Sensory-parietal cortical stimulation improves motor recovery in severe capsular infarct
Bibliographic record
Abstract
The prevalence of subcortical white matter strokes in elderly patients is on the rise, but these patients show mixed responses to conventional rehabilitative interventions. To examine whether cortical electrical stimulation can promote motor recovery after white matter stroke, we delivered stimulation to a small or wide region of sensory-parietal cortex for two weeks in a rodent model of circumscribed subcortical capsular infarct. The sham-operated group (SOG) showed persistent and severe motor impairments together with decreased activation in bilateral sensorimotor cortices and striatum. In contrast, sensory-parietal cortex stimulation significantly improved motor recovery: final recovery levels were 72.9% of prelesion levels in the wide stimulation group (WSG) and 37% of prelesion levels in the small stimulation group (SSG). The microPET imaging showed reversal of cortical diaschisis in both groups: in both hemispheres for the WSG, and in the hemisphere ipsilateral to stimulation in the SSG. In addition, we observed activation of the corpus callosum and subcortical corticostriatal structures after stimulation. The results from the c-Fos mapping study were grossly consistent with the microPET imaging. Sensory-parietal cortex stimulation may therefore be a useful strategy for overcoming the limits of rehabilitative training in patients with severe forms of subcortical capsular infarct.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".