery, including improved motor and cognitive function and psychological benefits mediated by complex protective signaling pathways. This article provides an overview of the enriched environment protocols used in animal models of ischemic stroke, ICH, and TBI, as well as relevant clinical studies, with a particular focus on ischemic stroke. Additionally, we explored studies of animals with stroke and TBI exposed to EE alone or in combination with multiple drugs and other rehabilitation modalities. Finally, we discuss the potential clinical applications of EE in future brain rehabilitation therapy and the molecular and cellular changes caused by EE in rodents with stroke or TBI. This article aims to advance preclinical and clinical research on EE rehabilitation therapy for acquired brain injury. © 2024 American Physiological Society. Compr Physiol 14:5291-5323, 2024.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".