Multi-timescale neural adaptation underlying long-term musculoskeletal reorganization

The central nervous system (CNS) continuously adapts bodily functions in response to both external and internal challenges. Experimental models based on external perturbations, such as altered gravitational fields or distorted sensory feedback, have illuminated mechanisms of sensorimotor adaptation (Sugita, 1996; Davidson and Wolpert, 2003; Luauté et al., 2009; Fleury et al., 2019). Because the changes in the external environment can be controlled by the experimental design, these models provide an opportunity to assess how the CNS adapts to them. However, the transient and predictable nature of these changes may not fully capture the demands posed by internal, long-lasting, and unpredictable alterations to the body’s internal environment.

In contrast, the internal changes such as developmental growth (Power and Schlaggar, 2017), fatigue (Green, 1997), postural sway (Zemková, 2022), and sensory disorientation (Schärli et al., 2024) impose distinct challenges. Because identifying the source, extent, and time constant of changes in the internal environment is usually difficult, assessing the corresponding CNS adaptation is also challenging. Particularly, structural alterations to the musculoskeletal system, whether due to injury, disease, or surgery, fundamentally change the body’s biomechanics and sensorimotor associations, but the quantification of these changes is usually difficult. Accordingly, the way CNS remaps its motor control strategies corresponding to the changes is not yet well understood (Walker et al., 2004), although understanding such adaptations is crucial for elucidating the pathophysiology of motor impairments observed in chronic musculoskeletal conditions such as osteoarthritis, a degenerative joint disease (Hunter and Eckstein, 2009), and muscular dystrophy, which involves progressive muscle weakness (Mercuri and Muntoni, 2013).

To address this question, in this study, we employed a tendon transfer (TT) surgery model (Sperry, 1940), which introduces a controlled, sustained change to the musculoskeletal structure. TT surgery is a clinically well-established procedure (Gardenier et al., 2020) that surgically reattaches the tendon of a specific muscle to that of a surrounding one. This procedure relocates a specific muscle so that its contraction generates a new mechanical action and, consequently, novel somatosensory feedback. Because the internal change is controlled by the experimenter, the TT model provides a powerful platform to investigate how the CNS adapts to a new internal state. Another unique feature of the TT model is that it places permanent changes of the internal environment while leaving the CNS anatomically intact. Unlike CNS lesion models (Hoffmann et al., 2009), where the injury itself disrupts neural circuits and thereby complicates the assessment of the adaptive capacity of the CNS, TT offers a distinct advantage: it allows investigation of CNS-driven adaptation without the confounding effects of direct neural damage. TT has been used in various species, with studies suggesting different adaptive capacities, ranging from limited adaptation in adult rodents (Sperry, 1940; Sperry, 1942; Slawinska and Kasicki, 2002; Bowlus et al., 2003) to more substantial adjustments in cats (Yumiya et al., 1979; Loeb, 1999) and primates, including humans (Lee and Seung, 1999; Wester et al., 2018; Gaetz et al., 2023). Therefore, adaptability to TT appears enhanced in primates and humans. Altogether, this approach provides a controlled platform to examine how the CNS adapts to musculoskeletal changes in primates.

According to earlier reports, there are two motor control strategies that could be involved when the CNS needs to face the sensorimotor remapping posed by structural alterations to the musculoskeletal system. First, the CNS may employ modular building blocks such as muscle synergies, coordinated activations of muscle groups that reduce the dimensionality of motor control (d’Avella et al., 2003; Bizzi and Cheung, 2013). Second, skilled behaviors like fine finger movements often require fractionation, the capacity to activate muscles independently. When confronted with structural changes to the musculoskeletal system, does the CNS adapt by modulating existing synergies, or by shifting toward more fractionated control strategies? Addressing this question requires examining the evolution of neural control strategies over extended periods of recovery to capture both immediate adjustments and long-term learning.

This study aimed to identify long-term adaptive mechanisms of the primate CNS following structural changes to the musculoskeletal system. We investigated whether the CNS adapts by modulating existing muscle synergies or by altering the fractionated control of muscles specifically affected by the surgical alteration. We surgically altered the limb structure by performing crossed TTs of finger extensor and flexor muscle tendons. Using a trained finger-grasping task as our behavioral readout, we examined how the CNS recalibrates muscle activity to regain skilled motor function. Our findings provide new insights into the dynamic reorganization of motor control following structural changes of the body.

In this study, we developed a novel crossed TT animal model. This involved surgically swapping the tendons of two antagonistic finger muscles in a macaque monkeys’ forearm (Figure 1; a complete list of all muscle abbreviations, their full names, and their assigned synergies for each monkey is provided in Supplementary file 1A). The effectiveness and consistency of the surgery was confirmed by measuring: (i) the distance traveled by muscle fibers and their intramuscular tendons in the transferred muscle (Figure 2B and C) and (ii) the amount of fingertip displacement (Figure 2D and E). Both measurements were induced by percutaneous electrical stimulation over the transferred muscles (Figure 2A). For a detailed description of the results of these measurements, see the Methods section. The two monkeys were assigned slightly different tasks, allowing us to examine controlled grasping in Monkey A and a more natural grasp in Monkey B (refer to the Methods section and Figures 3 and 4 for further details). The two monkeys ultimately performed related but distinct grasping tasks, a methodological divergence that provided a valuable opportunity to test the generality of the core adaptive mechanisms. Monkey A performed a controlled grasping task requiring a fine precision grip, designed to study adaptation of fine motor control (Figure 3). While the same task was initially planned for both animals, Monkey B performed this task inconsistently, frequently varying its grip strategy, and we could not reinforce this monkey to perform a single strategy consistently.

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Therefore, to ensure reliable task engagement and in accordance with the ethical principle of Reduction and Refinement (the ‘3Rs’; Tannenbaum and Bennett, 2015), the task for Monkey B was modified to a more naturalistic food retrieval grasp that the animal performed consistently (Figure 4).

Once the monkeys had mastered their respective tasks, we recorded control sessions and then performed the crossed TT. Post-surgery, and once the monkeys had fully recovered and were able to perform the task independently (~3–4 weeks), electromyography (EMG) recordings and behavior were resumed (Videos 1–7).

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Functional recovery follows a biphasic trajectory

Despite the now reversed roles of the transferred muscles, the monkeys were able to recover their grasping performance within 2 months, assessed by both the return of key behavioral metrics to pre-surgical levels (Figure 5A and D) and their consistent ability to perform the task independently (Videos 3, 4, and 7). However, this recovery was not immediate. The monkeys spent a period of 1–2 weeks in their home cages for recovery, followed by several weeks of assisted task practice. Formal recordings began once the monkeys could perform the task consistently without assistance. The initial post-surgical period was defined by a phase of significant motor impairment, primarily characterized by off-target reaching trajectories (Figure 5B and E) and prolonged grip formation times (Figure 5A and D). These off-target reaching movements included inefficient, ‘explorative’ trajectories in Monkey A and target overshoots in Monkey B (Videos 2, 3, and 6). This recovery process was quantified across several behavioral and kinematic metrics (Figure 5). A comparison of pre-TT behavioral variability on the grip formation task (Figure 5D) revealed that Monkey B was substantially more variable (coefficient of variation [CV] = 81.93%) than Monkey A (CV = 46.62%). This difference in variance was highly statistically significant (Ansari-Bradley test, p<0.0001), confirming a difference in baseline stability between the subjects likely due to the less constrained nature of Monkey B’s task. Post-surgery, variability in grip formation time initially increased dramatically for Monkey A (early phase CV = 132.98%), while remaining high for Monkey B (early phase CV = 76.46%). In the late phase, Monkey A’s variability stabilized slightly above baseline (late phase CV = 54.79%), whereas Monkey B’s variability increased further, exceeding its pre-TT level (late phase CV = 96.59%), suggesting persistent inconsistency in grasp timing for this animal. Variability patterns differed for the other metrics: Monkey A’s pull time variability decreased below baseline in the mid and late phases (pre CV = 64.97%, mid CV = 41.30%, late CV = 43.01%), indicating a refinement of this action, while Monkey B’s grasp aperture variability remained consistently low throughout recovery (pre CV = 26.37%, early CV = 23.80%, mid CV = 19.78%, late CV = 19.35%). The initial post-surgical period was defined by motor performance that we classify as maladaptive due to the persistence of inefficient, off-target reaching, and prolonged movement durations. First, task-related grip formation times were significantly longer immediately post-TT (Monkey A: 197.7±92.2 vs. 660.6±221 ms, p=0.014; Monkey B: 169.7±14 vs. 316.3±31.9 ms, p=0.016), taking approximately 40 days for Monkey A and 48 days for Monkey B to return to and stabilize at pre-surgical levels (Figure 5A and D). Second, the duration of off-target reaching was substantially elevated, stabilizing approximately after 70 days in Monkey A and 55 days in Monkey B (Figure 5B and E). Following this phase, motor performance showed a gradual recovery in efficiency (Figure 5C) and kinematics (Figure 5F). Ultimately, overcoming these initial maladaptive behaviors through the gradual refinement of motor performance led to the stabilization of grasping by the end of the experimental period.

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A two-phase adaptation is observed in the EMG activity of individual muscles

Initially, we investigated whether EMG activity exhibited any changes post-TT. Given that we interchanged the tendons of an antagonistic muscle pair, functionally rendering the flexor digitorum superficialis (FDS) an extensor and the extensor digitorum communis (EDC) a flexor (as confirmed mechanically, Figure 2), a biomechanically sound adaptation would be to use the former finger extensor (EDC) for finger flexion, and the former finger flexor (FDS) for finger extension during a grasping task. We therefore analyzed EMG activity from both transferred and non-transferred muscles to determine if these expected functional changes occurred. Figure 6 shows a comparison of EMG activity profiles for the two transferred muscles, EDC and FDS, in Monkey A (Figure 6A–D). Prior to surgery, the control showed distinct and contrasting EMG profiles in both muscles. For instance, peak activity in EDC (▼) occurred long after completion of the grasping action (indicated by vertical dashed lines at 0% task range) at 15% task time (Figure 6A), coinciding with the animal pre-shaping its hand to prepare for the subsequent grasp, whereas peak activity in FDS (▽) was observed immediately after completion of the grasping action (0.49%; Figure 6C). The question we posed was whether the post-surgery activity of EDC resembled its original profile (without adaptation to surgery) or the activity of FDS (the anticipated profile following surgical adaptation), and vice versa. Our findings suggested that EMG activity patterns largely shifted in a manner consistent with the new mechanical function imposed by the transfer. Early after TT, peak activity of EDC occurred at –0.58% task time (Figure 6B; days 29 [red line] and 64 [orange]), which aligns with peak activity of FDS prior to TT (Figure 6C; black line). Also, for FDS, peak activity occurred at 11% task time (Figure 6D; ▼), which aligned with peak activity of EDC (Figure 6A).

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This was corroborated by the results in Monkey B (Figure 6, panels K–M). Although the EMG patterns of both muscles varied significantly as the two monkeys performed different types of grasping actions, we found that the EMG of EDC peaked at –6.12% before (Figure 6K; ▼) and 0.35% and 1.05% after TT (Figure 6L; ▽, days 22 and 36, respectively), closely matching the timing for FDS control data (Figure 6M). The corresponding analysis for FDS could not be performed in Monkey B, since the EMG signal of FDS was lost early after TT (see Methods for details).

We were able to extend our recordings to 122 and 64 days post-TT for Monkeys A and B, respectively. This allowed us to examine whether the adaptations observed in the early period were consistent throughout the experimental period. These additional days (Figure 6B and D; 69 [cyan], 79 [blue], and 99 [black] post-TT; see Figure 6—figure supplement 1 for recordings from all days) showed that the EMG activity profiles for both muscles unexpectedly returned to their pre-TT state. For example, as already described, the EMG signal of EDC at days 29 and 64 shared the characteristics of FDS, but by the following day (only 5 days later, day 69 [light blue] post-TT) already exhibited pre-TT characteristics of EDC, continuing as such until the end of the recording period (day 79 [blue] and 99 [black]). This was confirmed for the EDC muscle of Monkey B (Figure 6K and L; Figure 6—figure supplement 1U–AO for recordings from all days).

To relate these neural changes to functional recovery, we overlaid the behavioral error metric (off-target reaching time; gray dashed lines) onto the cross-correlation plots (Figure 6G–J). Figure 6H (red line) illustrates cross-correlation coefficients between the EDC EMG profile post-TT (EDC-post) and the original EDC control data (EDC-pre) in Monkey A. Figure 6I shows coefficients for EDC-post and FDS-pre correlations in the same monkey. The coefficient corresponding to the original profile decreased to between –0.3 and 0.2 over approximately 65 days (Figure 6H). In contrast, the coefficient corresponding to the expected EMG profile exhibited an inverse pattern, with low coefficients pre-TT and a high coefficient (of 0.9) post-TT (Figure 6J). Cross-correlation analysis for FDS (Figure 6G and I) revealed a similar pattern. Cross-correlation coefficients for FDS-pre- vs. FDS-post analysis declined from 1 to negative values, only to rebound to their original values within a month (day 79; Figure 6G). This outcome aligns with our previous observations (Figure 6B and D) and were corroborated by the results from Monkey B (Figure 6K–Q). Here, cross-correlation coefficients dropped from +0.95 to +0.1 for EDC-pre vs. EDC-post comparison, and increased from –0.5 to 0.9 for the FDS-pre vs. EDC-post comparison (Figure 6P and Q). After 42 days post-TT, cross-correlation coefficients started to gradually increase or decrease to +0.9 and –0.7, respectively. This indicates that the anticipated initial changes in EMG activity profiles are not permanent. Instead, they revert to their original patterns 2 months post-TT. A supplementary analysis of the time lag at peak correlation (Figure 6—figure supplement 1) confirmed that the optimal lag for individual muscles also fluctuated significantly during the early adaptive phase before stabilizing, reflecting the process of temporal re-coordination of individual muscle commands. Interestingly, changes in EMG pattern were not isolated to the transferred muscles; non-transferred muscles exhibited a variety of complex adaptations (Figure 6, Figure 6—figure supplements 2–4). A comprehensive overview of the average EMG profiles for all recorded muscles across all recording sessions and landmark days is provided in Figure 6—figure supplement 2 (Monkey A) and Figure 6—figure supplement 3 (Monkey B), respectively. As shown in these figures, many agonists adapted in concert with their transferred synergist, following the same two-phase ‘swap-and-revert’ pattern seen in Figure 6. In Monkey A, these included extensors ED23 and ECU, while in Monkey B, they included ED23, ED45, and ECU (see Figure 6—figure supplement 2 for details). In contrast, other muscles showed patterns that were incompatible with a simple swap. For example, the non-transferred flexor carpi radialis (FCR) in Monkey A showed a distinct adaptive profile characterized by a drastic initial decrease in one activity peak and a consistent increase in a later peak (Figure 6E and F). A similar incompatible pattern was seen in palmaris longus (PL) (Figure 6—figure supplement 1H, I, R, S). Finally, some muscles, like the extensor carpi radialis (ECR) in Monkey B, remained relatively stable post-surgery (Figure 6N and O). These diverse patterns in non-transferred muscles do not necessarily contradict a modular control strategy (d’Avella et al., 2003; Bizzi and Cheung, 2013); rather, they likely reflect the fact that different muscles are primary members of different synergies (i.e. they possess the highest weightings within specific modules; e.g. FCR and PL in Synergy C), each undergoing its own task-specific adaptation.

Adaptation occurs through modulating the activation of stable muscle synergies

To determine whether the CNS adapted by fractionating muscle control or modulating existing modules, we extracted muscle synergies using non-negative matrix factorization (NMF) (Lee and Seung, 1999). Four synergies accounted for >80% (Cheung et al., 2012) of the variance in both monkeys (see Figure 7—figure supplement 1 for VAF plots; >85% for both monkeys; the results of the original and shuffled datasets are shown. See also the synergy weights [W] and activation profiles [C] for both monkeys and all synergies in Figure 7—figure supplements 2 and 3, respectively). We first focused on the two primary synergies responsible for the main task axis: the finger flexor synergy (Synergy A) and the finger extensor synergy (Synergy B) (Figure 7).

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We first analyzed whether the spatial structure of these primary synergies changed. To distinguish genuine adaptation from natural variability, we established a baseline by calculating the cosine similarity of spatial synergy weights (W) across all pre-surgery days (Figure 7I and J, blue traces). This revealed remarkably high stability (>0.99). Post-surgery, the core structure remained conserved. The cosine distance between pre- and post-surgery weights for the same synergy pairs was low (Monkey A: 0.03±0.03; Monkey B: 0.09±0.11), whereas for different pairs it was high (>0.60). This confirms that the CNS maintained the stable, neurally constrained building blocks of the primary flexor and extensor modules despite the altered biomechanics.

In contrast to the stable spatial structure, the temporal activation profiles of these primary synergies underwent drastic changes. In Monkey A, the flexor Synergy A (originally peaking at +0.97% task time) and extensor Synergy B (originally peaking at 12.62%) swapped their timing profiles shortly after surgery (Figure 7C and D). Specifically, the extensor synergy shifted to peak during the flexion phase and vice versa. This ‘swap’ persisted during the early impairment phase before reverting toward the original timing in the late phase. A similar ‘swap-and-revert’ pattern was observed in Monkey B (Figure 7G and H), where the extensor Synergy B shifted from a pre-surgery peak at –8.75% to a post-surgery peak of +3.5% (matching the flexor phase) before recovering.

To formally quantify the quality of this reversion, we compared the pre-surgery activation profiles with those from the final day of recording. We found that while the temporal shape was highly preserved (cosine similarity>0.90; Supplementary file 1B), the profiles remained statistically distinct (permutation test, p<0.0001; Figure 7—figure supplement 4). This confirms that the recovered motor program represents a ‘good enough’ functional approximation rather than a perfect mathematical restoration of the baseline.

We next examined the secondary synergies involved in wrist control and stabilization (Figure 8). In contrast to the coherent ‘swap’ seen in the primary synergies, these modules exhibited distinct, task-specific adaptations.

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In Monkey A, Synergy C (a wrist flexor synergy involving PL and FCR) showed a temporal shift that was incompatible with a simple swap (Figure 8C). While its spatial weights remained stable (Figure 8A and I), its activation peak shifted from the post-grasp phase (+1.46%) to the pre-grasp phase, increasing in magnitude over time. This pattern corresponds to the recruitment of wrist flexors to enable the compensatory tenodesis grasp. Synergy D (wrist extensor) underwent a single notable change on day 29 but quickly reverted (Figure 8D). In Monkey B, Synergy C showed no discernible trend (Figure 8G), while Synergy D exhibited a complex pattern of immediate change followed by a gradual shift in the late adaptation period (Figure 8H). Notably, the activation amplitude of Synergy D remained significantly suppressed compared to baseline (Figure 8H; Figure 7—figure supplement 2H). This suppression of the wrist extensor likely reflects the specific biomechanical requirements of the tenodesis strategy employed by Monkey B: relaxing the wrist extensors facilitates the necessary wrist flexion for hand opening. These independent changes in secondary synergies likely served to reinforce the overall functional recovery driven by the primary modules.

Again, the characteristics of these changes in activation profiles of muscle synergies were also quantitatively confirmed by cross-correlation analysis of all four muscle synergies in both monkeys. The coefficients were plotted over the course of experimental days in relation to tendon surgery together with the overlaid behavioral error metric (off-target reaching time; gray dashed lines) (Figure 9). In this analysis, the activation profile of each synergy was cross-correlated with either the one from synergy A (finger flexor synergy; Figure 9C, D, G, and H) or synergy B (extensor synergy; Figure 9A, B, E, and F) before TT. As previously demonstrated for the FDS and EDC muscles, the temporal activation profiles of muscle Synergies A and B (flexor and extensor) displayed a distinct pattern. After cross-correlation with their own control data, both the flexor and extensor synergies showed coefficients of 1 prior to surgery. These became negative for 66 days post-surgery before reverting close to their original values (Figure 9B and C). However, after cross-correlation with control data of the antagonistic muscle synergy (i.e. extensor for flexor synergy and vice versa, Figure 9A and D), the pattern was reversed. Coefficients started with negative values prior to TT and shifted to positive coefficients near 1 shortly after surgery. In Monkey A, coefficients returned to negative values around day 66 post-TT. In Monkey B, this reversal occurred earlier, around day 48. In short, these results mirror our findings between the transferred muscles (Figure 6G and J). Furthermore, to determine if low correlations were driven by simple timing mismatches, we analyzed the optimal time lag yielding the maximum cross-correlation (Figure 9—figure supplement 1). A positive lag indicates the post-surgery activity is delayed relative to baseline, while a negative lag indicates it is advanced. This analysis revealed significant fluctuations in timing (switching between delays and advances) during the early and mid-adaptation phases, particularly around the ‘switch-back’ period, before stabilizing closer to zero lag in the late phase. For Synergies C (main contributing muscles: ECR, BRD, DEL) and D (main contributing muscles: ECU), the changes in cross-correlation coefficients over time were markedly different. For Synergy C, coefficients begin to steadily increase when cross-correlated with control data of the extensor synergy (Synergy B, Figure 10E) and decrease with the flexor synergy (Synergy A, Figure 10G). This suggests an increasing contribution to finger extension. In contrast, Synergy D does not exhibit any specific pattern following TT.

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In Monkey B, this cross-correlation analysis revealed a more varied, or differential, pattern of adaptation across the four synergies (Figures 9 and 10). The primary extensor, Synergy B, mirrored the results in Monkey A, showing a clear swap-and-revert pattern (Figure 9F and H). In contrast, Synergy A did not show a clear reversal; its correlation coefficients gradually converged toward zero, likely due to the absence of the FDS EMG signal in the analysis (Figure 9E and G). The secondary synergies, Synergies C and D, showed no discernible trend (Figure 10E–H).

In summary, both monkeys exhibited a distinct two-phase adaptation following TT. We define the ‘early phase’ as the period from initial post-surgical recovery up to the reversal of the swapped activation patterns (approximately days 20/29 to ~65 post-TT, see Figure 9), characterized by the transferred muscles/synergies adopting antagonistic temporal profiles. We also identify a ‘mid-adaptation’ phase covering the transitional period around the ‘switch-back’ event, characterized by high variability. The ‘late phase’ encompasses the period following this switch-back (after ~day 66 post-TT), where original activation timings were largely restored. While this ‘reversion’ toward original activation timings in the late phase marked the abandonment of the initial maladaptive strategy, behavioral recovery involved more than just restoring original patterns. Concurrent with this neural shift in the primary synergies, a distinct compensatory strategy began to emerge, utilizing secondary synergies and biomechanical coupling to achieve functional hand opening, as detailed below.

The early adaptation phase is characterized by a maladaptive neural and behavioral profile

The early adaptation phase was defined by a distinct neural strategy that correlated with significant behavioral deficits. First, the period dominated by off-target reaching movements and prolonged grip times (Figure 5B and E) was precisely when the swapped activation of Synergies A and B was most prominent (Figure 9; see behavioral overlay). This temporal link provides strong evidence that this initial ‘swap’ strategy was, in fact, maladaptive, as the flawed neural control directly underpinned the impairments in movement efficiency and precision.

Second, we found that the net activity of muscles representing certain muscle synergies (aggregated average EMG [aaEMG]) showed distinct, synergy-specific changes over time (Figure 11). A key feature was the evolution of the activity of finger extensor synergy (Synergy B), which appeared to undergo a neuromuscular ‘arms race’, a rapid escalation of antagonistic co-activation, in both animals, albeit on different timescales (Figure 11B and J). In Monkey A, which struggled for a longer period to regain behavioral proficiency according to our subjective evaluation, our recordings now captured this process unfolding: we observed a steady and significant increase in Synergy B’s aaEMG post-TT until day 64 (p<0.0001) before the strategy changed (Figure 11B and F). In contrast, Monkey B, which adapted its behavior more rapidly according to our subjective evaluation, showed a different profile; its Synergy B activity peaked with a significant surge early in the recording period (day 22: p<0.0001) and subsequently declined toward and below baseline (day 64: p<0.001) (Figure 11J and N). This suggests the initial, rapid escalation of the ‘arms race’ in this animal had already occurred and peaked prior to our first postoperative recording session. Therefore, the apparent discrepancy in the evolution of Synergy B is likely not a fundamental difference in strategy, but rather a reflection of the different observational windows and overall adaptation rates of the two animals.

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For the finger flexor synergy (Synergy A), Monkey A showed a general decrease in activity (p=0.0004 at day 79; Figure 11A and E), while Monkey B showed a consistent and significant increase throughout the experiment (day 22: p=0.0004; day 64: p=0.0008). This was expected, as Monkey B’s Synergy A relied on FDP and PL to compensate for the loss of FDS function (Figure 11I and M). Synergy C exhibited a general increase in activity during the post-TT phase in both monkeys (Figure 11G and O), while Synergy D showed no significant differences in Monkey B and only minor changes in Monkey A (Figure 11H and P). All statistical comparisons were made against the pre-TT control period using a two-sample t-test with Bonferroni correction (α=0.01).

Taken together, these distinct patterns of aggregated EMG activity, especially the escalating co-activation within the conflicted Synergy B, a known marker of inefficient muscle recruitment (Thoroughman and Shadmehr, 1999; Osu et al., 2002), further illustrate that this early adaptive phase, which coincided with poor behavioral performance (Figure 5), was characterized by an unstable and inefficient neural control strategy.

Distinct neural implementations of a compensatory tenodesis strategy

The final phase of adaptation was characterized by the development of a compensatory tenodesis strategy. This effect describes the passive coupling of finger and wrist movements due to the routing of tendons across multiple joints (Zajac, 1992): because the finger extensor tendons pass over the back of the wrist, actively flexing the wrist passively tightens these tendons, which in turn pulls the fingers into extension (Valero-Cuevas and Hentz, 2002; Cash and Jones, 2011). For Monkey B, we found direct kinematic evidence for the acquisition of this strategy. Post-surgery, the monkey learned to significantly increase finger extension at the metacarpophalangeal (MCP) joint (Figure 12A) by concurrently flexing the wrist (Figure 12B; p<0.0001, ANOVA; α=0.01). This coordinated movement pattern is characteristic of an active tenodesis effect. Furthermore, kinematic analysis revealed that this was a gradually learned skill rather than an immediate mechanical consequence. The trial-by-trial coupling between wrist angle and MCP angle (Figure 13) was initially absent (R²=0.00) but strengthened over weeks, peaking in the mid-adaptation phase (R²=0.58). This period coincided with the stabilization of grasp aperture (Figure 5F) and the resolution of the maladaptive neural patterns (Figure 9). This kinematic strategy was supported by a precise neural implementation. Unlike the scaling strategy observed in Monkey A (see below), Monkey B did not rely on a massive increase in total muscle activity; the aggregated activation of its primary flexor synergy (Synergy A) showed consistent but moderate increases (Figure 11I and M). Instead, the strategy was one of temporal refinement. The activation profile of Synergy A shifted to increase specifically during the pre-contact phase (Figure 7G), providing the necessary wrist flexion for the tenodesis grasp. This was achieved by a precise sequential activation of muscles within the synergy, with the wrist flexor component (PL) peaking just before contact and the finger flexor component (FDP) peaking just after (Figure 6—figure supplement 2). For Monkey A, which successfully restored its primary extensor synergy, the tenodesis grasp likely served as a similar compensatory driver. While we could not perform a kinematic analysis for this animal due to low-resolution video images, strong evidence for this strategy is provided by the neural data. We observed a clear temporal shift in the activation of its dedicated wrist flexor synergy (Synergy C). The peak of this synergy’s activation moved from occurring just after object contact to just before it (Figure 8C), a re-timing well-suited to enable a tenodesis grasp. This increasing contribution is further supported by cross-correlation analysis, which shows its activation pattern became progressively more similar to that of the pre-TT extensor synergy over time (Figure 10A; p<0.05, two-sample t-test).

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Figure 13

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The CNS’ response to profound musculoskeletal change is a fundamental problem in motor control. This study sought to determine whether the primate CNS adapts to such change by flexibly modulating stable muscle synergies or by developing more fractionated, independent muscle control. We found that the CNS initially defaulted to a modular strategy, repurposing entire synergies by swapping their activation timings. This simple solution, however, proved to be maladaptive, creating a mechanical conflict that impaired motor function under TT surgery used in this study. This early maladaptive phase was ultimately resolved through the gradual development of compensatory movements, leading to a ‘good enough’ functional recovery. This multi-stage process, operating on different timescales, highlights the intricate balance between modularity and flexibility in neural adaptation.

A multi-timescale model reconciles smooth behavioral recovery and abrupt neural reorganization

The apparent paradox between the smooth, gradual recovery of motor function and the abrupt, switch-like reorganization of the underlying neural control strategy suggests that adaptation is not a single process. Instead, we propose it results from the interaction of at least two distinct adaptive systems operating in parallel on different timescales, a concept aligned with established two-state models of motor learning (Smith et al., 2006a; Trewartha et al., 2014).

We map the gradual acquisition of the compensatory tenodesis strategy to the slow process of this model. This iterative component is responsible for building the robust, stable motor skills necessary for long-term recovery. In contrast, we propose that the fast process drives the initial ‘swap' strategy and its subsequent abandonment. This system appears to be sensitive to immediate performance errors and metabolic costs, triggering the rapid ‘switch-back’ to the original synergy timings (recalibration) once the initial strategy proves inefficient.

The critical insight from our data lies in the interaction between these timescales. We propose that the slow system effectively ‘gates’ the fast system: the abrupt neural reorganization (the switch-back) is likely not an independent event, but a transition enabled only when the slow learning (tenodesis) reaches a ‘good enough’ threshold to support function. This hierarchical interaction resolves the tension between the smooth behavioral curve and the sharp neural transition, ensuring a stable long-term motor plan (Figure 14).

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All data and custom MATLAB code used to generate the figures in this study are available at the GitHub repository: https://github.com/animalmodel/Philipp_eLife_2025 (copy archived at Philipp and Kosugi, 2026). For long-term preservation, the code and dataset (including large EMG and Synergy matrices) have been archived at Zenodo (DOI: https://doi.org/10.5281/zenodo.18030926).

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Author details

1. Roland Philipp

   1. National Center of Neurology and Psychiatry, Department of Neurophysiology, Tokyo, Japan
   2. University of ElectroCommunications, Graduate School of Informatics and Engineering, Department of Mechanical and Intelligent Systems Engineering, Tokyo, Japan

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   roland@ncnp.go.jp

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5850-7539

2. Yuki Hara

   National Center of Neurology and Psychiatry, Department of Orthopaedic Surgery, Tokyo, Japan

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7378-4975

3. Naohito Ohta

   1. National Center of Neurology and Psychiatry, Department of Neurophysiology, Tokyo, Japan
   2. University of ElectroCommunications, Graduate School of Informatics and Engineering, Department of Mechanical and Intelligent Systems Engineering, Tokyo, Japan

   Contribution

   Software, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

4. Naoki Uchida

   1. National Center of Neurology and Psychiatry, Department of Neurophysiology, Tokyo, Japan
   2. University of ElectroCommunications, Graduate School of Informatics and Engineering, Department of Mechanical and Intelligent Systems Engineering, Tokyo, Japan

   Contribution

   Software, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Tomomichi Oya

   1. National Center of Neurology and Psychiatry, Department of Neurophysiology, Tokyo, Japan
   2. Western Institute for Neuroscience, University of Western Ontario, London, Canada

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

6. Tetsuro Funato

   University of ElectroCommunications, Graduate School of Informatics and Engineering, Department of Mechanical and Intelligent Systems Engineering, Tokyo, Japan

   Contribution

   Conceptualization, Software, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2964-5227

7. Kazuhiko Seki

   National Center of Neurology and Psychiatry, Department of Neurophysiology, Tokyo, Japan

   Contribution

   Conceptualization, Supervision, Funding acquisition, Investigation, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   seki@ncnp.go.jp

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4262-9590

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