Weekly dance training over eight months reduces depression and correlates with fMRI brain signals in subcallosal cingulate gyrus (SCG) for people with Parkinson’s Disease: An observational study
Bibliographic record
Abstract
Abstract Depression affects 280 million people globally and is considered a prodromal feature for increasingly prevalent neurodegenerative conditions including Parkinson’s disease (PD). With age-related neurodegeneration on the rise, it’s important to consider non-invasive, inexpensive interventions such as dance. Dance has emerged as a complementary treatment that may support adaptive neuroplasticity while diminishing motor and non-motor symptoms including depression. Although dance has been shown to impact brain structures and functions with improvements in motor and psychological symptoms, the neural mechanisms underlying depression/mood remain elusive. Our observational study tracks the relationship between depression scores and functional neuroimaging measures for subgenual cingulate gyrus (SCG). While learning choreography over an 8-month period, 34 dancers [23 people with PD] completed GDS questionnaires before and after their community dance classes. Seventeen of these dancers had BOLD fMRI scans conducted using learning-related protocols to examine underlying neural mechanisms for moving to music over 4 times points of learning. A significant decrease in depression scores correlated with a reduced BOLD signal from SCG, a putative node in the neural network of depression. Conclusions: This is the first study to clearly find a correlation with a neural substrate involved in mood changes as a function of dance for PD. Not only do the results contribute to understanding neural mechanisms involved in adaptive plasticity with a learning task, but they also uncover reduced activity within SCG during longitudinal therapeutic dance interventions. These results are especially illuminating since SCG is a controversial target in deep brain stimulation (DBS) used in the treatment of major depressive disorder (MDD).
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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".