Beta‐ and gamma‐band neuromagnetic oscillations in chronic stroke rehabilitation using music‐supported therapy and manual training
Bibliographic record
Abstract
Neural oscillations in beta (13-30 Hz) and gamma (>30 Hz) frequency bands index a variety of sensorimotor and cognitive processes. To compare two rehabilitation regimens for chronic stroke patients with a hemiparetic hand, we randomly assigned them to either music-supported therapy or physiotherapy for 10 weeks. Previously, we reported the music group's improved motor speed, mood, well-being, and rhythm perception. Here, we investigated changes in neural oscillatory activities. Here, our magnetoencephalography (MEG) data showed significant group-by-session interaction in both somatosensory and auditory-motor paradigms. The control group exhibited a prominent increase in gamma-band frequencies accompanying the somatosensory steady-state responses entrained by 22-Hz vibrotactile stimulation, indicating compensatory functions from the contralesional brain. In contrast, the music group showed a gradual enhancement of auditory-motor coupling in beta-band event-related power changes during passive metronome listening. The music group's increase in beta dynamics in the auditory cortex aligns with our previous work and their improvement in behavioral rhythm perception. Whole-brain data during listening and tapping demonstrated reduced beta modulation in the sensorimotor and prefrontal cortices and temporal poles in the music group, likely related to movements with less effort and attention. The current findings contribute to understanding the frequency-specific communications of the auditory, sensory, and motor systems.
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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) | 0.001 | 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".