Abstract TMP48: Subcortical Volumes Associated With Post-Stroke Motor Performance Vary Across Impairment Severity, Time Since Stroke, and Lesion Laterality: an ENIGMA Stroke Recovery Analysis
Bibliographic record
Abstract
Associations between subcortical gray matter volume and motor performance post-stroke are unclear, partly because many stroke MRI studies are underpowered. Potential influences of the severity of motor impairment, lesion laterality, and time since stroke on these associations is also unknown. Here, we addressed these questions using a large dataset (n=629) from the ENIGMA Stroke Recovery working group (http://enigma.usc.edu). Regression analyses examined brain volumes as predictors of motor scores. ENIGMA FreeSurfer protocols extracted volumes from 16 subcortical regions on T1-weighted MRIs; segmentations were manually quality controlled. Motor scores were calculated as a percentage of the maximum possible score (100% = no impairment). Covariates (e.g., age, sex, intracranial volume) were modeled. Statistical significance was assessed nonparametrically by permutation. Separate analyses were performed, stratifying by motor severity and time since stroke. Each analysis was also subdivided by lesioned hemisphere. The motor severity analysis (Table 1A) used subgroups of mild (66.7-99.9%), moderate (33.3-66.6%), and severe (0-33.2%). Significant associations were found for mild and moderate, but not severe, stroke; only the left hemisphere stroke group showed further significant results. The time since stroke analysis (Table 1B) used subgroups of acute (<1 month), subacute (1-6 months), and chronic (>6 months). Significant associations were found in chronic stroke, but not acute, subacute. Left versus right hemisphere lesions generated different results in chronic stroke. Overall, these results show that the most significant associations between subcortical volumes and motor outcomes are in chronic mild-to-moderate stroke. Stroke subgroups may recover via disparate mechanisms; establishing biomarkers of impairment and disability across stroke subgroups may be useful for clinical trials.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".