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

The Role of Enhanced Cognition to Counteract Detrimental Effects of Prolonged Bed Rest: Current Evidence and Perspectives

Prolonged periods of physical inactivity or bed rest can lead to a significant decline of functional and cognitive functions. Different kinds of countermeasures (e.g., centrifugation, nutritional, and aerobic interventions) have been developed to attempt to mitigate negative effects related to bed rest confinement. The aim of this report is to provide an overview of the current evidence related to the effectiveness of computerized cognitive training (CCT) intervention during a period of complete physical inactivity in older adults. CCT, using a virtual maze navigation task, appears to be effective and has long-lasting benefits (up to 1.5 years after the study). Moreover, enhanced cognition (executive control) reduces decline in the ability to perform complex motor-cognitive dual-tasks after prolonged period of bed rest. It has been demonstrated that CCT administration in older adults also prevents bed rest stress-related physiological changes [these groups showed minimal changes in vascular function and an unchanged level of brain-derived neurotrophic factor (BDNF)] while control subjects showed decreased peripheral vascularization and increased plasma level of the neurotrophin BDNF during a 14-day bed rest. In addition, the effects of CCT are evident also from the brain electrocortical findings: CCT group revealed a decreased power in lower delta and theta bands while significant increases in the same EEG spectral bands power were found in control subjects. If we consider an increase of power in delta band as a marker of cortical aging, then the lack of shift of EEG power to lower band indicates a preventive role of CCT on the cortical level during physiological deconditioning induced by 2-week bed rest immobilization. However, replication on a larger sample is required to confirm the observed findings. Applications derived from these findings could be appropriate for implementation of hospital treatment for bed ridden patients as well as for fall prevention programs

Environmental enrichment through virtual reality as multisensory stimulation to mitigate the negative effects of prolonged bed rest

Prolonged bed rest causes a multitude of deleterious physiological changes in the human body that require interventions even during immobilization to prevent or minimize these negative effects. In addition to other interventions such as physical and nutritional therapy, non-physical interventions such as cognitive training, motor imagery, and action observation have demonstrated efficacy in mitigating or improving not only cognitive but also motor outcomes in bedridden patients. Recent technological advances have opened new opportunities to implement such non-physical interventions in semi- or fully-immersive environments to enable the development of bed rest countermeasures. Extended Reality (XR), which covers augmented reality (AR), mixed reality (MR), and virtual reality (VR), can enhance the training process by further engaging the kinesthetic, visual, and auditory senses. XR-based enriched environments offer a promising research avenue to investigate the effects of multisensory stimulation on motor rehabilitation and to counteract dysfunctional brain mechanisms that occur during prolonged bed rest. This review discussed the use of enriched environment applications in bedridden patients as a promising tool to improve patient rehabilitation outcomes and suggested their integration into existing treatment protocols to improve patient care. Finally, the neurobiological mechanisms associated with the positive cognitive and motor effects of an enriched environment are highlighted

Motor imagery during action observation of locomotor tasks improves rehabilitation outcome in older adults after total hip arthroplasty

This study aimed at determining whether the combination of action observation and motor imagery (AO + MI) of locomotor tasks could positively affect rehabilitation outcome after hip replacement surgery. Of initially 405 screened participants, 21 were randomly split into intervention group (N=10; mean age = 64 y; AO + MI of locomotor tasks: 30 min/day in the hospital, then 3×/week in their homes for two months) and control group (N=11, mean age = 63 y, active controls). The functional outcomes (Timed Up and Go, TUG; Four Step Square Test, FSST; and single- and dual-task gait and postural control) were measured before (PRE) and 2 months after surgery (POST). Significant interactions indicated better rehabilitation outcome for the intervention group as compared to the control group: at POST, the intervention group revealed faster TUG (p=0.042), FSST (p=0.004), and dual-task fast-paced gait speed (p=0.022), reduced swing-time variability (p=0.005), and enhanced cognitive performance during dual tasks while walking or balancing (p<0.05). In contrast, no changes were observed for body sway parameters (p≥0.229). These results demonstrate that AO + MI is efficient to improve motor-cognitive performance after hip surgery. Moreover, only parameters associated with locomotor activities improved whereas balance skills that were not part of the AO + MI intervention were not affected, demonstrating the specificity of training intervention. Overall, utilizing AO + MI during rehabilitation is advised, especially when physical practice is limited

Removal of movement-induced EEG artifacts: current state of the art and guidelines

Objective: Electroencephalography (EEG) is a non-invasive technique used to record cortical neurons’ electrical activity using electrodes placed on the scalp. It has become a promising avenue for research beyond state-of-the-art EEG research that is conducted under static conditions. EEG signals are always contaminated by artifacts and other physiological signals. Artifact contamination increases with the intensity of movement. Approach: In the last decade (since 2010), researchers have started to implement EEG measurements in dynamic setups to increase the overall ecological validity of the studies. Many different methods are used to remove non-brain activity from the EEG signal, and there are no clear guidelines on which method should be used in dynamic setups and for specific movement intensities. Main results: Currently, the most common methods for removing artifacts in movement studies are methods based on independent component analysis. However, the choice of method for artifact removal depends on the type and intensity of movement, which affects the characteristics of the artifacts and the EEG parameters of interest. When dealing with EEG under non-static conditions, special care must be taken already in the designing period of an experiment. Software and hardware solutions must be combined to achieve sufficient removal of unwanted signals from EEG measurements. Significance: We have provided recommendations for the use of each method depending on the intensity of the movement and highlighted the advantages and disadvantages of the methods. However, due to the current gap in the literature, further development and evaluation of methods for artifact removal in EEG data during locomotion is needed.EC/H2020/952401/EU/TWINning the BRAIN with machine learning for neuro-muscular efficiency/TwinBrai

Review of the therapeutic neurofeedback method using electroencephalography: EEG Neurofeedback

Electroencephalographic neurofeedback (EEG-NFB) represents a broadly used method that involves a real-time EEG signal measurement, immediate data processing with the extraction of the parameter(s) of interest, and feedback to the individual in a real-time. Using such a feedback loop, the individual may gain better control over the neurophysiological parameters, by inducing changes in brain functioning and, consequently, behavior. It is used as a complementary treatment for a variety of neuropsychological disorders and improvement of cognitive capabilities, creativity or relaxation in healthy subjects. In this review, various types of EEG-NFB training are described, including training of slow cortical potentials (SCPs) and frequency and coherence training, with their main results and potential limitations. Furthermore, some general concerns about EEG-NFB methodology are presented, which still need to be addressed by the NFB community. Due to the heterogeneity of research designs in EEG-NFB protocols, clear conclusions on the effectiveness of this method are difficult to draw. Despite that, there seems to be a well-defined path for the EEG-NFB research in the future, opening up possibilities for improvement

Aging effects on prefrontal cortex oxygenation in a posture-cognition dual-task: an fNIRS pilot study

The aging process alters upright posture and locomotion control from an automatically processed to a more cortically controlled one. The present study investigated a postural-cognitive dual-task paradigm in young and older adults using functional Near- Infrared Spectroscopy (fNIRS).Methods: Twenty healthy participants (10 older adults 72 ± 3 y, 10 young adults 23 ± 3 y) performed a cognitive (serial subtractions) and a postural task (tandem stance) as single-tasks (ST) and concurrently as a dual-task (DT) while the oxygenation levels of the dorsolateral prefrontal cortex (DLPFC) were measured.Results: In the cognitive task, young adults performed better than older adults in both conditions (ST and DT) and could further increase the number of correct answers from ST to DT (all ps ≤ 0.027) while no change was found for older adults. No significant effects were found for the postural performance. Cerebral oxygenation values (O2Hb) increased significantly from baseline to the postural ST (p = 0.033), and from baseline to the DT (p = 0.031) whereas no changes were found in deoxygenated hemoglobin (HHb). Finally, the perceived exertion differed between all conditions (p ≤  0.003) except for the postural ST and the DT (p = 0.204).Conclusions: There was a general lack of age-related changes except the better cognitive performance under motor-cognitive conditions in young compared to older adults. However, the current results point out that DLPFC is influenced more strongly by postural than cognitive load. Future studies should assess the different modalities of cognitive as well as postural load

Relationship between exercise-induced oxidative stress changes and parasympathetic activity in chronic fatigue syndrome : an observational study in patients and healthy subjects

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A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics

Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram (EEG) signals to improve the control of active prostheses with brain-computer interfaces (BCI). Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our knowledge of the neural patterns associated with lower limb movement. Therefore, the main objective of this study is to show the feasibility of decoding lower limb movements from EEG data recordings. The second aim is to investigate whether well-known neuroplastic adaptations in individuals with an amputation have an influence on decoding performance. To address this, we collected data from multiple individuals with lower limb amputation and a matched able-bodied control group. Using these data, we trained and evaluated common BCI methods that have already been proven effective for upper limb BCI. With an average test decoding accuracy of 84% for both groups, our results show that it is possible to discriminate different lower extremity movements using EEG data with good accuracy. There are no significant differences (p = 0.99) in the decoding performance of these movements between healthy subjects and subjects with lower extremity amputation. These results show the feasibility of using BCI for lower limb prosthesis control and indicate that decoding performance is not influenced by neuroplasticity-induced differences between the two groups

Combining physical and virtual worlds for motor-cognitive training interventions: Position paper with guidelines on technology classification in movement-related research

Efficient movements require intact motor and cognitive function. There is a growing literature on motor-cognitive interventions to improve the overall quality of life of healthy or diseased older people. For such interventions, novel technological advances are crucial not only in terms of motivation but also to improve the user experience in a multi-stimuli world, usually offered as a mixture of real and virtual environments. This article provides a classification system for movement-related research dealing with motor-cognitive interventions performed in different extents of a virtual environment. The classification is divided into three categories: (a) type of digital device with the associated degree of immersiveness provided; (b) presence or absence of a human-computer interaction; and (c) activity engagement during training, defined by activity &gt;1.5 Metabolic Equivalent of task. Since virtual reality (VR) often categorizes different technologies under the same term, we propose a taxonomy of digital devices ranging from computer monitors and projectors to head-mounted VR technology. All immersive technologies that have developed rapidly in recent years are grouped under the umbrella term Extended Reality (XR). These include augmented reality (AR), mixed reality (MR), and VR, as well as all technologies that have yet to be developed. This technology has potential not only for gaming and entertainment, but also for research, motor-cognitive training programs, rehabilitation, telemedicine, etc. This position paper provides definitions, recommendations, and guidelines for future movement-related interventions based on digital devices, human-computer interactions, and physical engagement to use terms more consistently and contribute to a clearer understanding of their implications