Proprioception and motor performance after stroke: An examination of diffusion properties in sensory and motor pathways
Bibliographic record
Abstract
Proprioceptive and motor impairments commonly occur after stroke. Relationships between corticospinal tract (CST) fractional anisotropy (FA) and motor recovery have been identified. However, the relationship between sensory tract microstructure and proprioceptive recovery remains unexplored. Using probabilistic tractography, we examined the relationship between diffusion metrics in three tracts known to contain proprioceptive information (a) dorsal-column medial-lemniscal (DCML), (b) postcentral gyrus to supramarginal gyrus (POCG-SMG), (c) postcentral gyrus to Heschl's gyrus (POCG-HG) and proprioception at 1 (n = 26) and 6 months (n = 19) poststroke. Proprioception was assessed using two robotic tasks. Motor performance was also assessed robotically and compared to CST diffusion metrics. At 1-month poststroke, a nonsignificant relationship (r = -0.43, p = 0.05) was observed between DCML-FA and proprioceptive impairment. A moderate relationship was identified between POCG-SMG FA and POCG-HG FA and proprioceptive impairment (r = -0.47, p = 0.001 and r = -0.51, p = 0.008, respectively). No relationships were significant at 6 months poststroke. Similar to previous studies, lower CST-FA correlated with motor impairment at 1 month poststroke (r = -0.58, p = 0.002). While CST-FA is considered a predictor of motor impairment, our findings suggest that the relationship between FA and tracts containing proprioceptive information is not as straightforward and highlights the importance of sensory association areas in proprioception.
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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 | 0.001 |
| 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".