Shared and distinct voxel-based lesion-symptom mappings for spasticity and impaired movement in the hemiparetic upper limb
Bibliographic record
Abstract
Hemiparesis and spasticity are common co-occurring manifestations of hemispheric stroke. The relationship between impaired precision and force in voluntary movement (hemiparesis) and the increment in muscle tone that stems from dysregulated activity of the stretch reflex (spasticity) is far from clear. Here we aimed to elucidate whether variation in lesion topography affects hemiparesis and spasticity in a similar or dis-similar manner. Voxel-based lesion-symptom mapping (VLSM) was used to assess the impact of lesion topography on (a) upper limb paresis, as reflected by the Fugl-Meyer Assessment scale for the upper limb and (b) elbow flexor spasticity, as reflected by the Tonic Stretch Reflex Threshold, in 41 patients with first-ever stroke. Hemiparesis and spasticity were affected by damage to peri-Sylvian cortical and subcortical regions and the putamen. Hemiparesis (but not spasticity) was affected by damage to the corticospinal tract at corona-radiata and capsular levels, and by damage to white-matter association tracts and additional regions in the temporal cortex and pallidum. VLSM conjunction analysis showed only a minor overlap of brain voxels where the existence of damage affected both hemiparesis and spasticity, suggesting that control of voluntary movement and regulation of muscle tone at rest involve largely separate parts of the motor network.
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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".