Mobile brain/body Imaging in dance: A dynamic transdisciplinary field for applied research

Dance as mindful movement: a perspective from motor learning and predictive coding

Defining "dance" is challenging, because many distinct classes of human movement may be considered dance in a broad sense. Although the most obvious category is rhythmic dancing to a musical beat, other categories of expressive movement such as dance improvisation, pantomime, tai chi, or Japanese butoh suggest that a more inclusive conception of human dance is needed. Here we propose that a specific type of conscious awareness plays an overarching role in most forms of expressive movement and can be used to define dance (in the broad sense). We can briefly summarize this broader notion of dance as "mindful movement." However, to make this conception explicit and testable, we need an empirically verifiable characterization of "mindful movement." We propose such a characterization in terms of predictive coding and procedural learning theory: mindful movement involves a "suspension" of automatization. When first learning a new motor skill, we are highly conscious of our movements, and this is reflected in neural activation patterns. As skill increases, automatization and overlearning occurs, involving a progressive suppression of conscious awareness. Overlearned, habitual movement patterns become mostly unconscious, entering consciousness only when mistakes or surprising outcomes occur. In mindful movement, this automatization process is essentially inverted or suspended, reactivating previously unconscious details of movement in the conscious workspace, and crucially enabling a renewed aesthetic attention to such details. This wider perspective on dance has important implications for potential animal analogs of human dance and leads to multiple lines of experimental exploration.

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

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.

Mobile cognition: imaging the human brain in the 'real world'

Cognitive neuroscience studies in humans have enabled decades of impactful discoveries but have primarily been limited to recording the brain activity of immobile participants in a laboratory setting. In recent years, advances in neuroimaging technologies have enabled recordings of human brain activity to be obtained during freely moving behaviours in the real world. Here, we propose that these mobile neuroimaging methods can provide unique insights into the neural mechanisms of human cognition and contribute to the development of novel treatments for neurological and psychiatric disorders. We further discuss the challenges associated with studying naturalistic human behaviours in complex real-world settings as well as strategies for overcoming them. We conclude that mobile neuroimaging methods have the potential to bring about a new era of cognitive neuroscience in which neural mechanisms can be studied with increased ecological validity and with the ability to address questions about natural behaviour and cognitive processes in humans engaged in dynamic real-world experiences.

Rationale and Design of the PARTNER Trial: Partnered Rhythmic Rehabilitation for Enhanced Motor-Cognition in Prodromal Alzheimer's Disease

BACKGROUND

Functional decline in Alzheimer's disease (AD) is impacted by impaired ability to integrate and modulate complex cognitive and motor abilities, commonly known as motor-cognitive integration. Impaired motor-cognitive integration occurs in the early stages of AD, prodromal AD (pAD), and may precede other symptoms. Combined motor and cognitive training have been recommended for people with pAD and need to be better researched. Our data suggest that partnered rhythmic rehabilitation (PRR) improves motor-cognitive integration in older adults with cognitive impairment. PRR is an ideal intervention to simultaneously target cardiovascular, social, and motor-cognitive domains important to AD.

OBJECTIVE/METHODS

We propose to conduct a 1-year Phase II, single-blind randomized controlled trial using PRR in 66 patients with pAD. Participants will be assigned to three months of biweekly sessions, followed by nine months of weekly sessions of PRR or group walking (WALK) with 1 : 1 allocation. Group walking in the control group will allow us to compare physical exercise alone versus the added benefit of the cognitively engaging elements of PRR.

RESULTS/CONCLUSION

Using an intent-to-treat approach, this innovative pilot study will 1) Determine acceptability, safety, tolerability, and satisfaction with PRR; 2) Compare efficacy of PRR versus WALK for improving motor-cognitive integration and identify the most sensitive endpoint for a Phase III trial from a set of motor-cognitive, volumetric MRI, and cognitive measures. The study will additionally explore potential neural, vascular, and inflammatory mechanisms by which PRR affects pAD to derive effect size of these intermediary measures and aid us in estimating sample size for a future trial.

Time to move: Brain dynamics underlying natural action and cognition

Advances in Mobile Brain/Body Imaging (MoBI) technology allows for real-time measurements of human brain dynamics during every day, natural, real-life situations. This special issue Time to Move brings together a collection of experimental papers, targeted reviews and opinion articles that lay out the latest MoBI findings. A wide range of topics across different fields are covered including art, athletics, virtual reality, and mobility. What unites these diverse topics is the common goal to enhance and restore human abilities by reaching a better understanding on how cognition is implemented by the brain-body relationship. The breadth and novelty of paradigms and findings reported here positions MoBI as a new frontier in the field of human cognitive neuroscience.

Correcting whole-body motion capture data using rigid body transformation

Using motion capture data as a part of mobile brain-body imaging (MoBI) recording has been increasing. With minimal linear algebra background, this paper explains how the rigid body transformation can be a useful preprocessing step for denoising and missing marker recovery. Such a transformation can provide insight and necessary-and-sufficient solutions requiring no assumption other than the minimum number of markers present. First, a simulation test using the empirical datasets from the AudioMaze project published from this journal's same volume demonstrates theoretical accuracy. The simulation results show that the rigid-body method perfectly recovers missing markers on a rigid body if a minimum of three marker positions is available. Second, the same transformation is applied to the empirical dataset. Before preprocessing, the raw data show that 15-80% of data frames had all markers present for rigid-body defined body parts. After using the rigid-body correction, most body parts recovered full markers in 90-95% of the data frames. The result also suggests the necessity for performing across-trial corrections for within-participant (42% missing detected in one of the body parts) and across-participants (11% missing). The discussion section introduces a solution and a performance summary for non-rigid-body marker correction using a neural network. Data support that the rigid body transformation is an intuitive and powerful correction method necessary for preprocessing motion capture data for neurocognitive experiments. The supporting information section contains a URL link to Matlab code and example data.