Abstract WP181: Linking Post-Stroke Injury, Neural Function, and Motor Behavior With EEG
Bibliographic record
Abstract
Introduction: Elucidating the relationship between stroke-induced injury and changes in neural function may potentiate rehabilitation and treatment development. This study examined post-stroke associations between functional electroencephalography (EEG) measures, structural injury, and motor behavior. Hypothesis: Greater injury extent and motor impairment correspond to increasing delta band (1-3Hz) and decreasing high beta band (20-30Hz) EEG measures in a time-dependent manner. Methods: Subjects with stroke completed a 3-minute resting-state EEG recording, an MRI, and motor testing (Fugl-Meyer, FM). EEG power and ipsilesional primary motor cortex (M1) coherence (connectivity) were computed from dense-array EEG (194 leads). Global (lesion volume) and motor-specific (%CST injury) injury were measured on MRI. Associations between EEG measures and injury/behavior were significant if ≥10% of leads demonstrated significance (p≤0.05). Results: Sixty individuals (mean age 56.6 years, 11.9 months post-stroke) were stratified into subacute (n=24) and chronic (n=36) groups. For EEG power, larger delta band power correlated strongly with increasing lesion volume early after stroke, and this association expanded (21 to 82 leads) in chronic stroke to bilateral premotor, M1, and temporal-parietal regions. This delta power expansion strongly correlated with greater FM scores. For EEG coherence, larger delta band ipsilesional M1 coherence with leads overlying bilateral fronto-temporal-parietal regions strongly correlated with both greater lesion volume (64 leads) and CST injury (52 leads) early after stroke. These associations were reduced in chronic stroke but still strongly correlated with greater FM scores. Beta band power and coherence measurements showed negative and positive correlations with lesion volume, respectively, but did not relate to motor behavior. Conclusions: Low-frequency band EEG power and coherence measures predominantly capture global injury arising from stroke. The expansion of delta power and the reduction of delta coherence from subacute to chronic stroke are adaptive responses indicative of lower motor impairment, and these measures may prove valuable in monitoring stroke 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) | 0.006 | 0.001 |
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".