Non-invasive mapping of brain functions and brain recovery: Applying lessons from cognitive neuroscience to neurorehabilitation
Bibliographic record
Abstract
Modern cognitive neuroscience provides a powerful framework in which biological models of recovery and neurorehabilitation can be constructed and tested. The widespread availability, relatively low cost and informativeness of functional magnetic resonance imaging (fMRI) has made it the most popular of the techniques available to help with this task. Here, on the basis of functional imaging studies of stroke, diffuse microvascular disease and multiple sclerosis, we argue that processes of motor control and learning in the healthy brain share common mechanisms with those for adaptive functional reorganisation during spontaneous recovery after brain injury or with neurorehabilitation. Relatively stringent criteria can be met to confirm that adaptive functional reorganisation limits disability even in the adult brain: functional brain changes are related to disease burden, can be found in patients with demonstrable pathology but no clinical deficits and can be defined (in motor cortex) even in the absence of volitional recruitment. Initial studies of neurorehabilitation responses using fMRI and transcranial magnetic stimulation demonstrate that adaptive reorganisation can be manipulated directly with both pharmacological and behavioural interventions. The combination of strategies based on a strong biological rational with monitoring their effects using highly informative functional brain imaging methods heralds a new era of scientifically-founded neurorehabilitation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.008 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".