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Mean square of the specified angle torque as a function of the adaptive parameter for different stimulus conditions (the adaptive scheme's objective functions)
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Reweighting sensory information adaptively is considered critical for flexible postural control, but little is known of the time scale of the reweighting process. We analyzed the transient dynamics of sensory reweighting in a previously published nonlinear adaptive model of sensory integration in the human postural control system. The model's dynam...
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... situation is quite different upon the switch from the low amplitude condition to the high amplitude condition. The asymptotic value in the high amplitude condition is sub- stantially larger than in the low amplitude condition. When the switch occurs the adaptive parameter responds at a very rapid rate. However, the approach to the high amplitude equi- librium slows and the adaptive parameter fails to reach its asymptotic value by the time the 60 s in the high condition is complete. Upon the switch from the high condition to the low condition, the rate of adaptation is initially much slower than during the reverse switch. However, the rate increases as the adaptive parameter falls and the adaptive parameter crosses its low condition asymptotic value by the end of the 360 s trial. The temporal asymmetry can be understood by looking at the objective function -the mean square of the specified ankle torque -that the modeled nervous system seeks to min- imize by tweaking the adaptive parameter (see Fig. 4). We caution the reader that the objective function interpretation of this stochastic model is an approximation that only becomes accurate as the adaptation gain ε becomes small. Details of how the objective function is computed are presented in the appendices to Carver et al. (2005). The value of the objective function depends both on the current value of the adaptive parameter and on the current stimulus condition. Thus Fig. 4 shows three functions of the adaptive parameter -one for each stimulus condition (quiet stance, low amplitude, high amplitude). The scheme adjusts θ to minimize the objec- tive function corresponding to the current stimulus condi- tion. During quiet stance, this minimum lies on the square of Fig. 4; during the low amplitude condition this minimum lies on the asterisk, and during the high amplitude condition this minimum lies on the ...
Context 2
... situation is quite different upon the switch from the low amplitude condition to the high amplitude condition. The asymptotic value in the high amplitude condition is sub- stantially larger than in the low amplitude condition. When the switch occurs the adaptive parameter responds at a very rapid rate. However, the approach to the high amplitude equi- librium slows and the adaptive parameter fails to reach its asymptotic value by the time the 60 s in the high condition is complete. Upon the switch from the high condition to the low condition, the rate of adaptation is initially much slower than during the reverse switch. However, the rate increases as the adaptive parameter falls and the adaptive parameter crosses its low condition asymptotic value by the end of the 360 s trial. The temporal asymmetry can be understood by looking at the objective function -the mean square of the specified ankle torque -that the modeled nervous system seeks to min- imize by tweaking the adaptive parameter (see Fig. 4). We caution the reader that the objective function interpretation of this stochastic model is an approximation that only becomes accurate as the adaptation gain ε becomes small. Details of how the objective function is computed are presented in the appendices to Carver et al. (2005). The value of the objective function depends both on the current value of the adaptive parameter and on the current stimulus condition. Thus Fig. 4 shows three functions of the adaptive parameter -one for each stimulus condition (quiet stance, low amplitude, high amplitude). The scheme adjusts θ to minimize the objec- tive function corresponding to the current stimulus condi- tion. During quiet stance, this minimum lies on the square of Fig. 4; during the low amplitude condition this minimum lies on the asterisk, and during the high amplitude condition this minimum lies on the ...
Context 3
... situation is quite different upon the switch from the low amplitude condition to the high amplitude condition. The asymptotic value in the high amplitude condition is sub- stantially larger than in the low amplitude condition. When the switch occurs the adaptive parameter responds at a very rapid rate. However, the approach to the high amplitude equi- librium slows and the adaptive parameter fails to reach its asymptotic value by the time the 60 s in the high condition is complete. Upon the switch from the high condition to the low condition, the rate of adaptation is initially much slower than during the reverse switch. However, the rate increases as the adaptive parameter falls and the adaptive parameter crosses its low condition asymptotic value by the end of the 360 s trial. The temporal asymmetry can be understood by looking at the objective function -the mean square of the specified ankle torque -that the modeled nervous system seeks to min- imize by tweaking the adaptive parameter (see Fig. 4). We caution the reader that the objective function interpretation of this stochastic model is an approximation that only becomes accurate as the adaptation gain ε becomes small. Details of how the objective function is computed are presented in the appendices to Carver et al. (2005). The value of the objective function depends both on the current value of the adaptive parameter and on the current stimulus condition. Thus Fig. 4 shows three functions of the adaptive parameter -one for each stimulus condition (quiet stance, low amplitude, high amplitude). The scheme adjusts θ to minimize the objec- tive function corresponding to the current stimulus condi- tion. During quiet stance, this minimum lies on the square of Fig. 4; during the low amplitude condition this minimum lies on the asterisk, and during the high amplitude condition this minimum lies on the ...
Context 4
... trial begins with the adaptive parameter near its quiet stance asymptotic value (the square). At time 0 the low ampli- tude condition begins. The adaptive parameter changes con- tinuously because its rate of change is given by a stochastic differential equation (Eq. (7) in Appendix A). In contrast, the stimulus condition changes abruptly as specified by our experimental protocol. When the low amplitude condition begins the adaptive parameter initially remains near its quiet stance asymptotic value (≈ 0.1 cm s −1/2 ) but now the low amplitude curve applies. Thus, the horizontal position (rep- resenting θ ) on Fig. 4 initially remains roughly unchanged but the vertical position (representing the mean square of the specified ankle torque in the current condition) changes abruptly with the change in condition. At time 0, upon the switch to low amplitude stimulation, the point represent- ing the value of the objective function becomes the point above the square on the low amplitude (solid) curve. After the switch, the adaptive parameter begins to approach its low amplitude asymptotic value (the asterisk). By 120 s we can expect the adaptive parameter to be hovering near its low amplitude asymptotic value (the asterisk). In a similar man- ner, upon the switch to high amplitude stimulation, the point on Fig. 4 representing the current value of the objective func- tion becomes the point on the high amplitude (dashed) curve above the low amplitude asymptotic value (the asterisk). The slope of this curve is relatively large at this point. Because the adaptive scheme optimizes ankle torque, according to the gra- dient, descent rule adaptation happens at a rate proportional to this large slope -quickly. Thus, changing from low ampli- tude to high amplitude stimulation causes initially very rapid changes. Nevertheless, the rate of adaptation slows down as θ increases and after 60 s, at the end of the high ampli- tude condition, the system does not reach its high amplitude asymptotic value (the triangle) as evidenced by the trace of θ in Fig. 3. Instead, θ lies somewhere to the left of its high amplitude asymptotic value (the triangle). The slope of the low amplitude curve is relatively shallow in this region. Thus, upon the return to the low amplitude condition, the initial rate of return to the low amplitude asymptotic value (the asterisk) is slower than the previous initial rate of approach to the high amplitude asymptotic value (the triangle). From the differ- ential values of the slopes of the objective function in each condition arises the temporal asymmetry in the model. Figure 4 shows how the adaptive parameter changes as a function of stimulus condition. However, this figure does not make it directly apparent how gain changes as a function of stimulus condition. Nevertheless, gain is a function of the adaptive parameter and a simple change of variables makes the predicted changes in gain apparent (see Appendix B for details). Figure 5 plots the predicted approximate average rate of change of gain as a function of gain in the low ampli- tude condition (solid) and in the high amplitude condition (dashed). This plot depicts the vector fields that we call the averaged vector fields which are approximately accurate for small ε. The asymptotic values of gain in each condition are the values of gain at which the corresponding curves cross the horizontal axis (zero rate of change of gain). The direc- tion of crossing determines the stability of the equilibria. In this case, both equilibria are stable. As with the adaptive parameter, gain (the horizontal position on the axis of Fig. 5) changes continuously but the experimental condition (deter- mining which curve applies) changes abruptly. Part of the high amplitude curve is off the scale of the graph. Indeed, the high amplitude curve is about −0.7 below the low amplitude asymptotic value. Thus switching from low to high ampli- tude can cause rapid changes. This graph predicts that gain changes at an initial rate of about −0.7 s −1 upon a switch from low to high amplitude stimulation. However, the rate of change of gain is much more modest after a switch in the reverse direction: the rate is less than 0.02 s −1 . Close to the value of the high amplitude equilibrium the rate of change of gain in the low condition is even considerably lower. Note that the predicted high and low amplitude asymptotic gains match the simulations shown in Fig. ...
Context 5
... trial begins with the adaptive parameter near its quiet stance asymptotic value (the square). At time 0 the low ampli- tude condition begins. The adaptive parameter changes con- tinuously because its rate of change is given by a stochastic differential equation (Eq. (7) in Appendix A). In contrast, the stimulus condition changes abruptly as specified by our experimental protocol. When the low amplitude condition begins the adaptive parameter initially remains near its quiet stance asymptotic value (≈ 0.1 cm s −1/2 ) but now the low amplitude curve applies. Thus, the horizontal position (rep- resenting θ ) on Fig. 4 initially remains roughly unchanged but the vertical position (representing the mean square of the specified ankle torque in the current condition) changes abruptly with the change in condition. At time 0, upon the switch to low amplitude stimulation, the point represent- ing the value of the objective function becomes the point above the square on the low amplitude (solid) curve. After the switch, the adaptive parameter begins to approach its low amplitude asymptotic value (the asterisk). By 120 s we can expect the adaptive parameter to be hovering near its low amplitude asymptotic value (the asterisk). In a similar man- ner, upon the switch to high amplitude stimulation, the point on Fig. 4 representing the current value of the objective func- tion becomes the point on the high amplitude (dashed) curve above the low amplitude asymptotic value (the asterisk). The slope of this curve is relatively large at this point. Because the adaptive scheme optimizes ankle torque, according to the gra- dient, descent rule adaptation happens at a rate proportional to this large slope -quickly. Thus, changing from low ampli- tude to high amplitude stimulation causes initially very rapid changes. Nevertheless, the rate of adaptation slows down as θ increases and after 60 s, at the end of the high ampli- tude condition, the system does not reach its high amplitude asymptotic value (the triangle) as evidenced by the trace of θ in Fig. 3. Instead, θ lies somewhere to the left of its high amplitude asymptotic value (the triangle). The slope of the low amplitude curve is relatively shallow in this region. Thus, upon the return to the low amplitude condition, the initial rate of return to the low amplitude asymptotic value (the asterisk) is slower than the previous initial rate of approach to the high amplitude asymptotic value (the triangle). From the differ- ential values of the slopes of the objective function in each condition arises the temporal asymmetry in the model. Figure 4 shows how the adaptive parameter changes as a function of stimulus condition. However, this figure does not make it directly apparent how gain changes as a function of stimulus condition. Nevertheless, gain is a function of the adaptive parameter and a simple change of variables makes the predicted changes in gain apparent (see Appendix B for details). Figure 5 plots the predicted approximate average rate of change of gain as a function of gain in the low ampli- tude condition (solid) and in the high amplitude condition (dashed). This plot depicts the vector fields that we call the averaged vector fields which are approximately accurate for small ε. The asymptotic values of gain in each condition are the values of gain at which the corresponding curves cross the horizontal axis (zero rate of change of gain). The direc- tion of crossing determines the stability of the equilibria. In this case, both equilibria are stable. As with the adaptive parameter, gain (the horizontal position on the axis of Fig. 5) changes continuously but the experimental condition (deter- mining which curve applies) changes abruptly. Part of the high amplitude curve is off the scale of the graph. Indeed, the high amplitude curve is about −0.7 below the low amplitude asymptotic value. Thus switching from low to high ampli- tude can cause rapid changes. This graph predicts that gain changes at an initial rate of about −0.7 s −1 upon a switch from low to high amplitude stimulation. However, the rate of change of gain is much more modest after a switch in the reverse direction: the rate is less than 0.02 s −1 . Close to the value of the high amplitude equilibrium the rate of change of gain in the low condition is even considerably lower. Note that the predicted high and low amplitude asymptotic gains match the simulations shown in Fig. ...
Context 6
... trial begins with the adaptive parameter near its quiet stance asymptotic value (the square). At time 0 the low ampli- tude condition begins. The adaptive parameter changes con- tinuously because its rate of change is given by a stochastic differential equation (Eq. (7) in Appendix A). In contrast, the stimulus condition changes abruptly as specified by our experimental protocol. When the low amplitude condition begins the adaptive parameter initially remains near its quiet stance asymptotic value (≈ 0.1 cm s −1/2 ) but now the low amplitude curve applies. Thus, the horizontal position (rep- resenting θ ) on Fig. 4 initially remains roughly unchanged but the vertical position (representing the mean square of the specified ankle torque in the current condition) changes abruptly with the change in condition. At time 0, upon the switch to low amplitude stimulation, the point represent- ing the value of the objective function becomes the point above the square on the low amplitude (solid) curve. After the switch, the adaptive parameter begins to approach its low amplitude asymptotic value (the asterisk). By 120 s we can expect the adaptive parameter to be hovering near its low amplitude asymptotic value (the asterisk). In a similar man- ner, upon the switch to high amplitude stimulation, the point on Fig. 4 representing the current value of the objective func- tion becomes the point on the high amplitude (dashed) curve above the low amplitude asymptotic value (the asterisk). The slope of this curve is relatively large at this point. Because the adaptive scheme optimizes ankle torque, according to the gra- dient, descent rule adaptation happens at a rate proportional to this large slope -quickly. Thus, changing from low ampli- tude to high amplitude stimulation causes initially very rapid changes. Nevertheless, the rate of adaptation slows down as θ increases and after 60 s, at the end of the high ampli- tude condition, the system does not reach its high amplitude asymptotic value (the triangle) as evidenced by the trace of θ in Fig. 3. Instead, θ lies somewhere to the left of its high amplitude asymptotic value (the triangle). The slope of the low amplitude curve is relatively shallow in this region. Thus, upon the return to the low amplitude condition, the initial rate of return to the low amplitude asymptotic value (the asterisk) is slower than the previous initial rate of approach to the high amplitude asymptotic value (the triangle). From the differ- ential values of the slopes of the objective function in each condition arises the temporal asymmetry in the model. Figure 4 shows how the adaptive parameter changes as a function of stimulus condition. However, this figure does not make it directly apparent how gain changes as a function of stimulus condition. Nevertheless, gain is a function of the adaptive parameter and a simple change of variables makes the predicted changes in gain apparent (see Appendix B for details). Figure 5 plots the predicted approximate average rate of change of gain as a function of gain in the low ampli- tude condition (solid) and in the high amplitude condition (dashed). This plot depicts the vector fields that we call the averaged vector fields which are approximately accurate for small ε. The asymptotic values of gain in each condition are the values of gain at which the corresponding curves cross the horizontal axis (zero rate of change of gain). The direc- tion of crossing determines the stability of the equilibria. In this case, both equilibria are stable. As with the adaptive parameter, gain (the horizontal position on the axis of Fig. 5) changes continuously but the experimental condition (deter- mining which curve applies) changes abruptly. Part of the high amplitude curve is off the scale of the graph. Indeed, the high amplitude curve is about −0.7 below the low amplitude asymptotic value. Thus switching from low to high ampli- tude can cause rapid changes. This graph predicts that gain changes at an initial rate of about −0.7 s −1 upon a switch from low to high amplitude stimulation. However, the rate of change of gain is much more modest after a switch in the reverse direction: the rate is less than 0.02 s −1 . Close to the value of the high amplitude equilibrium the rate of change of gain in the low condition is even considerably lower. Note that the predicted high and low amplitude asymptotic gains match the simulations shown in Fig. ...
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... Proprioceptors can play a direct pro-nociceptive role in chronic pain states [31], and proprioceptive dysfunction has been suggested to indirectly provoke or maintain LBP through maladaptive/increased loading on spinal tissues, due to impaired paraspinal sensory feedback that negatively affects trunk motor control [26,35,36]. Proprioceptive weighting (PW) is a crucial function of the human proprioceptive system, which is the capability of the central nervous system to selectively prioritize the most reliable proprioceptive inputs from key body stabilizers, such as the ankle and lumbar muscles, which are essential for maintaining posture and effective motor control [4,6]. PW can be assessed in humans using balance control measures (based on recordings from a force plate) in combination with vibrotactile stimulation at 60 Hz, which mostly activates primary afferents in the muscle spindles [5,27,49]. ...
Manual therapy, such as spinal manipulation (SM), is commonly used to treat non-specific chronic low back pain (CLBP), although its mechanisms remain poorly understood. It has been hypothesized that the mechanical forces applied during spinal manipulation (SM) influence proprioceptive function, which is often impaired in patients with CLBP. This study aimed to investigate the effect of a single SM intervention on lumbar proprioceptive function and its potential relationship with analgesic effects in patients with CLBP. In a single-blind randomized controlled trial, data from 142 adults with or without CLBP were analyzed after random assignment to receive lumbar spinal manipulation (LMANIP), lumbar mobilization (LMOB), or no intervention (NI). The primary outcome was the proprioceptive weighting (PW) ratio, which reflects the central nervous system's preferred source of proprioceptive input for balance control, specifically from the lumbar and ankle muscles. PW ratios were assessed immediately before and after intervention by analyzing postural sway changes during vibrotactile stimulation (60 Hz). PW changed in both healthy participants and patients after the intervention, with a significantly stronger lumbar-steered PW following LMANIP compared to NI (β = -0.047, t(422) = -2.71, p = 0.007) and LMOB (β = -0.039, t(422) = -2.17, p = 0.030). Moreover, LMANIP was particularly effective in reducing pain in patients with stronger lumbar-steered PW before intervention (p < 0.017). These findings suggest that a single SM session enhances proprioceptive input from the lumbar muscles and that the strength of the analgesic effect is associated with the baseline PW status.
... Researchers also found that rats and humans dynamically adjusted weights assigned to each sensing modality based on their reliability to help decision-making [71]. Theories such as nonlinear adaptive control models [10] and Bayesian framework [72] have been applied to model the multisensory reweighting. ...
Animals integrate multiple sensory inputs to explore environments and execute locomotor behaviors. To carry out these behaviors, the nervous system internally reweights controller gains, putting more emphasis on those with the least uncertainty, e.g. in a Bayesian framework. Meanwhile, as sensory uncertainty increases, animals produce more vigorous active sensing movements. To better understand the link between these two complementary processes—multisensory reweighting and active sensing—we studied how the weakly electric glass knifefish Eigenmannia virescens alters its movement dynamics under parametric manipulations of illumination. We hypothesized a concomitant switch in both active sensing (an overt behavior) and multisensory reweighting (an internal control computation). To test this, we varied illumination levels from 0.1 to 210 lx as fish tracked a moving refuge. We discovered that in a neighborhood of a critical threshold (on the order of 1 to 10 lx), small increases in illumination led to dramatic changes in both active sensing and multisensory control, specifically in 1) steep reductions in fish head and tail movements and 2) decreased refuge tracking phase lag. Outside of this threshold, large changes in illumination only caused mild changes in behavior. A control-theoretic model that dynamically modulates the weights of vision and electrosense due to illumination changes explains the changes in closed-loop behavior in our experiments. Our findings enhance our understanding of active sensing, multisensory reweighting, and locomotor control.
... In accordance with the re-weighting principles, loss of balance is prevented in situations of degraded sensory information by decreasing the weight of the unreliable senses and giving more weight to the reliable ones (e.g., [8][9][10][11][12][13][14][15][16][17][18]). Thus, previous research on motion sickness susceptibility suggests that adaptation to changes in the environment may be critical for MS individuals. ...
... However, in order to detect transient phenomena, data were included that partially overlapped with the time of transitions between the fixed to sway-referenced and vice versa (because of spectra calculation computed on 10 s sliding time windows). This may be seen as a limitation of the method used here, but appears to be the only way of detecting phenomena that are transient in nature [9] that would otherwise be undetectable with a method that clearly separates the analyzed windows. ...
Postural control characteristics have been proposed as a predictor of Motion Sickness (MS). However, postural adaptation to sensory environment changes may also be critical for MS susceptibility. In order to address this issue, a postural paradigm was used where accurate orientation information from body sensors could be lost and restored, allowing us to infer sensory re-weighting dynamics from postural oscillation spectra in relation to car-sickness susceptibility. Seventy-one participants were standing on a platform (eyes closed) alternating from static phases (proprioceptive and vestibular sensors providing reliable orientation cues) to sway referenced to the ankle-angle phases (proprioceptive sensors providing unreliable orientation cues). The power spectrum density (PSD) on a 10 s sliding window was computed from the antero-posterior displacement of the center of pressure. Energy ratios (ERs) between the high (0.7–1.3 Hz) and low (0.1–0.7 Hz) frequency bands of these PSDs were computed on key time windows. Results showed no difference between MS and non-MS participants following loss of relevant ankle proprioception. However, the reintroduction of reliable ankle signals led, for the non-MS participants, to an increase of the ER originating from a previously up-weighted vestibular information during the sway-referenced situation. This suggests inter-individual differences in re-weighting dynamics in relation to car-sickness susceptibility.
... It is evidenced that PC involves cues from visual, vestibular, and proprioceptive channels [17]. During sensory stimulation, postural sway increased in response to changes in sensory stimuli [18] as a result of adjustments within the PC system to maintain an upright posture [19]. Yet, children with ASD are reported to have problems in modulating sensory information and deficiencies in visual processing [20,21]. ...
Postural stability and control are essential motor skills for successfully performing various activities of daily living. However, children with autism spectrum disorder (ASD) exhibit significant sensorimotor impairments. The aim of this study was to investigate the efficacy of psychomotricity training on postural control (PC) of children with ASD. We recruited thirty children (age = 8.01 ± 1.2; weight = 31.66 ± 8.1 kg; height = 129.7 ± 10.8 cm) diagnosed with ASD (intellectual quotient > 50) to participate in this study. They were divided into two groups: the experimental group (n = 16) and control group (n = 14). Children in the experimental group were trained with psychomotor activities two times a week for nine weeks. Statistic postural balance was assessed before and after intervention and on different vision conditions. The results showed that the psychomotor training significantly improved PC in standing position under different conditions when compared to the control group, in all parameters (CoPA; CoPLX; CoPLy) (p < 0.01). Our preliminary findings suggest the usefulness of the psychomotor training in children with ASD on static PC.
... It is also well known from research into postural control that visual, vestibular and somatosensory information can be reweighted, as necessary, to accommodate changes in the environment (Cenciarini & Peterka, 2006;Peterka & Loughlin, 2004). In the event that a sensory cue is absent, diminished, distorted or inappropriate, the sensory weighting hypothesis suggests that other, more reliable, cues are preferentially weighted during sensory integration (Carver et al., 2006;Craig et al., 2019). Extant evidence from research on postural control suggests that, compared to adults, adolescents transiently neglect information from cutaneous tactile mechanosensors to place greater weight on visual information in order to maintain orientation (Mallau et al., 2010;Viel et al., 2009) and on dynamic proprioceptive information from Golgi tendon organs and muscle spindles for segmental postural control (Cignetti et al., 2013). ...
Footwear may moderate the transiently heightened asymmetry in lower limb loading associated with peak growth in adolescence during running. This repeated-measures study compared the magnitude and symmetry of peak vertical ground reaction force and instantaneous loading rates (VILRs) in adolescents during barefoot and shod running. Ten adolescents (age, 10.6 ± 1.7 years) ran at self-selected speed (1.7 ± 0.3 m/s) on an instrumented treadmill under three counter-balanced conditions; barefoot and shod with partial-minimal and conventional running shoes. All participants were within one year of their estimated peak height velocity based on sex-specific regression equations. Foot-strike patterns, peak vertical ground reaction force and VILRs were recorded during 20 seconds of steady-state running. Symmetry of ground reaction forces was assessed using the symmetry index. Repeated-measures ANOVAs were used to compare conditions (α=.05). Adolescents used a rearfoot foot-strike pattern during barefoot and shod running. Use of conventional shoes resulted in a lower VILR (P < .05, dz = 0.9), but higher VILR asymmetry (P < .05) than running barefoot (dz = 1.5) or in partial-minimal shoes (dz = 1.6). Conventional running shoes result in a lower VILR than running unshod or in partial-minimal shoes but may have the unintended consequence of increasing VILR asymmetry. The findings may have implications for performance, musculoskeletal development and injury in adolescents.
... Because difficulty in standing varies with the size of the base of support, the flexible support of the central nervous system (CNS) in standing postures is often assumed (Watanabe et al., 2018a;Nandi et al., 2019). Furthermore, the difficulty in channeling the correct standing balance and the related neuromuscular activities varies depending on the available sensory inputs (Carver et al., 2006;Jeka et al., 2010). For example, more significant postural sway and specific neuromuscular activity while standing with eyes closed than with eyes open (Danna-Dos-Santos et al., 2015;Walker et al., 2020) has been reported. ...
Background
Intra- or inter-muscular (EMG-EMG) coherence is a simple and non-invasive method for estimating central nervous system control during human standing tasks. Although this research area has developed, no systematic literature review has been conducted.
Objectives
We aimed to map the current literature on EMG-EMG coherence during various standing tasks to identify the research gaps and summarize previous studies comparing EMG-EMG coherence between healthy young and elderly adults.
Methods
Electronic databases (PubMed, Cochrane Library, and CINAHL) were searched for articles published from inception to December 2021. We incorporated studies that analyzed EMG-EMG coherence of the postural muscles in various standing tasks.
Results
Finally, 25 articles fulfilled the inclusion criteria and involved 509 participants. Most participants were healthy young adults, while only one study included participants with medical conditions. There was some evidence that EMG-EMG coherence could identify differences in standing control between healthy young and elderly adults, although the methodology was highly heterogeneous.
Conclusion
The present review indicates that EMG-EMG coherence may help elucidate changes in standing control with age. In future studies, this method should be used in participants with central nervous system disorders to understand better the characteristics of standing balance disabilities.
... Postural control is possible through the integration of information from three sensory systems; somatosensory, visual and vestibular (Nashner, Black, & Wall, 1982). The integration of sensory information is dynamically regulated through a process referred to as sensory reweighting (Carver, Kiemel, & Jeka, 2006;Mahboobin, Loughlin, Redfern, & Sparto, 2005;Nashner & Berthoz, 1978;Peterka, 2002;Peterka & Loughlin, 2004;Van der Kooij, Jacobs, Koopman, & Van der Helm, 2001), which is a dynamical process in which the nervous system changes the "emphasis" of a particular sensory input by identifying and selecting the sensory inputs that provide the most useful and accurate information for attaining the postural control goals of orientation and equilibrium (Hwang, Agada, Kiemel, & Jeka, 2014). For example, changes in environmental conditions, such as moving from a bright to a dark environment, or from a fixed to a moving surface, or from a rough to slippery surface, requires dynamic updating of sensory weights to current conditions so that muscular control of balance is based on the most precise and accurate sensory information available (Horak, 2006;Logan, Kiemel, & Jeka, 2014;Teasdale, Stelmach, & Breunig, 1991;Woollacott, Shumway-Cook, & Nashner, 1986). ...
The purpose of this study was to identify and differentiate the motor strategies associated with sensory reweighting adapted during specific sensory integration tasks by healthy young adults. Thirty-six subjects (age range: 21-33 years) performed standing computerized dynamic posturography balance tasks across progressively increasing amplitudes of visual (VIS), somatosensory (SOM) and both (VIS+SOM) systems perturbation conditions. Adaptation in the motor strategy was measured as changes in electromyographic (EMG) activities and joint angles. The contribution of the perturbed sensory input in maintaining postural stability was calculated to determine the sensory reweighting. A multivariate design was used to model a linear combination of motor adaptation variables that discriminates specific sensory integration tasks. Results showed a significant progressive decrease in postural sway per unit amplitude of sensory perturbation in each condition, indicating dynamic sensory reweighting. Linear discriminant function analysis indicated that the adaptation in motor strategy during the VIS condition was associated with increased activity of EMG and joint angles in the upper body compared to the lower body. Conversely, during the SOM and VIS+SOM conditions, the adaptation in motor strategy was associated with decreased activity of EMG and joint angles in the lower body compared to the upper body. Therefore, the adaptation in motor strategies associated with sensory reweighting were different for different sensory integration tasks.
... This further supports the notion of a shift in sensory weighting and the consequences for motion perception. Sensory reweighting has been predominantly observed in multisensory perception and postural control based on optimal integration around the reliability and functional significance of the sensory signals (Assländer and Peterka 2014;Carver et al., 2006;Ernst and Banks 2002;Ernst and Bülthoff 2004). However, whether this mechanism has any relationship with cybersickness and sensory conflict has yet to be established. ...
The malaise symptoms of cybersickness are thought to be related to the sensory conflict present in the exposure to virtual reality (VR) content. When there is a sensory mismatch in the process of sensory perception, the perceptual estimate has been shown to change based on a reweighting mechanism between the relative contributions of the individual sensory signals involved. In this study, the reweighting of vestibular and body signals was assessed before and after exposure to different typical VR experiences and sickness severity was measured to investigate the relationship between susceptibility to cybersickness and sensory reweighting. Participants reported whether a visually presented line was rotated clockwise or counterclockwise from vertical while laying on their side in a subjective visual vertical (SVV) task. Task performance was recorded prior to VR exposure and after a low- and high-intensity VR game. The results show that the SVV was significantly shifted away from the body representation of upright and towards the vestibular signal after exposure to the high-intensity VR game. Cybersickness measured using the fast motion sickness (FMS) scale found that sickness severity ratings were higher in the high intensity compared to the low-intensity experience. The change in SVV from baseline after each VR exposure modelled using a simple 3-parameter Gaussian regression fit was found to explain 49.5% of the variance in the FMS ratings. These results highlight the aftereffects of VR for sensory perception and suggest a potential relationship between the susceptibility to cybersickness and sensory reweighting.
... The CNS, subsequently, reweights (dynamically adjusts the weight assigned to a particular signal) visual, vestibular, and proprioceptive input to generate the appropriate muscle forces required for the particular task. This allows effective control of the center of mass, resulting in proper equilibrium of the body (Brumagne et al., 2004;Carver et al., 2006). Previous investigations explored different models and methods for preserving equilibrium (Horak and Nashner, 1986;Runge et al., 1999). ...
... Many previous studies focused on the motor output of postural control in NSLBP patients (Ruhe et al., 2011). Others advocated the importance of considering the sensory inputs, particularly in terms of weighting the various proprioceptive signals, a critical consideration in optimal postural control (Gandevia et al., 1992;Lackner and DiZio, 2005;Carver et al., 2006). It is indeed well established that musculoskeletal injuries (Haghighat et al., 2021a;Haghighat et al., 2021b;Haghighat et al., 2021c;Haghighat et al., 2021d;Davoudi et al., 2022a), neuromusculoskeletal disease, such as stroke, Parkinson's, Multiple Sclerosis, Chronic obstructive pulmonary disease (Davoudi et al., 2022b) and other psychological factors including anxiety, are associated with disrupting the function of the sensory systems (Jamali et al., 2019;Dehmiyani et al., Frontiers in Bioengineering and Biotechnology frontiersin.org ...
The central nervous system (CNS) dynamically employs a sophisticated weighting strategy of sensory input, including vision, vestibular and proprioception signals, towards attaining optimal postural control during different conditions. Non-specific low back pain (NSLBP) patients frequently demonstrate postural control deficiencies which are generally attributed to challenges in proprioceptive reweighting, where they often rely on an ankle strategy regardless of postural conditions. Such impairment could lead to potential loss of balance, increased risk of falling, and Low back pain recurrence. In this study, linear and non-linear indicators were extracted from center-of-pressure (COP) and trunk sagittal angle data based on 4 conditions of vibration positioning (vibration on the back, ankle, none or both), 2 surface conditions (foam or rigid), and 2 different groups (healthy and non-specific low back pain patients). Linear discriminant analysis (LDA) was performed on linear and non-linear indicators to identify the best sensory condition towards accurate distinction of non-specific low back pain patients from healthy controls. Two indicators: Phase Plane Portrait ML and Entropy ML with foam surface condition and both ankle and back vibration on, were able to completely differentiate the non-specific low back pain groups. The proposed methodology can help clinicians quantitatively assess the sensory status of non-specific low back pain patients at the initial phase of diagnosis and throughout treatment. Although the results demonstrated the potential effectiveness of our approach in Low back pain patient distinction, a larger and more diverse population is required for comprehensive validation.
... This further supports the notion of a shift in sensory weighting and the consequences for motion perception. Sensory reweighting has been predominantly observed in multisensory perception and postural control based on optimal integration around the reliability and functional significance of the sensory signals (Assländer and Peterka, 2014;Carver et al., 2006;Ernst and Banks, 2002;Ernst and Bülthoff, 2004). However, whether this mechanism has any relationship with cybersickness and sensory conflict has yet to be established. ...
The malaise symptoms of cybersickness are thought to be related to the sensory conflict present in the exposure to virtual reality (VR) content. When there is a sensory mismatch in the process of sensory perception, the perceptual estimate has been shown to change based on a reweighting mechanism between the relative contributions of the individual sensory signals involved. In this study, the reweighting of vestibular and body signals was assessed before and after exposure to different typical VR experiences and sickness severity was measured to investigate the relationship between susceptibility to cybersickness and sensory reweighting. Participants reported whether a visually presented line was rotated clockwise or counterclockwise from vertical while laying on their side in a subjective visual vertical (SVV) task. Task performance was recorded prior to VR exposure and after a low and high intensity VR game. The results show that the SVV was significantly shifted away from the body representation of upright and towards the vestibular signal after exposure to the high intensity VR game. Cybersickness measured using the fast motion sickness (FMS) scale found that sickness severity ratings were higher in the high intensity compared to the low intensity experience. The change in SVV from baseline after each VR exposure modelled using a simple 3-parameter gaussian regression fit was found to explain 49.5% of the variance in the FMS ratings. These results highlight the aftereffects of VR for sensory perception and suggests a potential relationship between the susceptibility to cybersickness and sensory reweighting.
