Reward network modulation while learning movements to music during weekly multisensory training program for people with Parkinson’s disease: mood effects over 7-yrs
Bibliographic record
Abstract
Abstract Dance is an art form and simultaneously a form of exercise that provides pleasure to individuals who practise it regularly with subsequent improvements to motor and non-motor symptoms in people with Parkinson’s disease (PD). Recent evidence suggests that dance promotes neuroplasticity and correlates with improvements of depression scores in individuals with PD. However, no neuroplasticity studies using fMRI and EEG have been conducted on people with Parkinson’s disease (PwPD) who attend community dance programs. Thus, we present an observational examination across 7-years that explores the effects of non-motor symptoms of anxiety and depression. The study utilized multimodal recordings from functional MRI (fMRI), electroencephalogram (EEG) and measured affective state using the expanded version of the Positive and Negative Affect Schedule (PANAS-X) questionnaire. fMRI was performed at four time points in the first year (September, December, January, and May) where participants visualized/imagined the choreography they had learned in the dance studio while listening to the recording of the music in the MRI. Resting state EEG was recorded immediately before and after the dance class when the dancers volunteered over the 7 years. In addition, mood was monitored using the PANAS-X in conjunction with the EEG sessions. The PANAS-X was sorted by generalized anxiety disorder (positive and negative affect) symptoms, and major depressive disorder (positive and negative affect) symptoms, along with standard general negative affect (GNA) and general positive affect (GPA). This analysis reveals significant improvements in both symptoms after dance and across time. fMRI results show significant BOLD signal changes in the nucleus accumbens and anterior cingulate cortex, along with trends in other regions in the reward pathway such as the ventral tegmental area and the orbitofrontal cortex. rsEEG results show significant increases in alpha peak values after dance, in both EO and EC conditions. The findings support the idea that dance is a multimodal form of neurorehabilitation with beneficial effects on both motor and non-motor symptoms, including anxiety and depression.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".