Mobile brain/body Imaging in dance: A dynamic transdisciplinary field for applied research
Bibliographic record
Abstract
Neuroscience of dance is an emerging field with important applications related to health and well-being, as dance has shown potential to foster adaptive neuroplasticity and is increasingly popular as a therapeutic activity or adjunct therapy for people living with conditions such as Parkinson's and Alzheimer's diseases. However, the multimodal nature of dance presents challenges to researchers aiming to identify mechanisms involved when dance is used to combat neurodegeneration or support healthy ageing. Requiring simultaneous engagement of motor and cognitive domains, dancing includes coordination of systems involved in timing, memory and spatial learning. Studies on dance to this point rely primarily on assessments of brain dynamics and structure through pre/post-tests or studies on expertise, as traditional brain imaging modalities restrict participant movement to avoid movement-related artefacts. In this paper, we describe the process of designing and implementing a study that uses mobile brain/body imaging (MoBI) to investigate real-time changes in brain dynamics and behaviour during the process of learning and performing a novel dance choreography. We show the potential for new insights to emerge from the coordinated collection of movement and brain-based data, and the implications of these in an emerging field whose medium is motion.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".