Investigating the task’s role in learning through motor imagery
Motor imagery can help us learn new and improve existing motor skills without physically performing the movement. However, much of the current research investigating learning through motor imagery use experimental task designs where participants are asked to imagine tapping their finger in a specific sequence as a response to a cue, usually in the form of an auditory cue. When compared to imagining a basketball-free throw, or imagining writing one’s name, such task designs don’t seem as motoric. The goal of this study is to determine how the nature of an imagery task affects learning through motor imagery. The results will help us better understand which parts of the brain are involved in motor learning, the mechanism involved, and spur novel avenues for future research.
Project member: Hudson Barr
fNIRS prototype vs. gold standard comparison study
We are testing the performance of a prototype wireless functional near-infrared spectroscopy (fNIRS) device developed by Axem Neurotechnology (startup founded by LBRF lab members Chris Friesen and Tony Ingram) against an established fNIRS research system. Specifically, we’ll be looking to determine how these devices compare in their ability to measure the cerebral hemodynamic response to simple upper- and lower-extremity movements respectively.
Project members: Chris Friesen, Tony Ingram
Neurofeedback-based stroke rehabilitation at home
We are testing the feasibility of having stroke survivors perform upper-extremity rehabilitation at home using a rehabilitation system comprised of a tablet and headband that measures brain activity using functional near-infrared spectroscopy (fNIRS). This study will examine the feasibility of having stroke survivors perform neurofeedback-based rehabilitation independently in the comfort of their own home. The study will also examine the ability of a prototype fNIRS device developed by Axem Neurotechnology (startup founded by LBRF lab members Chris Friesen and Tony Ingram) to detect known biomarkers of post-stroke motor recovery based on the data collected during rehabilitation sessions.
Project members: Chris Friesen, Tony Ingram
Learning-model action observation: An investigation in long-term motor learning outcomes
Motor learning can take place using a number of strategies other than physical practice, either independently or in conjunction with one another. One such strategy is action observation (AO), which involves observing a model perform a motor skill. In typical AO protocols, the model executes the skill without committing any errors, thereby offering the viewer a perfect demonstration of the skill and task outcome. However, for individuals in the early phases of learning a novel motor skill, this type of skilled performance may not be ideal given the absence of errors in the model’s performance. This is due to the critical role that error correction and sensory feedback have in driving early learning gains in physical practice and the evidence that this role is similarly conserved in AO. The goal of this project is to determine if AO of a learning model (who commits and corrects errors, improving over time) results in improved performance outcomes on a dart-throwing task when compared to AO of a skilled model for early-phase learners completing a 6-week training program. The results of this study can be used to optimise the use of AO in performance, occupational, and rehabilitation settings, and address questions surrounding the conservation of error-based learning mechanisms across different motor learning strategies.
Project member: David Bowman
Investigating the nature of motor imagery-based motor learning
Often in learning a new skill or activity that involves movement, we are told to imagine ourselves doing it. Motor imagery is just that, the act of creating the mental representation of a motor action. In comparison, motor imagery is distinctly different from motor execution which is when one physically executes a movement. Motor imagery is thought to use similar pathways as those utilized by motor execution which would suggest that motor imagery can help with learning a motor skill. In motor imagery learning is thought to occur by building upon the motor plan that is usually developed several stages before a movement is executed. However, the learning process in motor imagery is still not fully understood. This study will investigate the nature of motor imagery based learning by looking about what component of a motor task, motor plan or movement execution, is learned through motor imagery. (2022)
Project members: Ernest Ng, Jack Solomon
Learning without doing. Error detection and correction in motor imagery-based learning
Motor imagery (MI) can help us learn new or improve motor skills without actually moving. In typical motor learning, information for the outcome of a movement (i.e. we reach for a coffee cup and knock it off the table) is compared against our plan for the given movement (i.e. how we decided to move our arm to grab the coffee cup). When this information is compared, we can adjust our motor plan to make our movement more accurate. However, in MI, we don’t move and therefore there is no way to compare our motor plan against the outcome of a movement. The goal of this study is to determine how we learn in MI by making it harder to use a brain area known to be involved in MI. The results of this study will help us better understand which parts of the brain are involved in error detection and correction MI based motor learning.
Project member: Jack Solomon
Seeing is not always believing. Investigating the content of motor imagery-based learning using illusions
Motor imagery (MI) allows us to learn new or improve upon existing motor skills, however the content of what is being learnt is unclear. Visual illusions have been used in the motor execution domain to dissociate visual control of movement into two streams, vision for perception and vision for action. In this context, participant’s perception reflects the illusion but within a few trials, their motor performance “solves” the illusion without participants being aware of this change. However, these illusions are solved in different ways. Some require kinematic feedback from the movement whereas others only require perceptual feedback about the movement performed. The goal of this project is to employ two illusions, one that is solved perceptually and one that requires kinematic feedback to solve, to characterize changes in performance on these illusions after MI training. From the results of this study we can make inferences about what type of feedback is being simulated during MI training (perceptual vs kinematic).
Project members: Jack Solomon, Wenyi Lyu
Investigating the effect of manipulating effector load on corticospinal excitability during motor imagery and motor execution of a grip force task
Performing motor imagery (MI), the mental rehearsal of a motor task, leads to an increase in cortical excitability and facilitates synaptic plasticity, which is required for motor skill learning to occur. As such, motor imagery is used as an adjunct to physical practice in motor skill training and rehabilitation. The Functional Equivalence and Cognitive Motor models are competing theories that explain the neural mechanisms underlying MI. Evidence about how cortical excitability changes during MI as a function of effort differs between the models; some research has found that cortical excitability during imagined tasks scales with effort in a linear fashion (similar to physical practice), supporting the functional equivalence model, while other studies have shown cortical excitability mirrors that of physical practice only during low effort tasks, but differs as the effort required to complete a task increases, supporting the cognitive motor model. This study will investigate how cortical excitability scales across varying levels of effort during imagined vs. executed tasks. We will also compare how this scaling differs when the imagined task is performed with high fidelity (familiar tasks) vs. low fidelity (unfamiliar tasks) imagery. The aim of this study is to provide an explanation for the divide in the current literature on how brain activity scales at varying efforts of MI tasks compared to motor execution, thereby generating further evidence to support the theoretical underpinnings of why MI is effective for motor skill acquisition.
Project members: Devan Pancura, Jack Solomon
Motor Imagery + Action observation: Is the sum greater than the parts?
Although action observation and mental imagery have typically been viewed as independent learning techniques, research has found increased learning outcomes when action observation and motor imagery are used simultaneously. While behavioural studies have shown the combined use of action observation and motor imagery results in greater learning outcomes, the link between neurophysiological processes behind the enhanced performance outcomes previous studies have found is largely unknown. The objective of this research is to investigate the neurophysiological processes underlying the enhanced behavioural outcomes previous research has reported when motor imagery and action observation are used simultaneously. In employing both behavioural and neurophysiological measures the proposed work will expand the knowledge base related to the mechanisms of AO and MI for motor skill learning.
Project member: Theresa Gaughan
Can you feel the burn? Probing the functional role of a brain region during motor imagery
Motor imagery, known as the mental rehearsal of movement, can be used to improve motor skills without physically executing the movement. While it is widely believed that motor imagery and motor execution are fundamentally similar, recent studies suggest that motor imagery and execution may not be as similar as we initially believed. One way to probe the difference between the two different modalities is by teasing apart the functional role of a specific brain area during the two different modalities. This study will look at the role of a particular brain area in using motor imagery to practice and learn a new skill. The results of this study help us understand similarities and differences between motor imagery and execution.
Project member: JungWoo Lee