Altered Basal Ganglia Network Topology Associated With Auditory–Motor Synchronization
Bibliographic record
Abstract
INTRODUCTION: Auditory-motor synchronization (AMS) embedded in Rhythmic Auditory Stimulation (RAS) is a validated method to improve gait, upper limb function, and motor speech in people with neurologic disorders like Parkinson's disease (PD). Predictable auditory cues optimize spatial movement patterns, and research has suggested that AMS reduces the brain's reliance on dopaminergic (DA) response in the ventral striatum. To gain a mechanistic understanding of the positive clinical outcomes related to AMS, this pilot study investigates the effects of AMS on the basal ganglia network (BGN) using brain network science methods. METHODS: Fourteen healthy adults (aged 22-37, seven females) completed two fMRI finger tapping tasks, a self-paced continuation (self) task and an auditory-motor synchronized (sync) task, both performed at 1 Hz. Using a modularity analysis of brain network data, we assessed the spatial consistency of the BGN. Additionally, we used a mixed-effects regression framework to test the hypotheses that changes in global and local efficiency are associated with the experimental tasks. RESULTS: The spatial consistency of the BGN community was significantly greater in the sync task compared to the self task. Then, the regression model showed a significant change in the BGN's efficiency in the sync task over the self task. Specifically, the probability and the strength of connections between highly efficient nodes were significantly greater, indicating a more synchronized BGN. CONCLUSION: AMS significantly changed the network topology of the BGN compared to no AMS. Specifically, the BGN became more functionally synchronized with AMS due to, mainly, greater network efficiency. These findings contribute to the growing mechanistic knowledge of how the BGN functional connections change with AMS and why AMS is a powerful tool to treat neurologic disorders such as PD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".