Abstract:BackgroundRobotic technologies to measure human behavior are emerging as a new approach to assess brain function. Recently, we developed a robot-based postural Load Task to assess corrective responses to mechanical disturbances to the arm and found impairments in many participants with stroke compared to a healthy cohort (Bourke et al, J NeuroEngineering Rehabil 12: 7, 2015). However, a striking feature was the large range and skewed distribution of healthy performance. This likely reflects the use of differen… Show more
“…Key to our design is its naturalism—motivated by our previous work in the jaw (Johansson, Pruszynski, Edin, & Westberg, ; Johansson, Westburg, & Edin, )—where the participants are in active control of the force increase needed to trigger the sudden and unpredictable decrease in resistive force. This is in contrast to previous studies along these lines where unloading was triggered by an external source (Angel, Eppler, & Iannone, ; Asatryan & Feldman, ; Dufossé, Hugon, & Massion, ; Grago, Houk, & Hasan, ; Latash & Gottlieb, ; Lowrey, Bourke, Bagg, Dukelow, & Scott, ; Paulignan, Dufossé, Hugon, & Massion, ). For example, in Angel and colleagues’ seminal study of reactions to unloading (1965) the participant counteracts a preset force level while controlling their position and passively waits for the unpredictable unloading event to be triggered by the experimenter cutting a wire.…”
Section: Introductioncontrasting
confidence: 89%
“…Unloading paradigms have been used by many previous groups to study a variety of behavioral phenomena. Some studies have used unloading events triggered by an experimenter to evoke postural perturbations (Angel et al., ; Lowrey et al., ) or investigate muscle characteristics under do‐not‐intervene instructions (Archambault, Mihaltchev, Levin, & Feldman, ). Other studies have used self‐triggered unloading perturbations to investigate preparation for expected perturbations to the upper limb (Johannson & Westling, ; Kennedy & Schwartz, ; Lum et al., ) or whole body (Aruin & Latash, ), or to trigger perturbations with unknown directions (Piscitelli, Falaki, Solnik, & Latash, ).…”
We often perform actions where we must break through some resistive force, but want to remain in control during this unpredictable transition; for example, when an object we are pushing on transitions from static to dynamic friction and begins to move. We designed a laboratory task to replicate this situation in which participants actively pushed against a robotic manipulandum until they exceeded an unpredictable threshold, at which point the manipulandum moved freely. Human participants were instructed to either stop the movement of the handle following this unloading perturbation, or to continue pushing. We found that participants were able to modulate their reflexes in response to this unpredictable and self‐triggered unloading perturbation according to the instruction they were following, and that this reflex modulation could not be explained by pre‐perturbation muscle state. However, in a second task, where participants reactively produced force during the pre‐unloading phase in response to the robotic manipulandum to maintain a set hand position, they were unable to modulate their reflexes in the same task‐dependent way. This occurred even though the forces they produced were matched to the first task and they had more time to prepare for the unloading event. We suggest this disparity occurs because of different neural circuits involved in posture and movement, meaning that participants in the first task did not require additional time to switch from postural to movement control.
“…Key to our design is its naturalism—motivated by our previous work in the jaw (Johansson, Pruszynski, Edin, & Westberg, ; Johansson, Westburg, & Edin, )—where the participants are in active control of the force increase needed to trigger the sudden and unpredictable decrease in resistive force. This is in contrast to previous studies along these lines where unloading was triggered by an external source (Angel, Eppler, & Iannone, ; Asatryan & Feldman, ; Dufossé, Hugon, & Massion, ; Grago, Houk, & Hasan, ; Latash & Gottlieb, ; Lowrey, Bourke, Bagg, Dukelow, & Scott, ; Paulignan, Dufossé, Hugon, & Massion, ). For example, in Angel and colleagues’ seminal study of reactions to unloading (1965) the participant counteracts a preset force level while controlling their position and passively waits for the unpredictable unloading event to be triggered by the experimenter cutting a wire.…”
Section: Introductioncontrasting
confidence: 89%
“…Unloading paradigms have been used by many previous groups to study a variety of behavioral phenomena. Some studies have used unloading events triggered by an experimenter to evoke postural perturbations (Angel et al., ; Lowrey et al., ) or investigate muscle characteristics under do‐not‐intervene instructions (Archambault, Mihaltchev, Levin, & Feldman, ). Other studies have used self‐triggered unloading perturbations to investigate preparation for expected perturbations to the upper limb (Johannson & Westling, ; Kennedy & Schwartz, ; Lum et al., ) or whole body (Aruin & Latash, ), or to trigger perturbations with unknown directions (Piscitelli, Falaki, Solnik, & Latash, ).…”
We often perform actions where we must break through some resistive force, but want to remain in control during this unpredictable transition; for example, when an object we are pushing on transitions from static to dynamic friction and begins to move. We designed a laboratory task to replicate this situation in which participants actively pushed against a robotic manipulandum until they exceeded an unpredictable threshold, at which point the manipulandum moved freely. Human participants were instructed to either stop the movement of the handle following this unloading perturbation, or to continue pushing. We found that participants were able to modulate their reflexes in response to this unpredictable and self‐triggered unloading perturbation according to the instruction they were following, and that this reflex modulation could not be explained by pre‐perturbation muscle state. However, in a second task, where participants reactively produced force during the pre‐unloading phase in response to the robotic manipulandum to maintain a set hand position, they were unable to modulate their reflexes in the same task‐dependent way. This occurred even though the forces they produced were matched to the first task and they had more time to prepare for the unloading event. We suggest this disparity occurs because of different neural circuits involved in posture and movement, meaning that participants in the first task did not require additional time to switch from postural to movement control.
“…Key to our design is its naturalism -motivated by our previous work in the jaw (Johansson 2014a,b) -where the participants are in active control of the force increase needed to trigger the sudden and unpredictable decrease in resistive force. This is contrast to previous studies along these lines where unloading was triggered by an external source (Angel et al 1965, Asatryan & Feldman 1965, Crago et al 1976, Dufossé et al 1985, Paulignan et al 1989, Latash & Gottlieb 1991, Lowrey et al 2019. For example, in Angel and colleagues' seminal study of reactions to unloading (1965) the participant counteracts a preset force level while controlling their position and passively waits for the unpredictable unloading event to be triggered by the experimenter cutting a wire.…”
Section: Introductionmentioning
confidence: 77%
“…Unloading paradigms have been used by many previous groups to study a variety of behavioral phenomena. Some studies have used unloading events triggered by an experimenter to evoke postural perturbations (Angel et al 1965, Lowrey et al 2019 or investigate muscle characteristics under do-not-intervene instructions (Archambault et al 2005). Other studies have used self-triggered unloading perturbations to investigate preparation for expected perturbations to the upper limb (Lum et al 1992, Johansson & Westling 1988, Kennedy & Schwartz 2018 or whole body (Aruin & Latash 1995), or to trigger perturbations with unknown directions (Piscitelli et al 2017).…”
We often perform actions where we must break through some resistive force, but want to remain in control during this unpredictable transition; for example, when an object we are pushing on transitions from static to dynamic friction and begins to move. We designed a laboratory task to replicate this situation in which participants actively pushed against a robotic manipulandum until they exceeded an unpredictable threshold, at which point the manipulandum moved freely. Human participants were instructed to either stop the movement of the handle following this unloading perturbation, or to continue pushing. We found that participants were able to modulate their reflexes in response to this unpredictable and self‐triggered unloading perturbation according to the instruction they were following, and that this reflex modulation could not be explained by pre‐perturbation muscle state. However, in a second task, where participants reactively produced force during the pre‐unloading phase in response to the robotic manipulandum to maintain a set hand position, they were unable to modulate their reflexes in the same task‐dependent way. This occurred even though the forces they produced were matched to the first task and they had more time to prepare for the unloading event. We suggest this disparity occurs because of different neural circuits involved in posture and movement, meaning that participants in the first task did not require additional time to switch from postural to movement control.
“…The potential caveat with this approach is the assumption that proprioceptive and motor function of the other arm is not impaired. However, ipsilesional impairments can be observed in some individuals (~30%) following stroke [30][31][32][33][34]. Further, bilateral impairments are common in other diseases such as ALS [35][36][37].…”
Background: Robotic technologies for neurological assessment provide sensitive, objective measures of behavioural impairments associated with injuries or disease such as stroke. Previous robotic tasks to assess proprioception typically involve single limbs or in some cases both limbs. The challenge with these approaches is that they often rely on intact motor function and/or working memory to remember/reproduce limb position, both of which can be impaired following stroke. Here, we examine the feasibility of a single-arm Movement Discrimination Threshold (MDT) task to assess proprioception by quantifying thresholds for sensing passive limb movement without vision. We use a staircase method to adjust movement magnitude based on subject performance throughout the task in order to reduce assessment time. We compare MDT task performance to our previously-designed Arm Position Matching (APM) task. Critically, we determine test-retest reliability of each task in the same population of healthy controls. Method: Healthy participants (N = 21, age = 18-22 years) completed both tasks in the End-Point Kinarm robot. In the MDT task the robot moved the dominant arm left or right and participants indicated the direction moved. Movement displacement was systematically adjusted (decreased after correct answers, increased after incorrect) until the Discrimination Threshold was found. In the APM task, the robot moved the dominant arm and participants "mirror-matched" with the non-dominant arm. Results: Discrimination Threshold for direction of arm displacement in the MDT task ranged from 0.1-1.3 cm. Displacement Variability ranged from 0.11-0.71 cm. Test-retest reliability of Discrimination Threshold based on ICC confidence intervals was moderate to excellent (range, ICC = 0.78 [0.52-0.90]). Interestingly, ICC values for Discrimination Threshold increased to 0.90 [0.77-0.96] (good to excellent) when the number of trials was reduced to the first 50. Most APM parameters had ICC's above 0.80, (range, ICC = [0.86-0.88]) with the exception of variability (ICC = 0.30). Importantly, no parameters were significantly correlated across tasks as Spearman rank correlations across parameter-pairings ranged from − 0.27 to 0.30.
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