Anatomical and Functional Characterization in Children With Unilateral Cerebral Palsy: An Atlas-Based Analysis
Bibliographic record
Abstract
Background. Variability in hand function among children with unilateral cerebral palsy (UCP) might reflect the type of brain injury and resulting anatomical sequelae. Objective. We used atlas-based analysis of structural images to determine whether children with periventricular (PV) versus middle cerebral artery (MCA) injuries might exhibit unique anatomical characteristics that account for differences in hand function. Methods. Forty children with UCP underwent structural brain imaging using 3-T magnetic resonance imaging. Brain lesions were classified as PV or MCA. A group of 40 typically developing (TD) children served as comparison controls. Whole brains were parcellated into 198 structures (regions of interest) to obtain volume estimates. Dexterity and bimanual hand function were assessed. Unbiased, differential expression analysis was performed to determine volumetric differences between PV and MCA groups. Principal component analysis (PCA) was performed and the top 3 components were extracted to perform regression on hand function. Results. Children with PV had significantly better hand function than children with MCA. Multidimensional scaling analysis of volumetric data revealed separate clustering of children with MCA, PV, and TD children. PCA extracted anatomical components that comprised the 2 types of brain injury. In the MCA group, reductions of volume were concentrated in sensorimotor structures of the injured hemisphere. Models using PCA predicted hand function with greater accuracy than models based on qualitative brain injury type. Conclusions. Our results highlight unique quantitative differences in children with UCP that also predict differences in hand function. The systematic discrimination between groups found in our study reveals future questions about the potential prognostic utility of this approach.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".