Neuromodulation Therapies in Pre-Clinical Models of Traumatic Brain Injury: Systematic Review and Translational Applications
Bibliographic record
Abstract
Traumatic brain injury (TBI) has been associated with several lasting impairments that affect quality of life. Pre-clinical models of TBI have been studied to further our understanding of the underlying short-term and long-term symptomatology. Neuromodulation techniques have become of great interest in recent years as potential rehabilitative therapies after injury because of their capacity to alter neuronal activity and neural circuits in targeted brain regions. This systematic review aims to provide an overlook of the behavioral and neurochemical effects of transcranial direct current stimulation (tDCS), transcranial magnetic stimulation (TMS), deep brain stimulation (DBS), and vagus nerve stimulation (VNS) in pre-clinical TBI models. After screening 629 abstracts, 30 articles were pooled for review. These studies showed that tDCS, TMS, DBS, or VNS delivered to rodents restored TBI-induced deficits in coordination, balance, locomotor activity and improved cognitive impairments in memory, learning, and impulsivity. Potential mechanisms for these effects included neuroprotection, a decrease in apoptosis, neuroplasticity, and the restoration of neural circuit abnormalities. The translational value, potential applicability, and the interpretation of these findings in light of outcome data from clinical trials in patients with TBI are discussed.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".