Evaluation of differences in brain neurophysiology and morphometry associated with hand function in individuals with chronic stroke
Bibliographic record
Abstract
PURPOSE: Rehabilitation interventions need to be optimized to maximize therapeutic effects and minimize stroke-related disability. However, a comprehensive understanding of the neural substrates underlying recovery is lacking. The purpose of this study was to investigate relationships between brain anatomy, physiology and hand motor function in individuals with chronic stroke. METHODS: Transcranial magnetic stimulation (TMS) and magnetic resonance imaging (MRI) approaches were used to evaluate cortical excitability and brain structural morphometry in individuals with chronic stroke. Hemispheric differences and relationships between these measures and hand dexterity were evaluated. RESULTS: Hemispheric differences were observed for TMS and MRI measures. Bilateral hand dexterity correlated with TMS resting motor threshold and precentral gyral thickness. Transcallosal inhibition across hemispheres was positively associated with midcallosal white matter volume. Regression modeling results demonstrated that combining TMS and MRI measures predicted unique amounts of variance in hand dexterity. CONCLUSIONS: RESULTS confirm and extend findings showing differences in brain structure and function after stroke. RESULTS suggested a structure-function relationship underlying interhemispheric connectivity in chronic stroke. The utility of combined TMS and MRI measures to predict motor function can be used in future investigations to aid identifying optimal biomarkers of stroke recovery to predict response to rehabilitation to maximize treatment 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".