Beyond the Dorsal Column Medial Lemniscus in Proprioception and Stroke: A White Matter Investigation
Bibliographic record
Abstract
Proprioceptive deficits are common following stroke, yet the white matter involved in proprioception is poorly understood. Evidence suggests that multiple cortical regions are involved in proprioception, each connected by major white matter tracts, namely: Superior Longitudinal Fasciculus (branches I, II and III), Arcuate Fasciculus and Middle Longitudinal Fasciculus (SLF I, SLF II, SLF III, AF and MdLF respectively). However, direct evidence on the involvement of these tracts in proprioception is lacking. Diffusion imaging was used to investigate the proprioceptive role of the SLF I, SLF II, SLF III, AF and MdLF in 26 participants with stroke, and seven control participants without stroke. Proprioception was assessed using a robotic Arm Position Matching (APM) task, performed in a Kinarm Exoskeleton robotic device. Lesions impacting each tract resulted in worse APM task performance. Lower Fractional Anisotropy (FA) was also associated with poorer APM task performance for the SLF II, III, AF and MdLF. Finally, connectivity data surrounding the cortical regions connected by each tract accurately predicted APM task impairments post-stroke. This study highlights the importance of major cortico-cortical white matter tracts, particularly the SLF III and AF, for accurate proprioception after stroke. It advances our understanding of the white matter tracts responsible for 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".