Greater Activation of Secondary Motor Areas Is Related to Less Arm Use After Stroke
Bibliographic record
Abstract
Background. Past studies have identified reorganization of brain activity in relation to motor outcome through standardized laboratory measures, which are quantifiable surrogates for arm use in real life. In contrast, accelerometers can provide a real-life estimate of arm and hand usage. Methods. Ten persons with chronic, subcortical stroke and 10 healthy controls of similar age performed a squeeze motor task at 40% maximum voluntary contraction during functional magnetic resonance imaging (fMRI). Use of the upper extremity was quantified over 3 consecutive days using wrist accelerometers. Correlations were performed between arm use and peak percent signal change (PSC) during grasp force production in 6 regions of interest (ROIs): bilateral primary motor cortex (M1), supplementary motor area (SMA), and premotor cortex (PM). Results. Results demonstrate that in healthy controls, PSC across all ROIs did not show a relationship between arm use and brain activation during force production. In contrast, after stroke, contralesional PM and M1 showed a significant ( P ≤ .05) correlation between increasing activation and decreasing paretic arm use, whereas ipsilesional PM showed a significant correlation ( P ≤ .05) between increasing activation and decreasing nonparetic arm use. Conclusions. The results of this pilot study demonstrate a negative relationship between brain activation and actual arm use after stroke. Larger studies using accelerometers that can detect amount and types of movement may offer further insight into brain reorganization and rehabilitation interventions.
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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 | 0.002 |
| 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) | 0.003 | 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".