Negative Neuroplasticity in Chronic Traumatic Brain Injury and Implications for Neurorehabilitation
Bibliographic record
Abstract
Based on growing findings of brain volume loss and deleterious white matter alterations during the chronic stages of injury, researchers posit that moderate-severe traumatic brain injury (TBI) may act to "age" the brain by reducing reserve capacity and inducing neurodegeneration. Evidence that these changes correlate with poorer cognitive and functional outcomes corroborates this progressive characterization of chronic TBI. Borrowing from a framework developed to explain cognitive aging (Mahncke et al., Progress in Brain Research, 157, 81-109, 2006a; Mahncke et al., Proceedings of the National Academy of Sciences of the United States of America, 103(33), 12523-12528, 2006b), we suggest here that environmental factors (specifically environmental impoverishment and cognitive disuse) contribute to a downward spiral of negative neuroplastic change that may modulate the brain changes described above. In this context, we review new literature supporting the original aging framework, and its extrapolation to chronic TBI. We conclude that negative neuroplasticity may be one of the mechanisms underlying cognitive and neural decline in chronic TBI, but that there are a number of points of intervention that would permit mitigation of this decline and better long-term clinical outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".