Sensory tractography and robot‐quantified proprioception in hemiparetic children with perinatal stroke
Bibliographic record
Abstract
Perinatal stroke causes most hemiparetic cerebral palsy, resulting in lifelong disability. We have demonstrated the ability of robots to quantify sensory dysfunction in hemiparetic children but the relationship between such deficits and sensory tract structural connectivity has not been explored. It was aimed to characterize the relationship between the dorsal column medial lemniscus (DCML) pathway connectivity and proprioceptive dysfunction in children with perinatal stroke. Twenty-nine participants (6-19 years old) with MRI-classified, unilateral perinatal ischemic stroke (14 arterial, 15 venous), and upper extremity deficits were recruited from a population-based cohort and compared with 21 healthy controls. Diffusion tensor imaging (DTI) defined DCML tracts and five diffusion properties were quantified: fractional anisotropy (FA), mean, radial, and axial diffusivities (MD, RD, AD), and fiber count. A robotic exoskeleton (KINARM) tested upper limb proprioception in an augmented reality environment. Correlations between robotic measures and sensory tract diffusion parameters were evaluated. Lesioned hemisphere sensory tracts demonstrated lower FA and higher MD, RD, and AD compared with the non-dominant hemisphere of controls. Dominant (contralesional) hemisphere tracts were not different from controls. Both arterial and venous stroke groups demonstrated impairments in proprioception that correlated with lesioned hemisphere DCML tract diffusion properties. Sensory tract connectivity is altered in the lesioned hemisphere of hemiparetic children with perinatal stroke. A correlation between lesioned DCML tract diffusion properties and robotic proprioceptive measures suggests clinical relevance and a possible target for therapeutic intervention. Hum Brain Mapp 38:2424-2440, 2017. © 2017 Wiley Periodicals, Inc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".