Abstract TMP42: Coherent Neural Oscillations Inform Early Stroke Motor Recovery
Bibliographic record
Abstract
Introduction: Neural oscillations may contain valuable information for stroke rehabilitation. The objective of this study was to examine the predictive performance of neural oscillations in early stroke motor recovery using dense array electroencephalography (EEG). Since past work theorizes that neural oscillations underlie behavior, we hypothesized that coherent oscillations with ipsilesional primary motor cortex (M1) across a 1-30 Hz band would significantly predict early motor recovery post-stroke. Methods: Individuals with stroke admitted to an inpatient rehabilitation facility (IRF) completed a three-minute resting EEG recording and structural MRI around the time of IRF admission and motor testing (Functional Independence Measurement motor subscale (FIM-motor)) at IRF admission and discharge. We examined how well FIM-Motor change was predicted using EEG power and coherence with ipsilesional M1 across delta (1-3 Hz), theta (4-7 Hz), alpha (8-12 Hz), low beta (13-19 Hz), and high beta (20-30 Hz) frequency bands, along with corticospinal tract (CST) injury, in a lasso regression with K-fold cross-validation for deviance estimation. Results: Twenty-seven subjects (20 males, 58.3±14.6 years, 8-17 days post-stroke) with predominantly mild-moderate motor impairment participated. EEG ipsilesional M1 coherence with 16 leads overlying both hemispheres predicted 61.8% of FIM-motor change from IRF admission to discharge, with higher frequencies (alpha, high beta) positively relating to motor recovery. Lower frequencies overlying contralesional parietal (theta) and frontal (delta) regions inversely and positively related to motor recovery respectively. Coherence outperformed EEG power and CST injury measurements. Ipsilesional M1 coherence also predicted 55.2% of the variance in residuals derived from a predictive model containing only CST injury, suggesting that EEG coherence and CST injury contain unique information for motor recovery prediction. Conclusions: Early after stroke, coherence of neural oscillations with ipsilesional M1 across the entire brain through a wide frequency spectrum is best at predicting functional gains from inpatient rehabilitation and may be feasible as a bedside biomarker of motor recovery.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".