Unleashing the potential of dance: a neuroplasticity-based approach bridging from older adults to Parkinson's disease patients

Front Aging Neurosci. 2023 Jun 26:15:1188855. doi: 10.3389/fnagi.2023.1188855. eCollection 2023.

Abstract

Parkinson's disease (PD) is a neurodegenerative disorder that affects >1% of individuals worldwide and is manifested by motor symptoms such as tremor, rigidity, and bradykinesia, as well as non-motor symptoms such as cognitive impairment and depression. Non-pharmacological interventions such as dance therapy are becoming increasingly popular as complementary therapies for PD, in addition to pharmacological treatments that are currently widely available. Dance as a sensorimotor activity stimulates multiple layers of the neural system, including those involved in motor planning and execution, sensory integration, and cognitive processing. Dance interventions in healthy older people have been associated with increased activation of the prefrontal cortex, as well as enhanced functional connectivity between the basal ganglia, cerebellum, and prefrontal cortex. Overall, the evidence suggests that dance interventions can induce neuroplastic changes in healthy older participants, leading to improvements in both motor and cognitive functions. Dance interventions involving patients with PD show better quality of life and improved mobility, whereas the literature on dance-induced neuroplasticity in PD is sparse. Nevertheless, this review argues that similar neuroplastic mechanisms may be at work in patients with PD, provides insight into the potential mechanisms underlying dance efficacy, and highlights the potential of dance therapy as a non-pharmacological intervention in PD. Further research is warranted to determine the optimal dance style, intensity, and duration for maximum therapeutic benefit and to determine the long-term effects of dance intervention on PD progression.

Keywords: dance; neurodegeneration; rhythm; sensorimotor integration; tremor.

Publication types

  • Review

Grants and funding

This study was supported by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 952401 (TwinBrain—TWINning the BRAIN with Machine Learning for Neuro-Muscular Efficiency). We also acknowledge financial support from the Slovenian Research Agency (research core funding no. P5-0381).