98%
921
2 minutes
20
Background: Multidomain interventions have demonstrable benefits for promoting healthy aging, but self-empowerment strategies to sustain long-term gains remain elusive.
Objective: This study evaluated the effects of digital somatosensory dance game participation on brain imagery changes as primary outcomes and other physical and mental health measures as secondary outcomes related to healthy aging.
Methods: Between August 31, 2020, and June 27, 2021, this randomized controlled trial recruited 60 eligible participants older than 55 years with no recent engagement in digital dance games. A computer-generated randomization sequence was used to allocate participants 1:1, without stratification, to an intervention group (n=30) who underwent digital somatosensory dance game training or a control group (n=30). An anonymized code masked the intervention allocations from the investigators, and individuals who assigned the interventions were not involved in analyzing the study data. The intervention entailed two 30-minute dance game sessions per week for 6 months, and the control group received healthy aging education. Primary outcomes were brain imagery changes. All variables were measured at baseline and the 6-month follow-up, and intervention effects were estimated using t tests with intention-to-treat analyses.
Results: Compared with the control group, intervention participants had significantly different brain imagery in the gray matter volume (GMV) of the left putamen (estimate 0.016, 95% CI 0.008 to 0.024; P<.001), GMV of the left pallidum (estimate 0.02, 95% CI 0.006 to 0.034; P=.004), and fractional amplitude of low frequency fluctuations of the left pallidum (estimate 0.262, 95% CI 0.084 to 0.439; P=.004). Additionally, the intervention group had different imagery in the cerebellum VI GMV (estimate 0.011, 95% CI 0.003 to 0.02; P=.01). The intervention group also had improved total Montreal Cognitive Assessment scores (estimate 1.2, 95% CI 0.27 to -2.13; P<.01), quality of life (estimate 7.08, 95% CI 2.35 to 11.82; P=.004), and time spent sitting on weekdays (estimate -1.96, 95% CI -3.33 to -0.60; P=.005). Furthermore, dance performance was significantly associated with cognitive performance (P=.003), health status (P=.14), resilience (P=.007), and demoralization (P<.001).
Conclusions: Digital somatosensory dance game participation for 6 months was associated with brain imagery changes in multiple regions involving somatosensory, motor, visual, and attention functions, which were consistent with phenotypic improvements associated with healthy aging.
Trial Registration: ClinicalTrials.gov NCT05411042; https://clinicaltrials.gov/study/NCT05411042.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11322681 | PMC |
http://dx.doi.org/10.2196/57694 | DOI Listing |
J Neurosci Methods
September 2025
Department of Computer Science and Engineering, IIT (ISM) Dhanbad, Dhanbad, 826004, Jharkhand, India. Electronic address:
Background: Interpretation of motor imagery (MI) in brain-computer interface (BCI) applications is largely driven by the use of electroencephalography (EEG) signals. However, precise classification in stroke patients remains challenging due to variability, non-stationarity, and abnormal EEG patterns.
New Methods: To address these challenges, an integrated architecture is proposed, combining multi-domain feature extraction with evolutionary optimization for enhanced EEG-based MI classification.
Front Sports Act Living
August 2025
Faculty of Physical Education, China West Normal University, Nanchong, China.
Understanding how athletes mentally simulate and anticipate actions provides key insights into experience-driven brain plasticity. While previous studies have investigated motor imagery and action anticipation separately, little is known about how their underlying neural mechanisms converge or diverge in expert performers. This study conducted a meta-analysis using activation likelihood estimation (ALE) and meta-analytic connectivity modeling (MACM) to compare brain activation patterns between athletes and non-athletes across both tasks.
View Article and Find Full Text PDFBrain Stimul
September 2025
Department of Philosophy, University of Milan, Milan, via Festa Del Perdono, 7, 20122, Italy; Cognition in Action (CIA) Unit, PHILAB, University of Milan, Via Santa Sofia, 9, 20122, Italy. Electronic address:
Background: To investigate covert motor processes, transcranial magnetic stimulation (TMS) studies often use motor-evoked potentials (MEPs) as a proxy for inferring the state of motor representations. Typically, these studies test motor representations of actions that can be produced by the isolated contraction of one muscle, limiting both the number of recorded muscles and the complexity of tested actions. Furthermore, univariate analyses treat MEPs from different muscles as independent, overlooking potentially meaningful intermuscular relationships encoded in MEPs amplitude patterns at the single-trial level.
View Article and Find Full Text PDFIEEE J Biomed Health Inform
September 2025
The multi-user motor imagery brain-computer interface (BCI) is a new approach that uses information from multiple users to improve decision-making and social interaction. Although researchers have shown interest in this field, the current decoding methods are limited to basic approaches like linear averaging or feature integration. They ignored accurately assessing the coupling relationship features, which results in incomplete extraction of multi-source information.
View Article and Find Full Text PDFNeuroscience
September 2025
College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an 710054, China; Xi'an Key Laboratory of Electrical Equipment Condition Monitoring and Power Supply Security., Xi'an 710054, China.
Motor imagery (MI) based brain-computer interfaces (BCI) decode neural activity to generate command outputs. However, the limited number of distinguishable commands in traditional MI-BCI systems restricts practical applications. To overcome this limitation, we propose a multi-character classification framework based on Electroencephalography (EEG) signals.
View Article and Find Full Text PDF