Is heart rate variability affected by distinct motor imagery strategies?

Brain-Computer interface control of stepping from invasive electrocorticography upper-limb motor imagery in a patient with quadriplegia

Introduction: Most spinal cord injuries (SCI) result in lower extremities paralysis, thus diminishing ambulation. Using brain-computer interfaces (BCI), patients may regain leg control using neural signals that actuate assistive devices. Here, we present a case of a subject with cervical SCI with an implanted electrocorticography (ECoG) device and determined whether the system is capable of motor-imagery-initiated walking in an assistive ambulator. Methods: A 24-year-old male subject with cervical SCI (C5 ASIA A) was implanted before the study with an ECoG sensing device over the sensorimotor hand region of the brain. The subject used motor-imagery (MI) to train decoders to classify sensorimotor rhythms. Fifteen sessions of closed-loop trials followed in which the subject ambulated for one hour on a robotic-assisted weight-supported treadmill one to three times per week. We evaluated the stability of the best-performing decoder over time to initiate walking on the treadmill by decoding upper-limb (UL) MI. Results: An online bagged trees classifier performed best with an accuracy of 84.15% averaged across 9 weeks. Decoder accuracy remained stable following throughout closed-loop data collection. Discussion: These results demonstrate that decoding UL MI is a feasible control signal for use in lower-limb motor control. Invasive BCI systems designed for upper-extremity motor control can be extended for controlling systems beyond upper extremity control alone. Importantly, the decoders used were able to use the invasive signal over several weeks to accurately classify MI from the invasive signal. More work is needed to determine the long-term consequence between UL MI and the resulting lower-limb control.

Learning neural decoders without labels using multiple data streams

OBJECTIVE

Recent advances in neural decoding have accelerated the development of brain-computer interfaces aimed at assisting users with everyday tasks such as speaking, walking, and manipulating objects. However, current approaches for training neural decoders commonly require large quantities of labeled data, which can be laborious or infeasible to obtain in real-world settings. Alternatively, self-supervised models that share self-generated pseudo-labels between two data streams have shown exceptional performance on unlabeled audio and video data, but it remains unclear how well they extend to neural decoding.

APPROACH

We learn neural decoders without labels by leveraging multiple simultaneously recorded data streams, including neural, kinematic, and physiological signals. Specifically, we apply cross-modal, self-supervised deep clustering to train decoders that can classify movements from brain recordings. After training, we then isolate the decoders for each input data stream and compare the accuracy of decoders trained using cross-modal deep clustering against supervised and unimodal, self-supervised models.

MAIN RESULTS

We find that sharing pseudo-labels between two data streams during training substantially increases decoding performance compared to unimodal, self-supervised models, with accuracies approaching those of supervised decoders trained on labeled data. Next, we extend cross-modal decoder training to three or more modalities, achieving state-of-the-art neural decoding accuracy that matches or slightly exceeds the performance of supervised models. Significance: We demonstrate that cross-modal, self-supervised decoding can be applied to train neural decoders when few or no labels are available and extend the cross-modal framework to share information among three or more data streams, further improving self-supervised training.

Effectiveness of Therapies Based on Mirror Neuron System to Treat Gait in Patients with Parkinson’s Disease—A Systematic Review

Parkinson’s disease (PD) is a neurodegenerative disease that alters gait patterns from early stages. The visuo-motor training strategies such as action observation (AO) and motor imagery (MI) that are based on the activity of the mirror neuron system (MNS) facilitate motor re-learning. The main purpose of this systematic review was to analyze the current scientific evidence about the effectiveness of MNS’s treatments (AO and MI) to treat gait in patients with PD. Searches were completed from the databases PubMed, Web of Science, and PEDro between November and December 2021. The following keywords were used: “Parkinson disease”, “mirror neurons”, “gait”, “action observation”, and “motor imagery”. Randomized control trials of the last 5 years written in English or Spanish were included. Two independent reviewers screened the articles and applied the eligibility criteria, and a third reviewer assisted in this process. A total of six articles were included for final revision. The risk of bias was assessed with the PEDro Scale. The effects of AO and MI using different outcome measures were referenced in terms of disease severity, quality of life, balance, and gait. Training with AO and MI are effective in improving disease severity, quality of life, balance, and gait in patients with PD.

Integrating mental imagery and fascial tissue: A conceptualization for research into movement and cognition

Mental imagery (MI) research has mainly focused to date on mechanisms of effect and performance gains associated with muscle and neural tissues. MI's potential to affect fascia has rarely been considered. This paper conceptualizes ways in which MI might mutually interact with fascial tissue to support performance and cognitive functions. Such ways acknowledge, among others, MI's positive effect on proprioception, body schema, and pain. Drawing on cellular, physiological, and functional similarities and associations between muscle and fascial tissues, we propose that MI has the potential to affect and be affected by fascial tissue. We suggest that fascia-targeted MI (fascial mental imagery; FMI) can therefore be a useful approach for scientific as well as clinical purposes. We use the example of fascial dynamic neuro-cognitive imagery (FDNI) as a codified FMI method available for scientific and therapeutic explorations into rehabilitation and prevention of fascia-related disabling conditions.

Combining motor imagery with action observation training does not lead to a greater autonomic nervous system response than motor imagery alone during simple and functional movements: a randomized controlled trial

Both motor imagery (MI) and action observation (AO) trigger the activation of the neurocognitive mechanisms that underlie the planning and execution of voluntary movements in a manner that resembles how the action is performed in a real way. The main objective of the present study was to compare the autonomic nervous system (ANS) response in an isolated MI group compared to a combined MI + AO group. The mental tasks were based on two simple movements that are recorded in the revised movement imagery questionnaire in third-person perspective. The secondary objective of the study was to test if there was any relationship between the ANS variables and the ability to generate mental motor imagery, the mental chronometry and the level of physical activity. The main outcomes that were measured were heart rate, respiratory rate and electrodermal activity. A Biopac MP150 system, a measurement device of autonomic changes, was used for the quantification and evaluation of autonomic variables. Forty five asymptomatic subjects were selected and randomized in three groups: isolated MI, MI + AO and control group (CG). In regards to the activation of the sympathetic nervous system (SNS), no differences were observed between MI and MI + AO groups (p > .05), although some differences were found between both groups when compared to the CG (p < .05). Additionally, even though no associations were reported between the ANS variables and the ability to generate mental motor imagery, moderate-strong positive associations were found in mental chronometry and the level of physical activity. Our results suggest that MI and MI + AO, lead to an activation of the SNS, although there are no significant differences between the two groups. Based on results obtained, we suggest that tasks of low complexity, providing a visual input through the AO does not facilitates their subsequent motor imagination. A higher level of physical activity as well as a longer time to perform mental task, seem to be associated with a greater increase in the ANS response.