Individuals can rapidly develop adaptive skills for fall prevention after their exposure to the repeated-slip paradigm. However, the changes in neuromuscular control contributing to such motor adaptation remain unclear. This study investigated changes in neuromuscular control across different stages of slip-adaptation by examining muscle synergies during slip training. Electromyography signals during 24 repeated slip trials in gait were collected for 30 healthy older adults. Muscle synergies in no-adaptation (novel slip), early-adaptation (slip 6 to 8), and late-adaptation trials (slip 22 to 24) were extracted. The similarity between the recruited muscle synergies in these different phases was subsequently analyzed. Results showed that participants made significant improvements in their balance outcomes from novel slips to adapted slips. Correspondingly, there was a significant increase in the muscle synergy numbers from no-adaptation slips to the adapted slips. The participants retained the majority of muscle synergies (5 out of 7) used in novel slips post adaptation. A few new patterns (n = 8) of muscle synergies presented in the early-adaptation stage to compensate for motor errors due to external perturbation. In the late-adaptation stage, only 2 out of these 8 new synergies were retained. Our findings indicated that the central nervous system could generate new muscle synergies through fractionating or modifying the pre-existing synergies in the early-adaptation phase, and these synergies produce motor strategies that could effectively assist in recovery from the slip perturbation. During the late-adaptation phase, the redundant synergies generated in the early-adaptation phase get eliminated as the adaptation process progresses with repeated exposure to the slips, which further consolidates the slip adaptation. Our findings improved the understanding of the key muscle synergies involved in preventing backward balance loss and how neuromuscular responses adapt through repeated slip training, which might be helpful to design synergy-based interventions for fall prevention. One major cause of injurious falls is slipping, which is responsible for over 40% of outdoor falls among older adults, and nearly one fifth of fractures3. Hence, abundant research is targeted at developing interventions for lowering the likelihood of slip-induced falls in older adults8. Among the interventions, repeated slip-perturbation training has shown to be efficacious in reducing laboratory-induced falls over longer-term10.The annual fall rates for older adults range from 0.3 to 1.6 times per person, with an annual cost of approximately $31 billion12. The reactive adaptations occur in the form of a better recovery stepping location, increased magnitude of certain joint moments, and earlier onset of muscle activation13. It is known that the changes in human movement kinematics and kinetics result from motor programs generated by the CNS to signal the neuromuscular system. While changes in biomechanical variables during slip adaptation have been well analyzed and documented, the underlying neuromuscular control mechanisms that could contribute towards the movement adaptations are still unclear.These studies have revealed that the rapid adaptation to slips occurs via improvements in both proactive and reactive control. After experiencing repeated slip perturbations, participants proactively modify their gait pattern , resulting in a reduction in the slip intensity14. It has been proposed that the improvement in balance performance during postural perturbations is associated with spinal and supraspinal control, which are responsible for controlling the structure of muscle synergies16. Therefore, muscle synergies could be considered as the lowest level of the motor control hierarchy. Additionally, balance training could modify spinal reflex circuits and lead to persistently reduced Hoffmann-reflexes18, ultimately affecting the recruitment of muscle synergies19. Thus, the investigation of muscle synergies could provide new insight into the motor control mechanisms underlying perturbation-induced motor adaptation. Additionally, understanding how muscle synergies contribute to a successful recovery in the adapted trials would be helpful for development of fall prevention protocols.According to the muscle synergy hypothesis, the CNS simplifies motor control through the flexible combination of several muscle synergies, which are defined as a set of muscles recruited by a single neural command signal20. The use of similar muscle synergies between balance tasks indicates that common neural mechanisms might be used to maintain center of mass stability during different conditions. Therefore, it is possible that even if biomechanical adaptations occur during repeated slip training, a common set of muscle synergies could still be found between novel slips and adapted slips. However, a study comparing perturbed and unperturbed walking also reported that some muscle synergies were recruited only during the novel perturbation trial but not during unperturbed walking, and those synergies were considered to play a key role in enhancing the recovery response against the unexpected perturbation20. Similarly, Sawers et al. found that muscle synergies with different structures were recruited between slip-induced falls and recoveries in older adults21. As repeated slip training can greatly lower fall rate, it is possible that the synergies related to recoveries might be maintained and those related to falls might be discarded following repeated perturbation exposure. These changes in muscle synergies could contribute towards motor adaptation and improved reactive balance responses.Previous studies have reported that a common set of muscle synergies are used between unperturbed walking and gait perturbations, as well as across different perturbation intensities24. However, such early adaptation is also prone to wash out effects and/or interference, and participants have deteriorated performance when re-exposed to perturbation trials after a block of unperturbed trials or an opposing perturbation26. To induce a more long lasting adaptation, studies have used principles of motor learning to provide both block and random training and also to increase the number of trials to ensure “overtraining”28. Extensive research using single session protocols has shown that after the first block there is a plateau in adaptation in kinematic variables such as center of mass (COM) state stability, which is retained even under random training conditions 27. Thus, overtraining might just help in longer-term retention (long-term gain) even if it does not result in any significant gains after the initial acquisition of adaptation (acute gain). However, given the wide range of muscle synergy patterns feasible for generating similar motor behaviors, it is possible that overtraining results in changes to muscle synergies which are not evident in biomechanical variables. In early adaptation stages, redundant muscle synergies might be used to prevent backward balance loss, which could be optimized through overtraining to minimize muscle effect (muscle activities)29 or motor effect cost (neuron activities)30. Therefore, optimization of muscle synergies through overtraining may decrease the amount of energy expenditure required to regain balance, even if biomechanical responses do not change. Thus, the changes in muscle synergies due to overtraining are not known and deserved to be analyzed.Previous evidence has shown that adaptation to repeated overground slip-perturbation exposure is induced rapidly with as few as 5–8 trials needed to reduce falls from about 50–60% to 0% in both young and older adultsThe purpose of this study was to investigate changes in neuromuscular control during 24 repeated over-ground slips. Three stages were selected from these trials: the no-adaptation stage (novel slip (S1)), early-adaptation stage (S6–S8), and late-adaptation stage (S22–S24). We hypothesized that there would be changes in muscle synergies from novel slip to adapted slips, indicated by modifications in structure and increment in number of muscle synergies. We also hypothesized that there would only be minor changes in muscle synergy structure from early-adaptation to late-adaptation stages, as overtraining is usually included in motor leaning or training protocols for consolidation or refinement of acquired performance gains.31. Participants were screened via a questionnaire before the experiment to exclude individuals with any neurological, musculoskeletal, cardiopulmonary, or other systemic disorders that would make participation unsafe. These participants were selected based on the following criteria: firstly, participants with missing electromyography data (missing channels or artifacts due to motion or other sources were not included in this study) were excluded. Second, left-leg dominant participants  were also excluded, as a previous study has reported differences in the number of muscle synergies between dominant side and non-dominant side32. The study was carried out in accordance with the Declaration of Helsinki of 1975, and all participants provided written informed consent which was approved by the Institutional Review Board of the University of Illinois at Chicago.Thirty healthy older adults  participated in the study. They were selected from our data-base on overground slip-perturbation training containing the data of 38 participants 31. The slip was introduced by a pair of low-friction moveable platforms (L: 0.65 × W: 0.30 m) imbedded in a 7-m walkway and hidden by stationary decoy platforms surrounding them. Thus, the sliders were less apparent to the participants. Without participants’ awareness, the right slider would be released to freely slide in the anterior–posterior direction up to 90 cm to bring the COM posterior to the base of support, just as a slip does in real-life. The instruction was consistent for all trials as “a slip may or may not occur.” Hence, participants were not aware of the sliders’ condition  in all the trials. The starting position was randomly adjusted to allow 2–4 steps before landing on the right slider, so the participants were not aware when (at which step) the slip would occur. The instruction and the design of experimental setup could minimize participants’ anticipation of the upcoming trial (slip occurrence and timing).All subjects received 24 slip trials during walking provided in a “mix-and-blocked” manner. This protocol consisted of 10 trials of unperturbed walking, a block of 8 repeated slips (S1–S8), a block of 3 nonslip trials, another block of 8 repeated slips (S9–S16), and a final block of 15 mixed trials. The mixed block contains 7 nonslip trials and 8 slip trials (S17–S24)33. After a single slip exposure, individuals show significant improvements in stability and a reduction in the slip distance on the second slip, even though a majority (> 80%) still lose their balance11. Thus, only the first trial was used to represent the true novel slip response (no-adaptation). Participants rapidly adapted within 3 to 5 slip perturbations, represented by the great improvement in slip outcomes 34. Therefore, S6–S8 were selected to represent the early-adaptation stage. The last three trials (S22–S24) following 3 blocks of slip training were used for the late-adaptation stage in this study.It is widely known that a single slip trial exposure is sufficient to induce changes in reactive balance, which is known as the first-trial effect9. Kinematics from a modified Helen Hayes marker set (26 retro-reflective markers)35 were recorded by an 8-camera motion capture system . The 26 markers were placed on vertex, ears, rear neck, shoulders, midpoint of the right scapula, elbows, wrists, sacrum, greater trochanters, mid-thighs, knees, mid-shanks, ankles, heels, and the fifth metatarsal heads. Another two markers were placed on the sliders36.Subjects were protected by a full-body harness connected to a loadcell  by shock-absorbing ropes. Four force plates  were installed beneath the walkway, including two force plates on each side of the walkway. There was no contact between the right and left side of the walkway and participants were instructed to walk without crossing the midline between left and right sides. The force plates recorded the vertical ground reaction force from leading foot touchdown to up to 4 steps after slip onset. Once a participant’s right (slipping) foot was detected by the force plates, a computer controlled triggering mechanism would release the right platformThe wired sliver-sliver chloride surface electrodes (1 cm diameter and 2 cm inter-electrode distance) with inbuilt preamplifiers of gain × 35  were used to record surface electromyography (EMG) activity from 4 muscles on each leg at 600 Hz. The ground electrode was placed on the anterior superior iliac spine. EMG signals from the preamplifiers were filtered (fourth-order low-pass Bessel filter with a 300 Hz cutoff frequency) and amplified (gain × 100) with a CyberAmp 380 amplifier . The EMG data, loadcell data, and force plate data were collected by the same data acquisition system at 600 Hz, which was synchronized with the kinematic data collected at 120 Hz.20.EMG was recorded from the tibialis anterior (TA), medial gastrocnemius (MGAS), vastus lateralis (VLAT), and biceps femoris long head (BFLH). Using custom MATLAB  routines, raw, unrectified EMG signals were digital high-pass filtered at 35 Hz after data collection and then full-wave rectified. The rectified data was smoothed via a second-order, dual-pass (both forward and backward directions) Butterworth low-pass filter with a 40 Hz cutoff frequency37, post-slip recovery  foot lift-off (LLO), and recovery foot touchdown (LTD2) in slip trials. These events were detected from the vertical ground reaction force (GRF decent below 10 N). Post-slip aborted steps taken by two participants, characterized by an unloading of the trailing limb upon slip onset, then reloading before its complete unloading were noted. As the GRF in these trials never decent below 10 N during the unload/reload period, their time events (LLO and LTD2) were detected based on the instant of minimum GRF37. Slip onset was defined as the time when the velocity of the slider marker exceeded 0.05 m/s in anteroposterior (AP) direction38. The duration analyzed in this study was between LTD1 and LTD2, which is one gait cycle. This duration contains both proactive phase prior to slip-onset and reactive phase after slip-onset. The duration of recovery foot swing phase was estimated by subtracting LLO from LTD2. COM position was calculated as the distance between the projected COM location (estimated using a 13-segment model)39 and slipping heel at pre-LTD2 (~ 10 ms prior to LTD2) in AP direction. COM velocity was calculated by subtracting the velocity of slipping foot (heel) from the velocity of COM at pre-LTD2. Dynamic stability was then calculated was the shortest distance from the instantaneous COM state  to thresholds against backward balance loss39. Positive stability indicate a stable COM state with a lower likelihood of backward balance loss, while negative value indicates an unstable state with a higher likelihood of balance loss. Here the pre-LTD2 was chosen as the stability at this instant has been proved to determine the slip outcome40. Hip height was calculated as the midpoint of hip markers in the vertical plane at LTD2, which could quantify the limb support. Slipping distance was calculated as the maximum displacement of the slider marker from the touchdown of slipping foot (RTD) to its lift-off, and slipping velocity was the maximum velocity of the slider in this duration. Step length was calculated as the heel distance between both limbs at LTD2, and then normalized by foot length. The trunk angle was calculated as the angle between the trunk segment and the horizontal plane, and the maximum trunk angle was calculated as the peak value between RTD and LTD2. All these variables were calculated in the sagittal plane and could be considered as good representatives of slip adaptation.The crucial time events were left foot touchdown (LTD1) prior to slip-onset, slipping  foot touchdown 11, or NLOB when the recovery heel landed anterior to the sliding heel and recovery stepping was not needed.Slip outcomes were classified as a backward loss of balance (BLOB) or no backward loss of balance (NLOB). Slip outcome was classified as a BLOB when the recovery heel landed posterior to the sliding heel21. The small number of muscles (n = 8) recorded for this study might lead to an underestimation of the number of muscle synergies41. The 30 ms time bins were selected in order to have sufficient time resolution and to avoid dealing with point to point changes in the highly variable EMG signals42. In addition, our previous study has compared two different time bins and found that there was very limited effect of time bins on the structure of muscle synergies43.EMG data for 8 muscles from LTD1 to LTD2 was down-sampled by averaging the data in 30 ms bins, and then EMG data from each trial was concatenated end-to-end to create matrices that were 8 (number of muscles) × n (number of time bins) in size23. Therefore, only the first slip trial was used for muscle synergy extraction for the novel condition. A prior study has reported increased intra-subject variability among the muscle synergies extracted from single trial compared to concatenated trials, suggesting multiple trials are more reliable to capture the inherent EMG variability representing motor pattern44. Therefore, three consecutive trials in each stage were used for muscle synergy extraction for early-adaptation and late-adaptation stages, and these trials were simply concatenated end-to-end in each stage. The single and concatenated EMG matrices were then normalized to the maximum activation in all non-slip trials prior to the novel slip trial45, and each row (muscle vector) was scaled to have unit variance to ensure that each muscle was equally weighted in the extraction. This unit variance was removed after muscle synergy extraction, changing the muscle synergies back to their original scaling.Due to the fast adaptation in kinematic outcomes to slip perturbations, changes in neuromuscular control could also occur after very few slips46, which is a decomposition algorithm used extensively in muscle synergy analysis47. This algorithm assumes that a muscle activation pattern, M, which was evoked by a perturbation (slip or auditory cue) in a given time period, is comprised of a linear combination of a few muscle synergies, wi, which are each recruited by a synergy recruitment coefficient, ci. Therefore, a particular muscle activation pattern would be characterized by the following equation: M = c1 w1 + c2 w2 + ⋯ + cn wn + ε.Muscle synergies were extracted from EMG matrices with unit variance for each condition  by non-negative matrix factorization using customized Matlab routinesε is a scalar of EMG noise, wi is a vector corresponding to the ith muscle synergy, and each wi is multiplied by a scalar recruitment coefficient, ci. The spatial components were considered a fixed time-invariant pattern, while the temporal activation coefficients varied across time48; therefore, the activation represents how the group of muscles in w is activated over time. If the average contribution of one component in a muscle synergy was above the threshold of 0.4, it was considered as a major contributor to that synergy49. In order to compare the temporal activation coefficients among different stages, these coefficients in one gait cycle (LTD1 to LTD2) were scaled to same length for each subject with 100 points.In this equation, 20. These VAF thresholds could guarantee that the recorded muscle curves could be well reconstructed.The number of muscle synergies required to explain any of these datasets was determined by selecting the smallest number of synergies that could adequately reconstruct the muscle responses, which was quantified by the variance accounted for (VAF). To ensure consistency in selecting the number of muscle synergies within each condition, the number of muscle synergies selected was the minimum number at which the muscle synergies accounted for greater than 75% of the VAF in each muscle and exceeded 90% of the overall VAF21. Such method could also reduce the effect of the innate variability of the muscle activity on the extracted motor patterns. The number of clusters for each condition was determined by identifying the minimum number of clusters that partitioned the muscle synergies such that no cluster contained more than one muscle synergy from the same subject.To facilitate the comparison of muscle coordination patterns between stages, muscle synergies extracted from each condition were firstly pooled across subjects and grouped with a hierarchical cluster analysis53. The merging of muscle synergies could be achieved by reassigning multiple synergy-encoding interneurons to be driven by the same oscillator. Conversely, the fractionation could be achieved by reassigning the synergy-encoding interneurons to be driven by a different oscillator , this single synergy would be considered as the merging of the set of synergies, while the set of synergies could be also considered as the fractionation of the single synergy. To analyze the adjustments of weight(s) in the structure of a muscle synergy, the weights were compared between different synergies with similar activation coefficients.Previous studies have shown that the CNS could modify existing motor patterns to adapt to different perturbations, including merging, fractionation, and adjustments of weight(s) in the structure of muscle synergytor Fig. a. The adtor Fig. b. To ver56. Five random subjects were removed from the whole dataset, and then the muscle synergies were extracted from this sub-maximum (25/30) dataset for no-adaptation stage, early-adaptation stage, and late-adaptation stage separately. This process was repeatedly conducted 300 times, and the averaged cluster numbers for each stage were calculated. In addition, we also compared the similarity  between the clustered muscle synergies from a random selected sub-dataset and the one from the whole dataset for each stage.To verify the robustness of the clustered muscle synergies (the consistency of cluster numbers across different samples), we used a cross-validation procedure57.To investigate the kinematic differences in different adaptation stages , normal distribution was checked in advance using Kolmogorov–Smirnov test. Kruskal–Wallis test was conducted for the variables with non-normal distribution (rate of BLOB), and repeated ANOVA was conducted to compare adaptation effects for the normally distributed variables . Mauchly’s sphericity test was used to validate the repeated ANOVA, and Huynh–Feldt correction was conducted when sphericity was violated. Lastly, significant main effects were resolved using paired t-tests between groups. Benjamini and Yekutieli (B-Y) corrections were applied to adjust the false discovery rate for these Post hoc tests (corrected α = 0.024)18. The muscle synergies from different stages with significant similarity were classified into the same mode. For the recruitment coefficients (degrees of freedom = 98), the similarity was checked for those muscle synergies from the same mode, and any pair with r > 0.3 was considered to be significantly similar (p < 0.01).To test the differences in muscle synergies among different adaptation stages, the number of muscle synergies were first compared using a paired Wilcoxon signed rank test. Cohen’s d mean difference effect sizes and power sizes were also calculated to verify the validity of these results. Next, the correlation coefficient (r) was calculated between the average muscle synergy vectors and between the average recruitment coefficients from the clusters to compare the coordination pattern of muscle synergies. A pair of muscle synergy vectors (degree of freedom is 6) were considered similar if they had r > 0.834, which represents statistically significant similarity (p < 0.01)During the slip perturbation training, all 30 participants lost their balance on novel slip trials S1), but successfully recovered in the subsequent early-adaptation trials (S6–S8) and late-adaptation trials (S22–S24) with a BLOB rate of 0% for both phases. Only two participants demonstrated an aborted stepping response while the rest all took a backward recovery step. The repeated ANOVA results indicated that there was a significant training effect on BLOB rate  to the early-adaptation (n = 4.4 ± 0.7) and late-adaptation n = 4.2 ± 0.6) slips , 13 were recruited in early-adaptation (WEi), and 7 were recruited in late-adaptation (WLi). Here, i indicates which mode the synergy belongs to . Among these modes, Modes 1–8 were the common modes which were recruited by at least two different stages , they were considered to be fractionated into units with fewer muscles in the early-adaptation stage. Specifically, Mode 9 was fractionated into Mode 1 and Mode 4, and Mode 10 was fractionated into Mode 2 and Mode 6 Fig. . SimilarThe mode of muscle synergies decreased from 13 to 7 in this stage, and all the 7 muscle synergies recruited in the late-adaptation stage had appeared in the early-adaptation stage  from the sub-maximum dataset were similar with those extracted from the entire dataset . More importantly, even though there was a slight change in the number of clusters after reducing the sample size, the synergy modes were highly consistent with those extracted from the entire dataset  used in novel slips are similar to those used in the adapted trials, there are significant changes in neuromuscular activity during repeated slip training in healthy older adults. Following repeated training, specific patterns of muscle synergies emerged to possibly compensate for the sensorimotor errors due to external perturbation. Around half of these new muscle synergy modes in the early-adaptation stage did not appear in the late-adaptation stage, indicating patterns of muscle synergies also significantly changed in the late-adaptation stage.58. Significant changes were detected from the novel slip to the adapted slips (S6–S8) in the step length, gait phase, stability, limb support (hip height), and the control of slip intensity. As a part of adaptation, participants lowered the slip perturbation intensity (displacement and velocity) and increased their swing phase which could have allowed them to take a longer recovery step. Such kinematic changes resulted in the BLOB rate dropping from 100% on S1 to 0% on S6–S8. Even if larger stability could increase the risk of forward balance loss, a previous study reported that prior adaptation to slips had only limited interference during the initial phase of trip recovery59, suggesting that the slip adaptation probably would not lead to forward balance loss. There was no change in kinematic variables between early-and late-adaptation phase. This suggests that larger training dosage may not serve to further improve gait characteristics and COM stability, while it is still possible that a larger training dosage might keep fine-tuning or optimizing the individual joints and their coordination, which could not be represented by gait kinematics and stability.Kinematic results indicated that all the participants rapidly adapted to the slip perturbation within 8 trials (early-adaptation stage), which was consistent with the previous findings60. As the novel slips always had a higher slip perturbation intensity  remained the same across the novel slip as well as early- and late-adaptation stages. Even though the other two muscle synergies  within only 6 repeated trials, suggesting that repeated perturbation training is an effective method for the fractionation of muscle synergies. This finding was consistent with previous arguments that the fractionation of muscle synergies is a process primarily driven by development and training62. The fractionation of muscle synergies in the adapted trials indicated that participants changed their recovery strategies for balance control, at least by stopping reliance on quick recovery stepping for fall prevention in these repeated slips  synergies were only recruited in the novel slips, as such strategy could not lower the slip intensity and prevent backward balance loss62, including fractionation, and adjustments of the weight for specific muscle(s). For the 4 new synergies  generated from weight adjustments, most of the adjustments occurred on the recovery side, and 3 out of 4 synergies modified the weight of VLAT of the recovery limb. Specifically, the weight of VLAT in WE8 increased to the maximum level, which could improve the knee extensor moment after recovery touchdown and enhance the propulsive force required to improve COM stability and limb support63. While the weight of VLAT decreased to ~ 0 in WE13 and WE14, the temporal activation of these two synergies indicated that their peak occurred in the swing phase, suggesting that both synergies might help moving the swing leg anterirorly. Both synergies (WE13 and WE14) showed a co-activation of BFLH and TA, which could increase the knee flexion and dorsi-flexion of the swing leg, resulting in a longer recovery step and larger toe clearance. Similarly, the co-activation of BFLH and TA could also be seen in WL7, indicating that such strategy was retained after repeated slip training for taking a post-slip step. The adjustments of the motor pattern indicated that the CNS might adopt the optimization algorithm to search for the optimal synergies during slip adaptation in gait, and the weight of motor patterns might be adjusted heuristically based on previous experience64. However, there were 2 muscle synergies (WE12 and WE15) that showed low similarity to all the synergies used in novel slips. Both synergies showed a co-activation of VLAT and GAS in the recovery limb, which could lead to knee extensor and plantar flexion to initiate a foot lift-off. These synergies might be due to the inter-individual variability in slip adaptation, but it is also possible that these synergies might be generated from other pre-existing synergies which were either inborn or acquired during other trainings65. In general, a majority of the new synergies could be considered as altered motor programs (common synergies) for balance control, and this fine-tuning of neuromuscular control would greatly affect the slip outcomes.Our results also reflected that the CNS tends to modify existing motor patterns rather than assembling new patterns66. The activation of the MGAS muscle in Mode 8 could lead to a flat foot landing, and the co-activation of the BFLH and VLAT in Mode 6 could increase the stiffness of the slipping limb knee joint. The reduction of the slip intensity could further improve the COM stability, and lower the likelihood of fall or backward balance loss. In addition, there are no muscle synergies in the novel slips that demonstrated activation of the slipping limb MGAS around slip onset, which might be one of the key factors leading to higher slip intensity in novel slips. All these findings suggested that there is a shift from a reliance on feedback control to feedforward control after repeated slip training, which is consistent with previous postulations68.The temporal activation of these generated synergies showed that some of them  were activated before slip-onset. These modes might be related to proactive adjustments, which can greatly lower slip intensity. Previous evidence has shown that slip intensity is highly related to changes in kinematics of the slipping limb ankle and knee joints69 and result in higher stability and lower fall risk70, it could also reduce knee flexion in swing-phase71, leading to an abnormal gait pattern and higher energy cost. After experiencing the mixed-training block, participants may abandon this type of synergy, as the increment of stiffness  in the slipping limb alone might be sufficient for balance control. Such findings further support our postulation that an optimization algorithm might be used to change muscle synergies during adaptation. Our findings indicated that no new synergies were generated from early-adaptation to late-adaptation after the plateau in performance was reached (around S8). The only change between these two stages was the optimization of muscle synergies by discarding the redundant synergies. These findings were consistent with the previous postulation that providing more repetitions once the plateau in performance is reached could reduce redundancies and consolidate the motor response72. Our findings indicated that a single training session was sufficient to induce changes in muscle synergies; however, it is still unclear whether the generated synergies or optimized synergies could be retained in a long period. Until now, only short-term retention of the acquired muscle coordination patterns has been investigated and proved74, while previous studies have reported that the acquired motor patterns could be retained for over three months75. It is possible that the retained motor patterns were due to the long-term retention of altered muscle synergies, and such possibility deserves further analysis.The CNS may not generate any new synergies in the late-adaptation stage after receiving a block of mixed-training trials, but only select the optimal ones or discard the redundant ones generated in the earlier stage. The principal reason to discard these redundant muscle synergies might be that they could affect efficiency of the gait pattern, if retained. For example, WE8 and WE15 were similar in both structure and activation. It can be postulated that such small differences might have had very limited effects on balance control and slip recovery outcomes. Hence, the discard of WE15 in the late-adaptation phase had very limited effects on the motor performance, represented by similar kinematic measures between early- and late-adaptation. Hence, it is possible that the redundant muscle synergies were abandoned in the late-adaptation phase. For WE12, although the coactivation of BFLH and VLAT in the recovery limb could enhance joint stiffness32. However, the inter-limb difference in synergy number was only found in the upper limb movements, and there is no evidence indicating that lower limb movements also have the same inter-limb difference. Furthermore, a previous study found that the acquired motor strategies for fall prevention could be transferred from the training limb to the contralateral one76, suggesting that similar muscle synergies might be recruited between the dominant and non-dominant limbs for fall prevention. Therefore, we expect similar stage-to-stage changes should also be observed on the non-dominant side with just slight differences in the synergy modes. However, such expectation needs to be further verified. For the muscle strength of lower limbs, it has been reported that muscle weakness has limited influence on the extracted muscle synergies during gait78; hence, the lower limb strength would not affect our conclusion.There are other factors which might affect the extraction of muscle synergies, such as the dominant side and muscle strength. As the perturbation was only triggered on the dominant side and the dominant side was found to have a higher number of extracted muscle synergies compared to the non-dominant side, it is possible that different synergy modes might be recruited on the non-dominant side compared to the dominant one33. Thus, using an average of the first three trials would contaminate the true novel slip response. Second, the number of muscles (n = 8) recorded for this study could be seen as another limitation of the study, and could lead to an underestimation of the number of muscle synergies recruited during walking and slip responses41. Although both the number of muscles and the choice of the muscles could impact the results of muscle synergies, the muscles included in the present analysis can be considered to be among the dominant muscles involved in the slipping response21. This may help offset the effect of a limited number of muscles being used in the muscle synergy analysis. For future studies, it is recommended to include data from more proximal muscles (hip and trunk) contributing to posture and balance control. Third, although our study tried to lower the anticipation of the upcoming slip, it is impossible to eliminate all the anticipation after the exposure of the novel slip. Thus, awareness of upcoming slip conditions might contribute to changes in muscle synergies, and the optimized synergies exhibited post-training might not be generalized to unexpected slips in real-life. However, previous reports have revealed that around 50% of real-life falls are induced by snowy or icy surfaces during winter80. Therefore, most slips experienced during daily living might not be totally unexpected when individuals expect they might experience a slip . Thus, our findings could at least generalize to parts of real-life situations. Lastly, a constant time bin of 30 ms was used in this study, as the duration of reactive stepping changed during the training session. Thus, the total number of time bins for each stage were different, which might affect the extracted muscle synergies. However, our previous study compared two different time bins and found that there was very limited effect of time bins on the structure of muscle synergies43. Further, no difference in the duration of reactive stepping was found between the early- and late- adaptation stage, and the selection of time bin would not affect our conclusion.This study has certain limitations which have been considered while interpreting the results. This study only used one trial for analyzing the novel slip response compared to three trials each for the early and late phase. This was done as it is widely known that a single slip trial exposure is sufficient to induce adaptive changes – known as the first-trial effectIn conclusion, our findings indicate that the CNS could generate effective muscle synergies through fractionating or modifying the pre-existing synergies recruited in the novel slips within 8 training trials. These new synergies could effectively compensate for the instability induced by the slip perturbation, but there were also redundant synergies generated in the early-adaptation stage. Because continued block and mixed training provides increased dosage, the CNS possibly chooses to retain optimal muscle synergies while the redundant ones are abandoned. Thus, our study provides evidence that the kinematic adaptation to overground slip-perturbations previously observed could be attributed to the recruitment of effective muscle synergies generated during repeated slip training. Our results suggest that the generation and elimination of muscle synergies is an important marker of neural change to adapt to external perturbations. The findings from our study could inform clinicians of basic mechanistic changes underlying slip outcome improvements and such knowledge could assist with designing perturbation-based slip training protocols for fall prevention.


Afferents of peripheral mechanoreceptors innervate the skin of vertebrates, where they detect physical touch via mechanically gated ion channels (mechanotransducers). While the afferent terminal is generally understood to be the primary site of mechanotransduction, the functional properties of mechanically activated (MA) ionic current generated by mechanotransducers at this location remain obscure. Until now, direct evidence of MA current and mechanically induced action potentials in the mechanoreceptor terminal has not been obtained. Here, we report patch-clamp recordings from the afferent terminal innervating Grandry (Meissner) corpuscles in the bill skin of a tactile specialist duck. We show that mechanical stimulation evokes MA current in the afferent with fast kinetics of activation and inactivation during the dynamic phases of the mechanical stimulus. These responses trigger rapidly adapting firing in the afferent detected at the terminal and in the afferent fiber outside of the corpuscle. Our findings elucidate the initial electrogenic events of touch detection in the mechanoreceptor nerve terminal. In vertebrates, extrinsic touch is detected in the skin by cutaneous mechanoreceptors and somatosensory neurons of the peripheral nervous system. The afferent nerve fibers of these cells innervate the skin, where they form specialized ending structures which sense mechanical stimuli. Within the afferent terminals, mechanically gated ion channels (mechanotransducers), such as Piezo2, detect touch and transform it into mechanically activated (MA) current . Extracein vitro (Studies of MA current and mechanotransducer biophysics have been limited to heterologous expression systems and dissociated somatosensory neurons in vitro . Most noin vitro . Howeverin vitro . To our Anas platyrhynchos domesticus). The Grandry corpuscle is an avian tactile end-organ innervated by rapidly adapting mechanoreceptors, which form thin terminals between Schwann cell-derived lamellar cells  corpuscles in the bill skin of the tactile specialist Mallard duck (ar cells . The Graar cells . Compareex vivo bill-skin preparation from late-stage duck embryos (τ=8.95 ± 1.82 ms) in the terminal is notably similar to the inactivation rate of fast-inactivating MA current measured from the somas of murine and duck mechanoreceptors in vitro  to the bill-skin preparation blocked APs and voltage-gated sodium current in the afferent terminal . In someImportantly, APs in the afferent terminal are physiologically distinct from APs fired by Grandry lamellar cells . Lamellain vitro models to study mechanotransducers. At the same time, an important aspect of the afferent terminal response in situ is absent from cultured cells: the MA current in the OFF phase. Further studies of rapidly adapting corpuscles and other mechanoreceptor endings will be required to understand the mechanisms underlying both the OFF and ON responses. Together, these findings reveal fundamental characteristics of mechanotransduction at the physiological site of touch detection in mechanosensory neurons.Here, we have shown that mechanical stimulation evokes MA current in the afferent terminal which initiates propagating APs. Critically, MA current in the terminal has properties closely resembling those observed in dissociated neuron somas. This ultimately confirms the validity of using Anas platyrhynchos domesticus) were approved by and performed in accordance with guidelines of the Institutional Animal Case and Use Committee of Yale University, protocol 11526. The bill-skin preparation was slightly modified from previously published methods , then washed with fresh Krebs solution.Experiments with duck embryos . All experiments were performed in Krebs solution at room temperature. Data were sampled at 20 kHz and low-pass filtered at 2 kHz. Terminals were recorded in whole-cell mode and were held at –60 mV during voltage-clamp experiments. Resting membrane potential was measured in current-clamp mode shortly after breaking in. In both voltage- and current-clamp, mechanical stimuli were applied to a single corpuscle using a blunt glass probe (2–10 μm tip diameter) mounted on a piezoelectric-driven actuator (Physik Instrumente GmbH). A mechanical step stimulus was applied to corpuscles starting at 1 μm and increasing by 1 μm after each indentation. The static plateau of the step stimulus lasted 150 ms, while the ramp had a duration of 3 ms for both the ON and OFF phases. For both phases in each terminal, the inactivation rate (τ) of the MA current was calculated by fitting a single exponential function (I = I0×exp^(− t/τ), where I0 is the baseline-subtracted peak current amplitude, t is the time from the peak current, and τ is the inactivation constant) to the decaying portion of the largest three MA current responses and averaging the fitted τ values. The activation τ was calculated similarly using the rise portion of the response . Standard-wall, 1.5 mm diameter borosilicate pipettes with tip resistances of 2–5 MΩ were pulled using a P-1000 micropipette puller (Sutter Instruments). Pipettes were filled with intracellular solution containing (in mM) 135 K-gluconate, 5 KCl, 0.5 CaClresponse . The thrRecordings from single afferent fibers of corpuscles were acquired simultaneously with patch-clamp recordings for three corpuscles, using the second channel of the MultiClamp 700B amplifier. Single-fiber recording pipettes were manufactured from thin-wall, 1.5 mm diameter borosilicate glass capillaries using a P-1000 micropipette puller (Sutter Instruments) to create tip diameters of 5–30 μm, then filled with Krebs solution. Pipettes were placed on an electrode headstage connected to a High-Speed Pressure Clamp (ALA Scientific Instruments). Light (1–20 mmHg) positive pressure was applied from the recording electrode to clear away tissue from a corpuscle-associated afferent. Negative pressure was then applied until a large section (~5 μm) of the afferent was sucked into the pipette. Extracellular afferent voltage was recording in current-clamp mode, sampled at 20 kHz and low-pass filtered at 1 kHz.Data from afferent terminals and lamellar cells were acquired from separate, individual preparations from different animals. Data were analyzed and plotted in GraphPad Prism 9.4.1  as individual data points or means ± SEM unless otherwise indicated. This fundamental work by Ziolkowski et al provides an exceptional advance in our understanding of the physiological sense of touch by directly perfoming in vivo patch-clamp recordings from vertebrate skin mechanoreceptor terminals. The provided evidence is compelling, overcoming a long-existing technical challenge and providing an experimental model to investigate the neuronal response to mechanical stimulation at the site of force detection that is of broad interest to physiologists, neuroscientists and biophysicists working on mechanoreceptors and mechanically activated ion channels. In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Decision letter after peer review:eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by a Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Mechanotransduction events at the physiological site of touch detection" for consideration by The individual reviews are provided below. Please note that the reviewers have pointed out some suggestions that may help to improve the manuscript. You may consider incorporating these suggestions to the final version for publication. Please take note of the points below and we hope you will continue to support eLife by reviewing for us and by submitting other papers going forward.Reviewer #1 (Recommendations for the authors):The manuscript by Ziolkowski et al. shows unprecedented in vivo recordings from skin mechanoreceptors. These recordings produce rapid excitatory currents (with fast activation and inactivation kinetics) when applying the mechanical stimulus (ON) and after removing it (OFF). This mechanically activated ionic current (MA) propagates through action potentials out of the terminals from a certain threshold. These action potentials are also sensitive to TTX, demonstrating a role of voltage-gated sodium channels in the process. In this study the authors overcome a long-existing technical challenge and present an experimental model to study skin mechanoreceptors. The contents seem adequate for a short report, and future work should warrant more detailed characterization of the ion channels involved in these MA currents .This short report is very well written and structured. Suggestions that may help to improve the manuscript are:– The activation and inactivation kinetics  of the MA current, both ON and OFF, is fast and very similar, although for inactivation, the MA OFF current seems to be twice as fast as the ON current ( figure 1F). However, this difference is not clearly inferred from the recordings of panel 1B. The authors could consider including an inset directly comparing zoomed/scaled MA-ON and OFF currents, so this significant difference in the inactivation kinetics is made more evident.– Figure 2E shows an example of the APs of the lamellar cell, blue line. Perhaps it would be interesting to accompany them with the AP that is generated in the afferent terminal (black line), which would be similar to what is shown in 1D. This would provide a comparison at-a-glance of some of the distinct properties of lamellar cell vs afferent terminals which are then summarized in 2G-I.Reviewer #2 (Recommendations for the authors):Overall, the experiments conducted by the authors were well designed and all claims and conclusions made were well supported by the data. Additionally, the experiments and data collected are required for a comprehensive understanding of touch detection which, up until now, has not been done. This is already a very strong manuscript with no major issues that need to be addressed. I only have one minor point to take into consideration.The manuscript would improve if the significance of the study was better explained; i.e. why are these results so fundamental for our understanding of the sense of touch?Reviewer #3 (Recommendations for the authors):This short communication builds up on the idea that most electrophysiological recordings to study mechanoreceptors and the mechanically activated current responses are performed on the dissociated neurons soma and far away from the site of action. Here the authors performed elegant recordings from afferent terminal (within the Grandry corpuscles) as well as afferent fiber (single-fiber recordings), not done before to the best of our knowledge. This strategy is useful and establishes the fundamental characteristics of mechanotransduction at the physiological site of touch/mechanical detection in the mechanosensory neurons.A few significant differences are found in the properties of mechanically activated responses recorded from lamellar cells and afferent terminal, suggesting distinct mechanism of action. Though the mechanisms are not explored in this study, this strategy provides the baseline for future electrophysiological studies for various mechanoreceptors.Another interesting aspect of this study is the presence of electrical responses during the off phase . Usually in dissociated neurons and in other in vitro studies, mechanical stimuli evoke currents during the start of the stimulus. Here the authors were able to look into the unique aspect of the current responses during the ON phase and the OFF phase. The results are summarized elegantly and the data supports the claim, with substantial discussion points.The work is complete in its current format, the data supports the claim. It's a nice sweet short paper, to the point. The authors didn't leave much room for suggestions but here are my two cents.1) A little more analysis or a line or two on the kinetics of "off and on phase". This is an important finding and can be highlighted a bit more. By the look of the traces seems like the decay of current is fitted with a single exponential. Did you try fitting with two or more?2) Action potential is recorded first time from these cell types. This point can be highlighted a bit more in the text.3) Possibility of Piezo2 as the major MA channel or an unknown mechano channel can also be discussed. Reviewer #1 (Recommendations for the authors):This short report is very well written and structured. Suggestions that may help to improve the manuscript are:– The activation and inactivation kinetics  of the MA current, both ON and OFF, is fast and very similar, although for inactivation, the MA OFF current seems to be twice as fast as the ON current ( figure 1F). However, this difference is not clearly inferred from the recordings of panel 1B. The authors could consider including an inset directly comparing zoomed/scaled MA-ON and OFF currents, so this significant difference in the inactivation kinetics is made more evident.We changed Figure 1B to a more representative trace where the slower inactivation in the ON phase compared to the OFF phase is more visually clear.– Figure 2E shows an example of the APs of the lamellar cell, blue line. Perhaps it would be interesting to accompany them with the AP that is generated in the afferent terminal (black line), which would be similar to what is shown in 1D. This would provide a comparison at-a-glance of some of the distinct properties of lamellar cell vs afferent terminals which are then summarized in 2G-I.In Figure 2E, we added an inset of afferent terminal AP response below lamellar cell AP response with added scale bar.Reviewer #2 (Recommendations for the authors):The manuscript would improve if the significance of the study was better explained; i.e. why are these results so fundamental for our understanding of the sense of touch?In abstract, we added "Until now, direct evidence of MA current and mechanically-induced action potentials in the mechanoreceptor terminal has not been obtained."Reviewer #3 (Recommendations for the authors):The work is complete in its current format, the data supports the claim. It's a nice sweet short paper, to the point. The authors didn't leave much room for suggestions but here are my two cents.1) A little more analysis or a line or two on the kinetics of "off and on phase". This is an important finding and can be highlighted a bit more. By the look of the traces seems like the decay of current is fitted with a single exponential. Did you try fitting with two or more?We used single-fit only because it was sufficient to capture the decay kinetics in our experiments.2) Action potential is recorded first time from these cell types. This point can be highlighted a bit more in the text.In abstract, we added "Until now, direct evidence of MA current and mechanically-induced action potentials in the mechanoreceptor terminal has not been obtained."3) Possibility of Piezo2 as the major MA channel or an unknown mechano channel can also be discussed.We added the following: “Though duck Piezo2 also displays fast-inactivating MA current , whether the ON phase MA current in the terminal is mediated by Piezo2 or another, unknown ion channel remains to be determined."