Morphological alterations of contralesional hemisphere relate to functional outcomes after stroke
Bibliographic record
Abstract
The present study aimed to investigate poststroke morphological alterations contralesionally and correlations with functional outcomes. Structural magnetic resonance images were obtained from 27 poststroke patients (24 males, 50.21 ± 10.97 years) and 20 healthy controls (13 males, 46.63 ± 12.18 years). Voxel-based and surface-based morphometry analysis were conducted to detect alterations of contralesional grey matter volume (GMV), cortical thickness (CT), gyrification index (GI), sulcus depth (SD), and fractal dimension (FD) in poststroke patients. Partial correlation analysis was used to explore the relationship between regions with significant structural differences and scores of clinical assessments, including Modified Barthel Index (MBI), Berg Balance Scale (BBS), Fugl-Meyer Assessment of Upper Extremity (FMA-UE), Mini-Mental State Examination (MMSE), and Montreal Cognitive Assessment (MoCA). Correction for multiplicity was conducted within each parameter and for all tests. GMV significantly decreased in the contralesional motor-related, occipital and temporal cortex, limbic system, and cerebellum lobe (P < 0.01, family-wise error [FWE] correction). Lower CT was found in the contralesional precentral and lingual gyrus (P < 0.01, FWE correction), while lower GI found in the contralesional superior temporal gyrus and insula (P < 0.01, FWE correction). There were significant correlations between GMV of contralesional lingual gyrus and MBI (P = 0.031, r = 0.441), and BBS (P = 0.047, r = 0.409) scores, and GMV of contralesional hippocampus and FMA-UE scores (P = 0.048, r = 0.408). In conclusion, stroke patients exhibited wide grey matter loss and cortical morphological changes in the contralesional hemisphere, which correlated with sensorimotor functions and the ability of daily living.
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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 | 0.001 | 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) | 0.001 | 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".