From Sound to Movement: Mapping the Neural Mechanisms of Auditory–Motor Entrainment and Synchronization
Bibliographic record
Abstract
BACKGROUND: Humans exhibit a remarkable ability to synchronize their actions with external auditory stimuli through a process called auditory-motor or rhythmic entrainment. Positive effects of rhythmic entrainment have been demonstrated in adults with neurological movement disorders, yet the neural substrates supporting the transformation of auditory input into timed rhythmic motor outputs are not fully understood. We aimed to systematically map and synthesize the research on the neural correlates of auditory-motor entrainment and synchronization. METHODS: Following the PRISMA-ScR guidelines for scoping reviews, a systematic search was conducted across four databases (MEDLINE, Embase, PsycInfo, and Scopus) for articles published between 2013 and 2023. RESULTS: From an initial return of 1430 records, 22 studies met the inclusion criteria and were synthesized based on the neuroimaging modality. There is converging evidence that auditory-motor synchronization engages bilateral cortical and subcortical networks, including the supplementary motor area, premotor cortex, ventrolateral prefrontal cortex, basal ganglia, and cerebellum. Specifically, the supplementary motor area and the basal ganglia are essential for beat-based timing and internally guided rhythmic movements, while the cerebellum plays an important role in tracking and processing complex rhythmic patterns and synchronizing to the external beat. Self-paced tapping is associated with additional activations in the prefrontal cortex and the basal ganglia, suggesting that tapping in the absence of auditory cues requires more neural resources. Lastly, existing studies indicate that movement rate and the type of music further modulate the EEG power in the alpha and beta frequency bands. CONCLUSIONS: These findings are discussed in the context of clinical implications and rhythm-based therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".