GP.05 Sensory-motor network functional connectivity in hemiparetic children with perinatal stroke
Bibliographic record
Abstract
Background: Perinatal stroke is the most common cause of hemiparetic cerebral palsy. Post-stroke plasticity is well studied in adults, but mechanisms in children are poorly understood. To better understand the relationship between functional connectivity and disability, we used rsfMRI to compare connectivity with sensorimotor dysfunction. Methods: Subjects with periventricular venous infarction were compared to controls. Resting-state BOLD signal was acquired on 3T MRI and analyzed using SPM12. Functional connectivity was computed between S1 and M1 of the left/non-lesioned and right/ lesioned hemisphere. Primary outcome was connectivity expressed as a Pearson correlation coefficient. Motor function was measured using the Assisting Hand Assessment (AHA), and Melbourne Assessment (MA). Proprioceptive function was measured using a robotic position matching task (VarXY). Results: Subjects included 17 PVI and 21 controls. AHA and MA in patients were negatively correlated with connectivity (increased connectivity=poorer performance). Correlations between AHA and connectivity between non-lesioned M1 to bilateral S1s were significant. VarXY in PVI was inversely correlated with connectivity (increased connectivity=improved performance), significantly between non-lesioned S1 and bilateral M1s. Control VarXY was positively correlated with connectivity between non-dominant S1 to bilateral M1s. Conclusions: We demonstrated significant correlations between connectivity and motor/sensory function in PVI patients. Greater insight into understanding reorganization of brain networks following perinatal stroke may facilitate personalized rehabilitation.
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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 | 0.000 | 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) | 0.005 | 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".