Assessing the Integrity of Corticospinal Pathways From Primary and Secondary Cortical Motor Areas After Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Aside from the primary motor cortex, the corticospinal tract (CST) also receives fibers from dorsal and ventral premotor cortices and supplementary motor area, all of which might potentially contribute to motor function after stroke. We sought to quantify the microstructural integrity of CST originating from the hand representations in these 4 motor cortices separately and examined how these values related to hand motor impairment. METHODS: Probabilistic tractography from functional MRI-defined cortical sites demonstrated continuous CST originating from hand representations within each motor area in a group of healthy subjects. Microstructural integrity for each tract was calculated using fractional anisotropy at the level of the posterior limb of the internal capsule in a group of patients with chronic stroke. RESULTS: Fractional anisotropy was reduced in all 4 CSTs in the affected hemisphere. Grip strength correlated with the integrity of the CSTs originating from primary motor and dorsal premotor cortices, whereas, in a multiple regression model, the latter improved the ability of primary motor cortex CST to explain variability in grip strength. CONCLUSION: Handgrip critically depends on the CST originating in primary motor cortex but microstructural integrity of CST originating from premotor cortices appears to play a role in supporting motor function after stroke.
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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 | 0.001 |
| 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".