Post-Stroke Resting-State EEG Connectivity: A Longitudinal Neuro-Rehabilitation Study
Bibliographic record
Abstract
Stroke is a leading cause of permanent disability worldwide. Even after adequate treatment, the majority of patients do not recover fully, making them dependent on others for carrying out Activities of Daily Living (ADL). An improved understanding of the underlying mechanism of plasticity will help us in customizing the translational approach for learning and rehabilitation following a stroke. For this study, a 2-minute resting state EEG data were recorded at 5 time-points for 3-months after stroke onset. Directed Transfer Function (DTF) was used to study neural reorganization for 3 months. DTF for different brain regions and sub-bands was correlated with FMA. The information flow was studied for different brain regions as well as Affected Region (AR). Occipital region showed good correlation (r = 0.45 to 0.47) with FMA. Contra-lesional and ipsi-lesional regions trajectories complement each other during acute and sub-acute phase. The information outflow vs inflow imbalance of AR was restored by the end of 3 months. DTF can be used as biomarker for studying neuroplasticity. Occipital, temporal and motor cortex regions play an important role during neuro-rehabilitation. The information about different regions during rehabilitation will help us in designing subject-specific interventions for better 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".