Global remapping of the sensory homunculus emerges early in childhood development

Domain: chemistry
Published: 2026-02-24
Editorial outcome: minor_revision

Abstract
Some of the most dramatic examples of neuroplasticity in the human brain follow congenital sensory deprivation, yet the plasticity mechanisms producing this large-scale cortical remapping remain poorly understood. Congenital malformation of the upper-limb provides a unique temporal dissociation of developmental plasticity mechanisms: While sensory deprivation from the absent hand is triggered before birth, compensatory motor behaviours develop gradually throughout childhood. Using paediatric neuroimaging and semi-ecological behavioural analysis in children (5-7 years old) and adults (>25 years old) with unilateral upper-limb congenital limb difference, we studied deprivation- and use-dependent plasticity in the deprived primary somatosensory cortex and beyond. We reveal that global remapping, encompassing the entire sensory homunculus, is established early and maintained in adulthood. Modelling indicates that deprivation-driven homeostatic plasticity can account for this global remapping. Hebbian-based compensatory learning further contributes to the magnitude of inter-individual differences observed at both childhood and adulthood. Our findings emphasise the early establishment and stability of cortical maps, despite extensive daily-life behavioural adaptation.

Introduction
The brain’s ability to adapt to unique sensory and motor experiences remains one of the most compelling mechanisms within neuroscience. Particularly striking illustrations of brain remapping are apparent in individuals born with different abilities, such as congenital blindness or upper limb differences, providing naturalistic paradigms through which we can study the capacity for plasticity. In congenitally blind individuals, for example, in the absence of visual input, the visual cortex shows enhanced activity for tactile1,2 and auditory stimuli3,4. Similarly, individuals born with upper limb difference exhibit increased activity in response to movements of body parts such as the lower face, residual arm and feet, in their deprived (missing) sensorimotor hand cortex5–10. This extensive remapping of multiple body parts into the deprived cortex has been considered to reflect the brain’s intrinsic ability to repurpose its architecture in response to developmental variations11. Traditionally, these striking differences in sensory maps were attributed to the deprivation of neurotypical sensory input12, which triggers intrinsic homeostatic plasticity mechanisms13. The primary function of homeostatic plasticity is to provide stabilising mechanisms that regulate changes in the system, preventing activity levels from becoming excessively high or low14. One such homeostatic mechanism is synaptic scaling: When there is a decrease in firing rate, the synaptic weights of the excitatory post-synapses on a cell are scaled up by a multiplicative factor, preserving the relative weights of the synapses. Within this framework, due to the underlying topography of the sensory cortex, inputs neighbouring the deprived cortex may have the physiological advantage to dominate the cortical territory of the missing input15.

A second important facilitator for brain plasticity is the process of tuning receptive fields by Hebbian learning16, which is thought to be experience-driven. Let us consider the lived experience of those growing up with one hand. For example, imagine a child, born with a single hand, encountering the challenge of opening a jar to reach a treat inside. Lacking the conventional approach afforded by two hands, the child embarks on a journey of exploration and adaptation. The brain orchestrates a series of trials—perhaps stabilising the jar between the knees or pressing it to the torso with the residual limb, maybe even using the mouth to pull the lid. This drive to adapt and overcome, fuelled by necessity and reward, exemplifies what we define as ‘behavioural pressure’. This pressure is thought to be particularly potent in moulding changes in neural organisation and function during early (‘sensitive’) periods of development17–19. It has been speculated that these extraordinary behavioural shifts in individuals with different abilities guide (or are guided by) brain reorganisation11. For example, it has been suggested that the increased activity found in the deprived hand cortex for multiple other body parts reflects compensatory strategies in one-handed individuals5,9,20. This idea was instrumental in explaining why inputs of body parts that are not neighbouring the deprived hand cortex, such as the feet, could benefit from increased activity in the deprived cortex. This theory was further propelled by studies demonstrating that single-pulse TMS to the deprived hand M1 area of congenital limb difference (CLD) individuals elicited evoked responses in the feet7,21 and lower face22, demonstrating that the missing hand cortex supports motor control of other body parts that are not necessarily cortical neighbours. While conceptually compelling, previous studies struggled to find a one-to-one relationship between brain remapping and behaviour, e.g. on an individual level5,23.

Revisiting the extreme examples where cortical maps diverge significantly from their neurotypical protoarchitecture, it is reasonable to expect a collaborative process between homeostatic and Hebbian plasticity15. In scenarios where developmental anomalies occur, such as the underdevelopment of a hand, residual inputs from other body parts, normally masked or not functionally relevant to the hand area, become potentiated. This unmasking is likely intensified by homeostatic plasticity processes, ensuring neural equilibrium in response to the altered sensory input14. Subsequently, behavioural demands, interwoven with top-down directive influences from higher brain functions, modulate these inputs to produce context-dependent responses—which are therefore behaviourally relevant. Paradoxically, this dynamic interplay between brain and behaviour may also explain why previous studies looking at links between adaptive behaviour and brain remapping in adults have failed to find direct correlations. It is because the behaviour most relevant for shaping brain remapping has likely occurred in earlier life, which has since altered through adulthood. Interestingly, this mechanism calls for the prediction that, given a change in adaptive behaviour between children and adults, children might display different cortical body maps than adults.

Yet, recent studies challenge this view by showing that the perinatal sensory cortex is already relatively mature. For example, a recent study in macaque monkeys has shown that, despite behavioural improvements in texture perception during early life, the neural features in areas V1, V2, V4 and inferotemporal cortex (IT) remain stable from the earliest ages tested24 (see ref. 25 for related results in auditory cortex). Another study, examining body maps in developing monkeys, found that sensory hand maps are already present at birth and remain relatively stable even in visually impaired monkeys relying more heavily on tactile interactions26. These and other studies27,28 indicate that impressive changes in early sensory experiences do not necessarily drive extraordinary changes in sensory cortical organisation. Instead, the neural representation in these sensory areas may be established much earlier than previously thought, suggesting a fundamental protoarchitecture present from birth. This perspective will predict that any brain changes observed in adults with congenital deprivation would already be established in their early life and resilient to behavioural changes.

We wanted to disentangle some of the mechanisms that will ultimately determine the distinct topography in adults with one missing hand due to a developmental difference, namely, deprivation and adaptive behaviour. We recruited people born with one missing hand due to a unilateral upper limb malformation (hereafter, congenital limb difference, or CLD). We studied their brain and behaviour in two age groups––children (age range 5–7 years; n=16), and adults (age range 25–61 years; n=16), and age-matched participants with neurotypical development (hereafter the control group, or CTR; 21 children and 16 adults). Inspired by mounting evidence that suggests a greater capacity for reorganisation in mid-level sensory cortex11, we focused on higher-level S1 (Brodmann areas 1 and 2). We asked if behaviour and brain differences between children and adults with CLD are uniquely distinct from developmental differences observed in neurotypical development, and whether these brain and behavioural changes are linked. In other words, we asked whether differences in body topography found in the CLD population could be attributed to behavioural-dominant (Hebbian plasticity) changes, in addition to deprivation-related (Homeostatic) mechanisms, or simply age (development).

We predicted that children would display a more extensive behavioural repertoire than adults, and that this would be particularly relevant for compensatory strategies in children with CLD. We further predicted that this more diverse behaviour will cause greater expression of those body parts in CLD children’s S1 versus both control children and CLD adults. We finally predicted that the cortical body maps of adults with CLD could be linked with the CLD children’s behaviour.

Results
Greater behavioural diversity in children with upper limb differences
We first examined the behavioural compensatory adaptations by CLD children and adults in a task designed to mimic real-life object manipulation. We used a custom-designed battery of semi-ecological tasks, which would largely be completed using two hands in the typical population. Tasks, completed in a fixed order while sitting on a mat, included pulling a lid off a tin, unscrewing a nut from a small wooden bolt, and separating Lego bricks (for a complete list of the 15 tasks, see Supplementary Table 1). Behaviour was video recorded and analysed offline to ascertain which body parts were engaged and the proportion of time each body part was used during each of the sub-tasks (Fig. S1).

We first calculated ‘co-use’ for each sub-task, which takes into account how many body parts were used while also considering how consistently they were used. For example, while CTR participants tend to use both hands to open a tin box, CLD individuals might stabilise the tin with their legs, torso, residual arm (or any combination of these body parts), while lifting the lid with their intact hand. A higher index means more body parts are reliably used. A lower index means either fewer body parts are used, or the usage is uneven across the body parts. The average co-use indices are presented across groups in Fig. 1A. For control adults, the index averages just over 2, indicating they mainly completed the tasks bimanually. For all other groups, the indices are higher, hinting at more complex incorporation of additional body parts. CLD children showed values close to 3, meaning they incorporated, on average, nearly 3 different body parts to complete the tasks. Here, we found a significant interaction between age and limb difference (F 1,60.04 =8.08, p=0.006), alongside main effects for each of these factors (F 1, 60.03 =49.94, p<0.001 for age; F 1, 60.04 =128.19, p<0.001 for limb difference). The results provide evidence that physically more diverse compensatory behaviours are present in CLD children relative to both control children (t 60.10 =10.84, p<0.001) and CLD adults (t 60.10 =−6.96, p<0.001).

Further analysis centred on the proportion of time each of the different body parts was used across the sub-tasks. The average values across the sub-tasks are presented in Fig. 1B for each of the four study groups. As expected, the intact hand (or dominant hand in controls) was universally engaged, with no significant differences across groups. The omnibus analysis, which combined all body parts tested for non-manual compensatory purposes (i.e. excluding the intact hand), revealed a significant interaction between age and limb difference (F 1,60.08 =6.28, p=0.015). This interaction shows that CLD children behave differently from the CLD adults, even after we account for overall developmental effects. The omnibus analysis also revealed main effects of age, limb difference and body part (all p≤0.001, F>34.31) (see Supplementary Table 3 for full results of the mixed models). It further identified interactions between body part and age (F 4625.23 =12.80, p<0.001), meaning that different body parts are utilised more by children than adults; and an interaction between body part and limb difference (F 4625.22 =156.50, p<0.001), which could be explained by the inclusion of the residual/non-dominant hand, which was under-used by the CLD population, relative to controls.

When analysing compensatory behaviours for each of the different body parts, we found that CLD relied most on their residual arm. While there were no differences between CLD children and adults in terms of frequency of residual arm use (t 544.99 =1.55, p=0.121), both used the residual arm significantly less than control participants used their non-dominant hand (F 1,600.33 =104.34, p<0.001; main effect of limb difference). Compared to CTR, CLD participants exhibited a higher proportion of use of their feet, legs and torso (all p≤0.001, all F≥11.17). Age-related main effects were also found for feet, legs and torso (all p≤0.001, all F≥13.18), highlighting more non-manual exploratory behaviour in children relative to adults, and this was further confirmed in significant differences between CLD children and adults (all p≤0.001, all t≥4.95). This age-dependent diversification in body part usage was particularly pronounced in CLD children, even relative to control children (Feet: t 601.25 =4.90, p<0.001; Legs: t 601.25 =18.03, p<0.001; Torso: t 601.25 =6.84, p<0.001). These findings suggest that CLD children demonstrated greater versatility in their actions compared to CLD adults and two-handed peers (Fig. S2 for absolute time analysis).

The study was conducted immediately after the COVID-19 lockdown and overlapped with ensuing safety restrictions, during which some children wore masks and were generally hesitant to use their mouths to interact with objects. We therefore conducted a separate analysis for mouth use due to the unique circumstances during data collection. Out of the 65 participants who completed the behavioural tasks, only 10 (15.4%) used their mouths or chins in at least one of the 15 tasks. Notably, all of these participants were from the CLD groups, including five children, resulting in a significant group difference, based on a Chi-square test of independence (X2=12.64, df=3, p=0.005). A two-sample bootstrap analysis was conducted to assess the difference in mean use percentage scores between CLD children and adults (p=0.027), indicating that children used their mouths more on average compared to the adults. This finding highlights that even in such an unusual situation, CLD participants, and children in particular, still tended to use their mouths for object manipulation.

In summary, our results demonstrate that the compensatory repertoire of CLD involves multiple body parts. The compensatory behaviour of the CLD children is richer than found in adults. While our analysis does not allow us to ascertain whether this also reflects greater exploration or task switching, our finding indicates that adaptive behaviour at the tested age range (5–7 years) still has not matured. This leads to the pivotal question of whether such behavioural diversity is mirrored in the somatotopic organisation of the brain.

Investigating activity along S1 for multiple body parts—validation
We aimed to compare body map characteristics in BA1-2 of the primary somatosensory cortex (S1; see also Fig. S3) across groups. Participants of all groups were fitted with custom-made soft pneumatic actuators (SPAs), designed to deliver vibration in the magnetic resonance imaging (MRI) environment across a range of body parts that are relevant for compensatory behaviours, namely: the chin (middle; hereafter, lower face), residual arm (or the wrist of the non-dominant hand (CTR)), torso, leg and foot (ipsilateral to the missing hand (CLD)/non-dominant hand (CTR)) (Fig. 2A, B). As demonstrated in Fig. 2C, on average, the fMRI paradigm generated distinct activity for each of the stimulated body parts in the contralateral hemisphere, matching the expected topography of S1 (see also Fig. S4). We also stimulated two body parts as control conditions. First, the thumb of the intact/dominant hand, which we included to account for any potential general inter-group differences in activity levels. This includes any differences in the quality of the data, e.g. due to potential head movements, head size or level of arousal. Second, we stimulated the upper face—which serves as the cortical neighbour of the hand but was not commonly used for compensatory strategies. This allows us to consider the distinct contributions of adaptive behaviour.

We first compared the activity for the control thumb condition in the intact/dominant hand hemisphere across groups. Averaged activity (z values) along the postcentral gyrus (medial—left to lateral—right; values on the x-axis indicate Montreal Neurological Institute, or MNI, coordinates) is plotted for the four groups in Fig. 2D. We focused on the spatial position and amplitude of each individual participants’ peak activity along S1 (see Fig. S5 for single participant plots). We found no significant main effects nor interaction effects across our groups for peak activity level (all p>0.110, F<2.64). We found no significant main effects (all p>0.791, F<0.01) and a weak trend towards an interaction for the position of the peaks (F 1,62 =2.96, p=0.091) that was not supported by Bayesian analysis (BF 10 =0.14, providing substantial evidence in favour of the null hypothesis) (Fig. 2E). We also found no significant pairwise group differences (all p>0.109, t<1.63, not corrected for 9 comparisons). This control analysis confirms previous findings in adults with CLD23,29 and allowed us to ascertain that, despite age differences, the activity across groups is comparable. Therefore, any group differences observed in the deprived hand hemisphere are not due to methodological confounds in the quality of the fMRI recordings.

Local remapping of body parts in the deprived hand area
The very localised deprivation in CLD individuals generates specific predictions of local remapping in the deprived hand cortex. To identify the missing hand area in individual participants, we mirrored the spatial position of peak activity for the intact/dominant thumb identified in its contralateral hemisphere in the deprived hand hemisphere. Considering the thumb comprises the lateral boundary of the hand map, we used the mirror-projection of the thumb peak as the reference point to define the missing hand region of interest (ROI). More specifically, for each participant, we selected one bin lateral and eight bins medial from the mirror-projection of the thumb peak for a total of 10 bins. An example of a functional ROI for an exemplar participant can be seen in Fig. 3A (brain plot). We then extracted activity levels within this individual hand ROI for all body parts (Fig. 3A). When comparing the averaged activity across the study’s main body parts (i.e. the ones typically used for compensatory behaviour: feet, legs, torso, arm and lower face) within the deprived hand ROI, we found evidence for increased activity in CLD participants, as indicated by a main effect of limb difference (F 1, 62 =34.26, p<0.001)—reflecting the classical remapping effect reported in CLD adults5,6. This effect did not interact with age (F 1,62 =0.86, p=0.357); however, we did find a significant interaction between age and body part (F 4,248 =3.73, p=0.006). A weakly trending 3-way interaction between age, limb difference and body part (F 4,248 =2.01, p=0.094) was supported by Bayes analysis (BF 10 =1.31e30), indicating that remapping across different body parts might be differently affected by development (see Supplementary Table 6 for full results of the mixed models).

When looking at individual body parts separately, we find clear effects of remapping (expressed as a main effect of limb difference) by the cortical neighbours (arm: F 1,209.16 =117.02, p<0.001; and lower face: F 1,209.16 =25.20, p<0.001). Whole-brain analysis further confirmed the remapping effect for the arm and the other body parts (Fig. S6). We also find a trend towards a deprivation-driven main effect of remapping of the foot, which is topographically furthest from the hand, as supported by a Bayes analysis (F 1,203.74 =3.59, p=0.06, BF 10 =3.67). Evidence of remapping for the legs and torso did not reach significance (F<2.47, p>0.289). We further find that the effect of limb difference, on arm remapping specifically, interacts with age (F 1,203.34 =4.17, p=0.043). This supports the interpretation that, for the most part, local topographic remapping in the deprived cortex may already be established in children.

To complement this analysis, we next employed a representational similarity analysis (RSA) to quantify how distinct the information is for the different body parts with the S1 hand area (Fig. 3B). Here, we examined differences in activity patterns between each of the five body parts (Fig. 3C). An average of the 10 distances shown in (C) demonstrates more representation of different body parts (increased distances) in CLD individuals. These results indicate that local topographic remapping in the deprived cortex may already be established in children.

Topographic shifts in CLD individuals are global across S1
We next examined the global topography for the study’s main body parts (feet, legs, torso, arm and lower face) along the full extent of S1 (BA1-2) in the deprived hand hemisphere (see Fig. S7 for homologous results in BA3b). Figure 4 shows the mean activity for each body part and group (contrasted with baseline). Qualitatively, perhaps the most striking results are the activity patterns of the residual arm (green) and lower face (red) in the CLD groups, which were already recorded in the deprived hand area analysis (Fig. 3). More broadly, activity seemed to vary between groups, both in terms of activity peak shift and amplitude.

We first characterise the spatial position of the activity peaks based on individual participants’ datasets along S1, for each body part of interest. As apparent from Fig. 5, all body parts of interest show clear shifts in the CLD groups relative to controls, resulting in main effects of limb difference (F 1,58.50 =30.78, p<0.001). In the CLD groups, we see the residual arm’s spatial position showing a lateral shift, whereas the lower face’s spatial position shows a medial shift (relative to the CTR groups), both toward the (missing) hand area (arm: F 1,255.27 =116.68, p<0.001; lower face: F 1,255.27 =5.61, p=0.019; main effects of limb difference). We also see lateral peak shifts for the body parts which are further away from the deprived hand cortex toward the hand area—the feet (F 1,246.56 =7.43, p=0.007), legs (F 1,246.56 =5.81, p=0.017) and torso (F 1,246.56 =7.84, p=0.005; all main effects of limb difference). Crucially, the peak shifts did not compromise the overall S1 topography—the peaks were appropriately located in their relative topographic positions, indicating that the local deprivation triggered extensive shifts which were found globally along the deprived S1.

With respect to developmental effects, we observed a main effect of age for the lower face only (F 1,255.54 =4.30, p=0.039), with no evidence that general development plays a role in the topographic distribution of the other body parts along S1 (all F<1.58, all p>0.209 for main effects of age). We found some indication for development-related remapping effects in the CLD groups, with the leg and lower face showing a significant interaction with age (leg: F 1,254.45 =4.61, p=0.033, lower face: F 1,255.54 =4.68, p=0.032). However, with the exception of the lower face (t 255.65 =2.91, p=0.004), none of the body parts showed significant differences between CLD children and adults. This finding indicates that the topographic shift may already be largely finalised in the children. That is, while CLD participants are different to controls in the topographic distribution of body parts, and CLD adults in particular, children and adults in each group mostly display consistent topography.

Interestingly, when adding the upper face in the model, which is the lateral neighbour of the deprived hand and is not widely used for compensatory behaviour, we found a weak trend towards a main effect of limb difference (F 1,317.97 =2.99, p=0.084), which remained ambiguous following the Bayesian analysis (BF 10 =0.52). While trend results should be interpreted with caution, this might indicate that the shifts observed in children might not be exclusively shaped by behaviour in the earlier stages of development.

The omnibus analysis (across the 5 main body parts) found a strong main effect of limb difference (F 1,58.50 =30.78, p<0.001), but no main effects of age. The fact that limb difference, rather than age, strongly modulates spatial positions suggests that S1 topography is pre-determined to a large degree before the age of 5–7 years. Of interest, when combining across our primary body parts, we also find a strong trend towards an interaction between age and limb difference (F 1,58.49 =3.97, p=0.051, BF 10 =1.69e168). This indicates that, although age effects are not robustly found for individual body parts, put together, there are also distinct developmental effects that determine global remapping in CLD individuals.

To complement this analysis of peak position, we also examined the activity levels at the peak of each individual participant’s body part representations along S1. When comparing individual z values for peak activity, the omnibus analysis reveals a significant interaction between age, limb difference and body part (F 4,224.94 =3.09, p=0.02), as well as main effects of limb difference (F 1,58.88 =20.91, p<0.001) and age (F 1,58.81 =4.88, p=0.031) (see Supplementary Results and Fig. S8 for full peak activity level analysis). In other words—overall, CLD adults show distinct effects to CLD children, which cannot be explained fully by the typical course of development, but this effect is not shared for all body parts.

Together, these results show that the representation of the entire homunculus is shifted in the CLD from an early age, and even in body parts that are represented further away from the deprived hand area. Nevertheless, some interactions with development for remapping in CLD individuals may hint at a potential modulatory role for behaviour.

Homeostatic plasticity as a driver of global remapping across S1—a computational model
Our findings so far indicate that, while later-life determinants––such as compensatory behaviour––may contribute to brain topography, the most dramatic group differences are already determined in childhood. Given the unexpectedly broad effects of limb difference, we wanted to consider whether homeostatic plasticity is a plausible mechanism for topographic shifts spanning the entire somatosensory strip. To strengthen our conceptual understanding of the role of homeostatic plasticity, we implemented a biologically inspired computational model. Our model posits that when there is a change in the averaged input firing rate to a specific brain region (such as deprivation in the hand region), all weights of synaptic connections to that region are adjusted to maintain the overall/average local activity14,30,31. The primary goal of the model is to investigate how the global topography in S1 changes in CLD individuals. Rather than attempting to capture all biological complexity, the model operates at a high level and incorporates substantial simplifications and abstractions, in order to help us focus on core dynamics and underlying principles32–34.

The model (shown in Fig. 6) simulates activity of the somatosensory homunculus in line with the typical topography of this region. Fifty internal nodes or neurons (Fig. 6A, top layer) represent the cortical regions that receive input from various body parts. For this implementation, we considered 10 main body parts: genitals, foot, leg, torso, arm, hand (including palm and fingers), face (including upper face and lower face) and throat (Fig. 6A, bottom layer). Each body part can be either at rest (stimulation value equal to zero) or stimulated (stimulation value equal to one) during a simulation. The activity of a neuron during stimulation of a single body part is determined by the connection weight between the stimulated body part and the corresponding internal neuron (as an example, see Fig. 6B where the torso is stimulated).

A crucial factor for synaptic scaling, which is driven by homeostatic plasticity is the average activity of neurons over time—referred to as Mean Neural Activity (MNA) in this model13,14,31. We calculate a neuron’s MNA as a sum of the products of the stimulation probabilities across body parts and their respective connection weights, as shown in Fig. 6C. The stimulation probability estimate shows often a body part is typically used or stimulated. It is assumed that not all body parts receive the same amount of input during daily activity. In particular, hands typically experience more tactile stimulation than other body parts due to interaction with objects35, as well as frequent self-touch of the other body parts, such as the face36. The connection weights reflect the node’s body part selectivity. All cortical S1 regions receive input from all body parts’ stimulations, but with different strength37.

The essential feature of the model is the following: it assumes that the MNA value of each internal neuron over the long term (~hours-days) is preserved, regardless of outer conditions (e.g. complete loss of tactile stimuli from a body part). We called this value homeostatic MNA (or MNAhom). This is what we consider homeostatic plasticity, which can be achieved through a synaptic scaling mechanism14,30,31. If there is a deviation from this homeostatic value, the connection weights are updated (i.e. multiplied by a factor) to restore the target value.

Our model allowed us to simulate the somatosensory homunculus activity when stimulating a specific body part, as well as deprivation effects by excluding or reducing inputs from a specific body part. More specifically, we considered the reduced input from the hand into the homuncular strip for CLD individuals. It is important to note that being born with a missing hand due to an upper limb developmental malformation does not completely eliminate primary input to the hand area, as some peripheral nerves that are genetically hard-wired to send information to the central nervous system remain intact. That is, nerve endings intended to innervate the hand densely populate the end of the residual limb, a phenomenon akin to peripheral reorganisation38. Consequently, when the residual arm is stimulated, either through use or passive stimulation, nerves associated with the arm are activated, in addition to residual nerves of the missing hand, although to a lesser extent. To account for peripheral reorganisation, the initial weights of the model from the hand in CLD individuals were only reduced and not set to zero, as can be seen in Fig. S9. To clarify that these represent residual nerves, in our model, we use different labels (i.e. Palm_nerves and Finger_nerves in Fig. S9 and onwards).

The difference in initial connectivity weights between the two groups lead to distinct MNA values for their corresponding neurons. This, in turn, results in different trajectories of homeostatic adjustment in each group. We simulated this mechanism with our model (see formulas in the Methods section). The model results are presented in Fig. 6D, which shows the activity profiles across the 50 nodes when a specific body part is stimulated (top plots) and the related shift of the position of the activity peaks (bottom plots). To investigate whether the synaptic scaling homeostatic mechanism can explain the observed activity patterns in CLD and control groups, we qualitatively compared the model activities with the activity patterns observed in humans (both CLD and CTR). More specifically, we compared the model’s properties with four key empirical observations, as presented in Fig. 5 (see Methods for a more detailed explanation): (1) The activity peak during arm stimulation is higher and shifted laterally in CLD individuals compared to CTRs; (2) The activity peaks for the foot, leg, torso, upper face and lower face are closer to the hand area in CLD individuals than in CTRs; (3) The activity peaks are higher in CLD than in CTR individual; (4) Both groups show a decrease in activity peak magnitude from the foot to the leg and torso, and an increase from the upper face to the lower face. The model, which incorporates homeostatic adaptation, peripheral reorganisation, higher hand stimulation probability and stronger initial connection weights from hand to the internal nodes in CTRs, successfully replicates all four empirical findings (for descriptive comparisons, see Fig. 6D). That is, our key empirical observations can be explained using a key homeostatic plasticity mechanism—synaptic scaling.

To further confirm the role of homeostatic plasticity, we compared the results to a simplified, more rudimentary model based solely on the homeostatic mechanism, i.e. excluding peripheral reorganisation and assumptions of input inhomogeneity for the upper limbs. Despite its simplicity, the model largely aligns with the key findings related to differences between the two groups (observations 1–3). However, this rudimentary model did not replicate our 4th observation for the CLD group. This shows that our model is robust to variations in parameter choice (see also Supplementary Results). Notably, with our main model, we were able to replicate all four observations without considering behavioural differences between the groups and further attempts to consider behavioural differences did not improve compatibility with the observed findings (see Supplementary Results for model variants with different parameter settings).

The impact of behaviour on topographic shifts
While most of the topographic differences observed in the CLD groups appear to have been pre-determined prior to childhood (as evidenced in the very strong main effect of limb difference across all analyses), we also identified evidence for divergent developmental trajectories in the CLD groups. In particular, in the S1 peak shift analysis, when combining across our primary body parts, we reported an interaction between age and limb difference (F 1, 58.49 =3.97, p=0.051, BF 10 =1.69e168). To directly examine whether the global S1 remapping reported above relates to behaviour, we analysed deviations in activity profiles and behavioural performance for each CLD individual relative to their peer control group. If behaviour links to brain remapping, then participants who used a specific body part more than their peers would also show larger peak shifts compared to their peers (Fig. 7A). In other words, larger deviations from the average peer group in behavioural usage would predict larger deviations in activity peak shifts relative to the average peer group shift.

We analysed the two CLD groups (n=15 children and n=15 adults) separately using permutation tests with 5000 iterations. The tests aimed to determine: (1) if the Spearman correlations between the behavioural and peak shift deviations were significantly greater than zero for both groups, and (2) if the children’s group had a larger correlation than the adult group. Both tests yielded significant results (Fig. 7B–D). Specifically, the averaged Spearman correlation was 0.59 (p<0.001) and 0.39 (p=0.003) for the children and adults, respectively. Furthermore, the children’s mean correlation was significantly larger than the adults’ (t=2.25, p=0.016). These results were corroborated when using frequency as a measure to estimate compensatory behaviour (see the Supplementary Section ‘Supportive analysis for the impact of behaviour on topographic shift’). These findings demonstrate that the unique behavioural adaptations that are favoured by individuals are also reflected in variations in S1 brain topography, particularly during early development.

Methods
Participants
We tested 16 CLD children (mean age: 6.69±0.60; 7 left-handed; 10 female; see Table 1 for details) and 16 CLD adults (mean age: 41.19±11.39; 5 left-handers; 12 female; see Table 2 for details) with an isolated congenital upper limb malformation—of either the entire limb or the hand-plate. CLD individuals were included if they have only one hand which can perform a pincer grip (i.e. the residual arm has either no digits or digits which do not provide a useful grip). CLD individuals with a single malformed but functional hand are excluded as ‘too functional’; individuals with no functional hands are also excluded. One CLD child was excluded from the study due to having three residual fingers and a functional grip in the malformed limb. Control participants were recruited to match the other two groups in terms of age, gender and handedness (with respect to the intact hand). For the control group, we tested 21 children (mean age: 6.71±1.10; 4 left-handed; 13 female) and 16 adults (mean age: 39.56±9.85; 5 left-handers; 7 female). Not all participants (4 control children and 1 CLD adult) participated in the behavioural session, therefore the behavioural analyses were conducted on the following sample: 16 CLD children (mean age: 6.69±0.60; 7 left-handed; 10 female); 15 CLD adults (mean age: 42.27±10.91; 5 left-handers; 12 female); 17 control children (mean age: 6.59±1.18; 4 left-handed; 10 female) and 16 control adults (mean age: 39.56±9.85; 5 left-handers; 7 female). Not all participants (1 control child, 1 CLD child and 1 control adult) participated in the fMRI session, either because they decided to stop the experiment or for technical issues with the SPAs, therefore the fMRI analyses were conducted on the following sample: 15 CLD children (mean age: 6.73±0.59; 7 left-handed; 9 female); 16 CLD adults (mean age: 41.19±11.39; 5 left-handers; 12 female); 20 control children (mean age: 6.70±1.13; 4 left-handed; 13 female) and 15 control adults (mean age: 39.67±10.18; 4 left-handers; 7 female). Ethics approval for this study was granted by the University College of London research ethics committees (17205/001). Informed consent and consent to publish were obtained in accordance with ethical standards set out by the Declaration of Helsinki. All participants met local MRI safety guidelines and provided written consent. For child participants, a parent provided written consent, and the child gave verbal assent. Adult participants were compensated for their participation and had the option of donating all or part of their compensation to a charity (either REACH or LimBO). Child participants received an Amazon voucher, and a fixed amount was also donated to a charity.

To improve access to CLD participants, the study was conducted from two locations: the Birkbeck UCL Centre for Neurological Imaging (BUCNI) in London and the MRC Cognition and Brain Sciences Unit (CBU) in Cambridge. 21 CLD participants (10 children and 11 adults) were tested at BUCNI, while 12 CLD participants (7 children and 5 adults) were tested at CBU. Additionally, nine control children were tested at BUCNI, and 12 control children were tested at CBU. All control adults (n=16) were tested at CBU.

Behavioural session
Procedure and task. To evaluate how CLD participants compensate for their missing limb during daily activities, a standardised object manipulation task (‘Surprise Suitcase’) designed to elicit bimanual coordination was employed. This task involved a series of everyday objects placed inside a large, lightweight suitcase locked with a keyed padlock (see complete task and object list in Supplementary Table 1). This task was performed by all four participant groups (including adults). Participants were asked to open or manipulate these objects (e.g. open a plastic salad container, unwrap candy) without any guidelines or restrictions regarding the body parts to employ. The task was conducted in a separate room prior to the scanning session. Participants were seated on a soft play mat, and the session was video recorded for later analysis. The experimenters sat directly in front of the participant, providing instructions for task completion and encouragement. For children, a parent typically remained in the room for comfort and support. The task began with the participant seated on the mat with the closed suitcase in front of them. The first instruction involved using the key to unlock the padlock. Following this, the experimenter presented each object in a pre-determined order, instructing the participant on how to manipulate it. For instance, an instruction might sound like: ‘Could you please open the salad box?’. Generally, the execution time varied by age group. For children, completing the task typically took 10–15 min (including all conditions), whereas adults generally finished within 5 min. While CLD participants were given the option to wear a prosthetic to complete the task, none of the children did. One CLD adult preferred to complete the task with the prostheses, and five CLD adults did the task with and without the prostheses. Analyses were conducted with their preferred and more comfortable method.

Compensatory behaviour analysis
Video coding. Compensatory motor behaviour was coded offline from video recordings of the behavioural tasks. Behaviour was characterised as the active use of the following body parts: intact or dominant limb, residual or non-dominant limb, torso, lower face, legs and feet. Behavioural coding was completed by four independent raters. To establish the reliability of the coding, the ratings of the three additional raters were compared against those of the main rater for 20% of videos. Raters demonstrated almost perfect agreement on body part used (Cohen’s kappa=0.972) and excellent reliability for body part usage duration (ICC=0.989). As agreement rates were almost indistinguishable, coding was left as per the secondary raters’ rating.

Behaviour indices. To assess everyday compensatory behaviours in CLD and control participants, two primary measures were calculated for each sub-task: (1) Co-use: The co-occurrence of body part usage during tasks, and (2) Time Usage: The percentage of time spent utilising each body part relative to the total task duration. More specifically, to calculate the percentage of use metric, in each sub-task, we summed the durations of all bouts using a particular body part and expressed that as a percentage of the duration of the sub-task time. These percentages were then averaged across sub-tasks. Furthermore, we derived two outcome measures from the co-use measure. The first co-use measure is an estimated index that reflects both the number of body parts used and how consistently they were used, with higher values indicating more usage or more consistent usage (see Supplementary Methods for details on how this index was calculated). The second co-use measure allowed us to compare compensatory co-use with brain measures. A co-use matrix (5×5) was generated for each participant. This matrix captured the frequency of co-occurrence for each pair of body parts across all tasks within the session. The value at each cell in the co-use matrix represented the number of sub-tasks where those two body parts were co-used during the entire session.

fMRI session
Soft pneumatic actuator (SPA) skin stimulator and the controller setup. Participants were stimulated during the scanning session with a modified version of the SPA-skin interface79–81. Owing to its soft and compliant materials, this technology was previously demonstrated to provide reproducible, localised and high-fidelity tactile stimulation over various parts of the human body, with stiffness ranging from 0.5 to 2 megapascal (MPa).

The complete apparatus comprised an actuator, an electronic controller and a pneumatic supply system. The silicon-fabricated actuator (1 mm thickness) consists of two layers of thin silicone sandwiching a masking tape layer (Black tape Fig. 2A) that determines the shape of the active area of actuation. Upon inflation through pneumatic air input of up to 100 kPa, the actuator is able to provide a force of 3N with 10 mm2 active area79,80. In our study, we used a round-shaped actuator with a 3.75 mm radius and an active area of 4.4 mm² for all body parts, except for the thumb, where we applied a smaller round-shaped actuator with a 3.2 mm radius and a 3.25 mm² active area to ensure a better fit. By controlling the rate at which the air is sent in and extracted out, a sense of vibratory feeling is generated through the electronic controller unit, which is the second component of the apparatus. The electronic controller is connected to a computer via USB cable, receiving instructions at 50 times per second to activate the respective valve at the respective actuation amplitude and frequency. An air compressor in the control room of the fMRI scanner was used to supply input pressure to a pneumatic pressure regulator (ITV1050, SMC Corp., USA) holding stable output pressure up to 2 bars or 200 kPa. The complete system was designed in EPFL, Switzerland79,80, and a homologous system was created for the CBU testing site. The system components have been extensively tested for artefacts they may introduce in the fMRI environment81.

Scanning procedure and fMRI task. After the behavioural session, participants took part in the MRI session. To familiarise the children with the scanner setting before starting the experiment, they were taken with their parents to see either the actual scanner (at BUCNI) or a mock scanner (at CBU). Children were invited to enter the scanner to ensure they were not scared of the machine and helped the researcher place a puppet on the scanner bed. This procedure aimed to make the children comfortable and excited about the study. Additionally, the noise of the scanner was presented to the participants at this stage to avoid any surprise during the actual scanning. Furthermore, to ensure participation in the experiment and to make sense of the sensory stimulation provided in the scanner, children were introduced to a narrative about an enchanted forest where butterflies became invisible. Participants were asked to help make them visible again by wearing special devices (the soft vibrotactors) that could capture these invisible butterflies. The vibrotactors delivered gentle vibrotactile stimulation to the participants’ skin, mimicking the sensations of butterfly wings. Participants were instructed to simply be aware of these sensations during the scan, with no further action required. They were also informed that they would only need to watch videos throughout the session (Pixar short films).

After this first familiarisation phase, the 7 soft vibrotactors were attached to 7 predefined body parts (see Fig. 2B) using medical tape and self-adhesive sports bandages. The predefined body parts for the CLD participants (children and adults) were: (1) Chin (just below the lower lip, close to the tip; lower face throughout the text); (2) Residual arm (palmar side); (3) Torso (just below the rib cage); (4) Leg (end section, just above the knee); (5) Foot (internal section, just above the midsection of the foot arch); (6) Thumb (on the palmar side distal phalanx) of the intact or dominant hand; (7) Forehead (around the midline; upper face throughout the text). Note that body parts 3, 4 and 5 were ipsilateral to the residual arm (missing-hand side). These body parts were chosen because they are the alternative effectors that congenital children tend to use for their daily activities (with the exception of the upper face, which was included as a control condition). For the control children and adults, a similar configuration was used, except that the actuator on body part 2 (i.e. the residual arm for the experimental group) was placed on the palmar side of the non-dominant wrist. For the control group, body parts 3, 4 and 5 were ipsilateral to the non-dominant hand.

After all the actuators were placed on the participant’s body, they were taken to the scanner room to start the actual experiment and were invited to lie on the scanner bed. A few exemplar stimulations were delivered to each body part to ensure that the correct body part was stimulated and that the participant was able to clearly feel each stimulation. After this second familiarisation phase, the actual experiment could start. For the group of children, one parent sat with the experimenters in the control room (at BUCNI) or in the scanner room while wearing protective headphones (at CBU) to comfort the children during the session.

The MRI session consisted of three functional runs, one structural run, and a fieldmap run. The fieldmap was acquired after the first functional run. The remaining two functional runs and the structural scan followed in that order. The protocol was flexible and could be adjusted based on each participant’s needs.

After each functional run, participants were asked if they felt the SPA stimulation (butterflies) throughout the experiment. Positive reinforcement was provided by mentioning how many butterflies were caught with their help. Before the final structural run, where no tactile stimulation occurred, participants were informed that all butterflies had been captured, but an additional scan was necessary to confirm there were none remaining.

To ensure that participants remained still and comfortable throughout the scanning process, cartoons were projected and visible to participants through a mirror throughout the session. The passive nature of watching cartoons helped the children keep still, essential for acquiring high-quality imaging data over the multiple scans. We confirmed that the cartoons minimally affected S1 topography mapping prior to the start of the study using several pilot sessions, where we compared selectivity patterns across body parts with and without cartoon watching. Moreover, any age-related differences that might be causing attentional changes to the vibrotactor stimulation were accounted for by the age-matched control groups. Additionally, avoiding a motor task eliminated head movements and other potential performance differences between groups, especially between younger children and older children, or relative to adults.

MRI data acquisition. All MRI measurements were acquired using a Siemens 3T Magnetom Prisma scanner. At the BUCNI, a 30-channel head coil provided a clearer view of the screen due to the removal of the two frontal coils. At the MRC Cognition and Brain Sciences Unit (CBU), a 32-channel head coil was used. Signal-to-noise analysis confirmed no significant difference between the data collected at the two sites, and activity profiles were also comparable. Task fMRI data were acquired in both sites using a multiband GE echo planar imaging sequence with an acceleration factor of 4. The field-of-view (FOV) consisted of 72 slices (TR: 1450 ms, TE: 35 ms, FA: 70°) with a spatial resolution of 2 mm isotropic. A whole brain anatomical T1-weighted (MPRAGE) image was also collected with a 1 mm isotropic spatial resolution (FOV: 192×192×176, TR: 2530 ms, TE: 3.34 ms, FA: 7°, TI: 1100 ms, GRAPPA factor: 2).

Three separate sensory stimulation sequences were created using a custom-made Matlab script using a block design. Each run consisted of 5 block repetitions for each of the 7 tested body part conditions, resulting in a total of 15 block repetitions per body part across the three runs. All analysed participants completed the three runs. As achieving perfect first-order counterbalancing wasn’t feasible due to the specific number of blocks and repetitions, we designed an optimal first-order counterbalancing across the three functional runs. This ensured that each body part was stimulated roughly the same number of times after each other body part throughout the entire experiment. Notably, all participants experienced the same three sequences, but the run order was randomised for each individual.

Tactile stimulation within each 9s block began 1s after the block started to allow for pressure stabilisation and lasted for 7.5s, followed by 0.5s of no stimulation before the next block began. Each stimulation involved triplets of frequencies (5 Hz, 15 Hz, 30 Hz) repeated five times. The frequency changed every 400ms with a 100-ms gap between frequencies. This variation in frequency aimed to prevent peripheral or central adaptation81–84, which could result in reduced sensory processing.

The tactile blocks were interleaved with six 9s ‘null blocks’ interspersed throughout the run, to allow for BOLD signal relaxation for baseline estimation. These null blocks were placed randomly with the following restrictions: No null block could occur at the beginning or end of the run. Null blocks were grouped in chunks of either one or two consecutive blocks. Chunks of two null blocks had to be separated by at least two experimental blocks. In addition, two 16s null blocks were added to the beginning and end of each run. The total run time was 401.6s (277 volumes with a TR of 1.45s).

MRI analysis. For each individual, cortical surfaces were estimated from the structural images using FreeSurfer 7.4.085,86. We used the Connectome Workbench software (https://www.humanconnectome.org/software/connectome-workbench) to visualise the individual (native space) and group (standard space) topographical maps projected on the surfaces and to ensure accurate spatial registration between the structural and functional volumes, as well as to verify the precise alignment of the ROIs. Connectome Workbench was also used to map the volumetric maps to the surface space.

fMRI pre-processing and first-level analysis. All MRI data pre-processing and analysis were carried out using FMRIB Software Library87 (FSL version 6.0) as well as scripts written in MATLAB (version R2020b) and R (4.2.0), which were developed in-house.

To ensure that the functional scans were well aligned for each participant, we calculated a midspace between the three runs. This midspace represents the average space where the images are minimally reoriented. Each scan was then aligned to this midspace using FMRIB’s Linear Image Registration Tool or FLIRT88,89. Finally, the midspace and functional images were then aligned to the high-resolution anatomy image using SPM12. fMRI data were subject to standard pre-processing, which included motion correction using FMRIB’s MCFLIRT89, brain extraction using Brain Extraction Tool90, temporal high-pass filtering, with a cutoff of 90s and spatial smoothing. Signal distortions caused by B0 field inhomogeneities were corrected using B0 field maps. The field map magnitude and phase images were processed with FSL’s fsl_prepare_fieldmap. No slice-timing correction was applied because of the relatively short TR (1450 ms). A 4 mm (2× voxel resolution) full-width at half maximum (FWHM) kernel was used for smoothing data in the univariate analyses, while a 2 mm FWHM kernel was used for multivariate analyses.

To localise brain regions activated during tactile stimulation, we used a voxel-based General Linear Model as implemented in FEAT (FMRI Expert Analysis Tool). For each run, seven regressors were created, one for each body part condition. These regressors were convoluted with a double-gamma function to account for the delayed BOLD response. Additional first-derivative regressors were included to capture temporal variations in the BOLD signal and estimated head movements (six parameters). Finally, volumes with excessive motion or unusual signal intensity (frame-to-frame displacement larger than 0.9 mm) were flagged for exclusion (using the FSL function fsl_motion_outliers) and included in the model as additional regressors of no interest (one for each outlier). The 0.9 mm threshold was selected based on previous methodological work by Siegel et al.91. For children, across the three runs (totalling 99 runs for 33 children), the vast majority of the data (85% of all runs) contained 10 or fewer identified volume outliers which is 4.63% or less of the total volumes with stimulation (around 215 volumes out of 277 total volumes per run) were flagged as outliers. This low proportion of outlier data suggests that children generally remained still during the stimulation. Importantly, when comparing the distribution of outliers between the two groups of children using a chi-square test, we observed no significant difference (X2=23.41, df=23, p value=0.437). Nine children showed a larger number of outliers in at least one of the three runs (min=11 and max=69, corresponding to 5.09% and 31.94% of the total volumes with stimulation). These children were visually inspected to ensure the estimated maps were as expected. Since the maps were consistent, we decided to keep these datasets. For adults, 97% of the outliers across the three runs were less than or equal to 1 (0.46% of the total volumes with stimulation).

Second-level and group-level analyses. For each participant, the three first-level contrast images of each body part obtained from the first-level analysis were further processed with a second-level analysis using a fixed-effect model in FSL. The resulting images were normalised to MNI space and used for the line analyses (Figs. 3–5).

Before conducting the group-level analysis, we flipped these second-level images along the x-axis for CLD participants missing the right hand and for control participants who were left-hand dominant. This standardised the left hemisphere as the ‘deprived/non-dominant’ hand hemisphere across all participants. This step was necessary, as the tactile stimulators were aligned with the missing/non-dominant side. We then conducted two types of group analysis using these second-level contrast images. First, we estimated group-level activation to identify regions engaged during stimulation of each body part for visualisation purposes (Figs. 2C and S4). Second, we contrasted body part maps between the CLD and control groups to determine differential activity in response to stimulation (Fig. S6). Both analyses employed a random-effect model in FSL (FLAME1) and a cluster correction to control for multiple comparisons with the following parameters: Z threshold=2.58 (corresponds to a two-tailed p value of approximately 0.01) and cluster p threshold=0.05. The resulting thresholded maps were then projected on a standardised surface for visualisation purposes (Figs. 2C, S4 and S5-Arm). Where statistical significance was not reached, uncorrected maps (p<0.01) are presented (Supplementary Fig. S6, except for Arm).

ROIs definition
As we were interested in studying both global (i.e. along S1) and local (i.e. hand territory) changes due to age and/or deprivation, we defined two different types of ROIs within S1. Specifically, we defined a group ROI for the global analysis and individual ROIs for the local analysis. The group ROI, derived from Glasser atlas92 areas 1 and 2 in MNI space, provided an unbiased and efficient means of capturing global changes across the somatosensory cortex. In contrast, the individual ROIs, defined based on thumb peak locations, allowed for more accurate and sensitive detection of local changes within the hand territory, given its known anatomical variability93. We also defined an ROI to study activity changes in S2 (see Supplementary Results). The ROIs were focused on the deprived hemisphere in individuals with CLD and compared with the non-dominant hand hemisphere in controls. In addition, homologous ROIs in the intact/dominant hand hemisphere (in CLD/controls, respectively) were constructed for control analyses involving the intact/dominant-hand thumb.

Global S1 analysis (bins-ROI). We used anatomical atlases to investigate global changes for all body parts across the entire postcentral gyrus strip. The ROI was defined in each hemisphere on the standard flat maps (FS_LR32k) of the Human Connectome Project. Hemisphere-specific rotations (15° counter-clockwise for the left and clockwise for the right hemisphere) were applied to the flat maps, aligning the central sulcus perpendicular to the horizontal plane using the fundus of the central sulcus (defined by the lowest point of BA3b based on the FreeSurfer atlas) as the pivot (index 133 and 130 for left and right hemispheres, respectively; these points correspond to the origin flat map coordinate). We first created a mask for the postcentral gyrus using Glasser’s multimodal parcellation encompassing BA1 and BA292. Since this combined ROI did not fully cover the medial aspect of the central sulcus, we also added region BA5m, which corresponds with the medial aspect of FreeSurfer’s S1 and also overlapped with the foot and leg activity in our cohort (see Figs. 2C and S3). Within the highest and lowest points of the defined mask on the flat map, fifty horizontal lines equally spaced were defined, resulting in 49 smaller rectangles with their longer axis oriented with the anterior-posterior axis. These rectangles were subsequently mapped onto cortical surface vertices, resulting in 49 left and 48 right ROIs spanning the medial-to-lateral extent of S1. The different number of bins between the two hemispheres is due to small differences in the geometry of the two surfaces, resulting in zero vertices enclosed in the most medial rectangle. This procedure yielded surface ROIs that were subsequently transformed into volumetric ROIs. For comparison, we also extracted an equivalent ROI based on BA3b, as presented in the Supplementary Materials (Fig. S7).

Functionally-defined hand ROIs. To create a hand-specific ROI for each participant, we employed the functional second-level contrast of thumb vs baseline from the intact and dominant hands for the CLD and control groups, respectively. For each individual, the functional response (z-statistic) elicited in the hemisphere contralateral to the dominant/intact hand during thumb stimulation was averaged within each bin defined in the aforementioned S1 global ROI (Figs. 2C and S3). For each participant, the bin with the peak activity (highest z-statistic) was identified, and its corresponding MNI coordinate was used to locate the homologous bin in the hemisphere ipsilateral to the dominant/intact hand (i.e. the deprived/non-dominant hemisphere) to estimate the location of the missing/non-dominant thumb. We have previously extensively validated this procedure5,94. Finally, since the thumb defines the lateral boundary of the hand area95–97, the hand ROI was defined by combining the peak bin with eight bins medial to it and one bin lateral to it, encompassing a total of ten bins. We also defined an S2 ROI according to Glasser’s parcellation (see Supplementary Methods for full details).

ROI analysis
Local remapping: univariate analysis. We investigated local plasticity effects specifically within the deprived (missing) hand ROI. We averaged the contrast values (z-statistic) from the second-level analysis within the ROI for each body part compared to baseline, for each participant. These averaged values were then used for the group analysis. This analysis provided a basic measure of activity changes within the hand representation, as measured in prior studies5,6. The same approach was further utilised for studying S2 (Fig. S14).

Local remapping: representational similarity analysis (RSA). To confirm that activity levels for the different body parts in the S1 (missing) hand area provide functional information, and not just noise, we employed RSA98. For each participant and run, we extracted the beta weights (representing brain activity) estimated with FEAT from each ROI. We then calculated the pairwise distance between these beta patterns across all body parts (excluding the thumb), using the cross-validated Mahalanobis distance99. This distance reflects the dissimilarity between brain activity patterns evoked by different body parts. To improve the reliability of these distance estimates, we applied multidimensional noise normalisation. This approach down-weights the influence of noisy voxels within the ROI, focusing on the more reliable signal. The Mahalanobis distance offers two key advantages: (1) Multivariate noise normalisation removes noise that might be correlated across voxels, leading to a more accurate measure of dissimilarity between activity patterns99. (2) Cross-validation ensures that random noise alone won’t inflate the distance between patterns. If the only difference between two patterns is noise, their average dissimilarity will be close to zero. Consequently, the dissimilarity measure can even be negative100. Importantly, dissimilarities that are significantly greater than zero provide evidence that the ROIs contain distinct, task-related information (e.g. separate representations for different body parts). The cross-validated Mahalanobis distance calculations were performed using the Python library for RSA, rsatoolbox version 0.0.4 (https://github.com/rsagroup/rsatoolbox). The same approach was further utilised for studying S2 (Fig. S14).

Global S1 remapping
A two-dimensional analysis procedure was employed to quantify functional activity elicited in the deprived hand hemisphere during tactile stimulation of each body part (foot, leg, torso, arm, upper face, lower face). First, for each individual, the second-level estimated contrast (z-statistic) related to each body part (versus baseline) elicited in the deprived/non-dominant hand hemisphere (or the hemisphere contralateral to the intact/dominant hand for thumb-related analyses) was averaged within each bin, defined in the global ROI. This resulted in a medial-to-lateral line plot depicting activity across S1 (Figs. 3–5). For visualisation purposes (Figs. 3 and 4), these activity plots were averaged across participants within each group, conveying a distribution of peak activity along S1. Note that for two participants, a clear peak activity (z-statistic exceeding zero, indicating a statistically significant positive activation) was not identifiable for the torso, and therefore these two contrasts (out of 462 total contrasts, i.e. 7×66 participants) were excluded from further analysis. Additionally, double-peaked activity patterns were observed in a subset of participants (n=20) and contrasts. In these cases, the peak closer to the group average for that specific contrast, which typically aligned with the expected location based on the classical homunculus model, was chosen manually. Most (15/20) of the double peaks were observed in control adults, particularly when stimulating the arm (7/15) and leg (4/15). Contrasts that were excluded and with double peaks are reported in Supplementary Table 12 for further reference. Following data cleaning, based on the peak activity identified for each individual participant and body part, we conducted a position analysis. This analysis investigated the location (bin) of the activity peak within the activity analysis (see Supplementary Materials and Fig. S8). This analysis examined peak values (z-statistic) identified in the activity profiles to assess changes in activity for body part representation.

Brain-behaviour correlation analysis
For each CLD participant, we conducted an analysis to examine the correlation between behavioural deviance and brain position deviance across different body parts. This analysis involved several steps: First, we quantified the behavioural deviance for each body part by calculating the difference in the percentage of usage of each body part, compared to the peer group norm. The corresponding brain change was estimated as the topographical shift in MNI coordinates, relative to the peer group norm. This approach yielded five paired values (compensatory behaviour and brain shift) per CLD participant (Fig. 7A). We then used the individual participant and the peer group-matched control participants to calculate the behavioural and brain deviance measures, one for each body part. These measures indicate an increase/decrease in body part use by the participant, compared to the control peer group.

Second, for both control and CLD participants, we measured the bin distance between each body part peak position (i.e. bin location) and the participant’s functional thumb position. Then, for the CLD participants only and each body part, a deprivation-related position deviance was defined as the ratio of the participant’s body-part-from-hand distance to the average body-part-from-hand distance of the control peer group. We thus obtained for each participant five brain-related deviance measures, one for each body part. The resulting ratio reflects the extent of deviation from the typical bin location of each body part.

Finally, to evaluate the relationship between behavioural changes and brain changes, we calculated the Spearman correlation coefficient for each participant between their behavioural deviance measures and brain position deviance measures (Fig. 7B). This correlation provided an individual-specific measure of how deviations in body part usage were related to deviations in brain activity locations. Through this approach, we aimed to uncover the nuanced relationship between compensatory behaviour and corresponding brain changes in individuals with CLD. For information about the permutational analyses conducted to verify the statistical significance of the Spearman correlation coefficients, see the ‘Statistical analysis’ section.

Model
We used a computational model based on a key homeostatic stabilisation mechanism, synaptic scaling31 (see ref. 14 for a review), to investigate whether our empirical findings could be explained by this mechanism. The model is available here: https://colab.research.google.com/drive/1L9Bv3mbHKG1wh2oF2VHgOhZljOiDjsmH?usp=sharing.

Model description. The model (Fig. 6) is designed as a shallow neural network with 50 internal neurons, each representing regions that receive input from ten body parts (genitals, foot, leg, torso, arm, hand (including palm and fingers or corresponding residual nerves in CLD individuals), face (including upper face and lower face) and throat). The internal neurons mimic the activity along the S1 strip (Y 1, Y 2, ..., Y 50). The activity of a neuron Y j is calculated as Y j= i=1,2,...,10 W j,BPi * X BPi, where W j,BPi is the weight of the connection between body part BPi and internal neuron j, and X BPi are stimulation values (X BPi=1 for the stimulated body part, X BPi=0 for the non-stimulated body part). If only one body part BPl is stimulated (X BPj≠l =0), the activation of the neuron Y j is simply calculated as Y j=W j,BPl * 1=W j,BPl.

As mentioned in the results section, the aim of the model is to simulate homeostatic plasticity via a synaptic scaling mechanism14,30,31. This is achieved through two main steps. First, an initial Mean Neural Activity (MNA) is calculated for a neuron j:

MNAj init= i=1,2,...,10 W j,BPi * P BPi

where P BPi is the stimulation probability of each body part. We considered equal probability (P=0.05) for all body parts except for the hand (both palm and fingers), which were assumed to have a probability of 0.15 (which means that the hand is stimulated 15% of the time). For a detailed analysis, please see the section ‘Justification of stimulation probability settings’ in the Supplementary Materials.

Second, all weights of the neuron j are updated according to the following formula:

W j,BPi = MNAhom / MNAj init * W j,BPi

After the weight update, MNAj value is equal to the homeostatic value MNAhom. For this implementation, we used an MNAhom equal to 0.5.

Note that for the CLD group, the hand will be referred to as hand nerves (including palm nerves and fingers’ nerves) to consider residual hand input from the residual arm (see Results section). During model evaluation, when arm stimulation occurs, it is assumed that the palm/fingers nerves are also stimulated. The activity of an internal neuron j is calculated as Y j=W j,Arm * 1+W j,Palmnerve * 1+W j,Fingersnerve * 1. Peripheral reorganisation is also considered during the homeostatic adjustment of weights by considering the stimulation of the residual nerves. To study remapping without behavioural differences between the groups, the stimulation probabilities of arm and palm/fingers residual nerves of CLD individuals remain the same as the arm and palm/fingers probabilities of CTRs.

Initial weights of the model. Our model posits that there is a genetically pre-determined pattern of connections between the body parts and internal neurons (see Fig. S9). These initial connection weights are modified by homeostatic mechanisms. The initial weights are represented by bell-shaped curves, with their peak positions uniformly distributed across the S1 homunculus strip and in line with the typical topography of this region (please see the Supplementary Section ‘Setting of initial connection weights’ for a detailed description of how these weights are calculated). To ensure a weak connection of all body parts with all neurons15, the bell-shaped weights are vertically offset by a small constant value equal to 0.05. The density of tactile nerve fibres in the skin is not uniform; it varies significantly across different parts of the body. Notably, the hand (including the palm and fingers) has a high concentration of Aβ myelinated tactile fibres101. This dense innervation is related to greater tactile sensitivity102 and a larger representation of the hand in the brain103. In the model, this is reflected by assigning stronger connections between the hand and the somatosensory homunculus compared with the connections related to other body parts (Fig. S9, left plot). For the CLD group, to reflect the reduced influence of residual nerves, the original weights associated with palm and fingers are multiplied by a factor of 0.15 (see Fig. S9, right plot). This adjustment reflects the weaker connection between residual nerves and internal neurons. Notice that homeostatic adaptation modifies the strength of the individual connections but does not change the relative proportions of the weights between body parts and a given neuron.

Key observations to be explained by the model. To evaluate the explanatory power of our model, we identified four key empirical findings from the global S1 remapping analysis and investigated whether the model replicates these findings, which is a standard approach in assessing computational models32–34,104. The model must account for both the differences between the CLD and CTR groups, as well as the underlying patterns common to both groups.

Observation 1. The activity peak during arm stimulation is both higher (main effect of limb difference for activity: F 1,226.50 =71.70, p<0.0001) and laterally shifted (main effect of limb difference for position: F 1,255.27 =116.68, p<0.001) in the CLD group compared to the CTR group, as shown in Figs. 5-Arm and S8-Arm, respectively. Notably, this residual arm stimulation also activates a large portion of the hand area (approximate position indicated by the dashed line). This is likely due to the dense concentration of nerves from the missing hand that are anchored at the tip of the residual arm, which still respond to stimulation.

Observation 2. In the CLD group, the activity peaks for the foot (F 1,246.56 =7.43, p=0.007), leg (F 1,246.56 =5.81, p=0.017), torso (F 1,246.56 =7.84, p=0.005; all main effects of limb difference), lower face (although, this was only significant for children, F 1,255.54 =4.68, p=0.032) and upper face (F 1,317.97 =2.99, p=0.084, BF 10 =0.52) are shifted towards the hand area compared to the CTR group, as shown in the corresponding subplots of Fig. 5. This suggests that a local deprivation triggers a global cortical remapping of other body parts to shift towards the area corresponding to the missing hand—laterally for the foot, leg and torso, and medially for the upper and lower face. This shift highlights the brain’s adaptability in reallocating cortical space following sensory deprivation.

Observation 3. The activity peaks were overall significantly higher in the CLD group than in the CTR group (main effect of limb difference for activity: F 1,57.86 =21.38, p<0.001). Alongside the shifts described in Observation 2, this increase in activity suggests that the deprivation of the hand leads not only to a spatial reorganisation but also to a general increase in cortical activity across the entire S1 region. This heightened activity may further impact remapping matrices that are commonly used (such as the activity increase in the deprived cortex, which we replicate in the current study).

Observation 4. CLD and CTR groups exhibit a decrease from the foot to the leg and torso (linear trend: F 1,56.45 =18.30, p<0.001), followed by an increase from the upper face to the lower face (linear trend: F 1,59.42 =34.39, p<0.001). This observation serves as a critical benchmark for our model.

Statistical analysis
Statistical analyses were performed using custom scripts written in Matlab R2020b (MathWorks), R version 4.1.3 (R Core Team, 2022) with RStudio (2021.09.0 Build 351), and Python 3.10.6 with Spyder 5.3.3. Behavioural and fMRI data were analysed using Linear Mixed Models implemented in the R package lmerTest105, based on the lme4 package106. Model parameters were estimated, and variance partitions were derived using the anova function in R, which are the results reported throughout the text.

In all models, participants were treated as random effects. Additionally, tasks in the behavioural session, body parts in both behavioural and fMRI sessions (for univariate analysis), and pairwise labels (for distance analysis) were also considered random effects. The predictor variables generally included age (children/adults) and limb difference (CLD/CTR), unless otherwise indicated. Body parts were included in models predicting percentage of usage and functional activity, with the exception of the lower face in brain analysis, as it was designed as a control body part. For completeness, a model including the lower face was also run. In the behavioural analysis, face usage was analysed separately using a chi-square test to examine distribution differences across the four groups.

In the behavioural analysis, face usage was separately examined using a chi-square test to determine if there was a different distribution of use across the four groups. For evaluating differences in face usage among the 10 CLD participants who showed face usage (five children and five adults), a bootstrap analysis with 5000 iterations was employed. At each iteration, participants were sampled with replacement and divided into two groups mirroring the original group size to create a null distribution, estimating the p value as the proportion of bootstrap mean differences that were more extreme than the observed mean difference.

To test the significance of the correlation between brain measures and compensatory behaviour, a permutation test was conducted. This involved 5000 iterations where behavioural and brain deviance data were shuffled within each participant, maintaining the original structure but randomising pairings of deviance measures. Spearman correlation coefficients between behavioural and brain deviance measures were calculated for each participant in each iteration and averaged for each group (children and adults) to form a null distribution of mean correlation coefficients. The observed mean correlation coefficient was compared to this distribution to determine the empirical p value, representing the proportion of permuted datasets producing a correlation coefficient as extreme or more extreme than the observed coefficient. This analysis assessed the significance of observed brain-behaviour correlations, shedding light on the relationship between behavioural changes and corresponding brain changes in CLD individuals.

Contrasts of interest were estimated using the R package emmeans 1.10.1107. For trending results of interest, we reported the corresponding Bayes factor (BF 10), defined as the relative support for the alternative hypothesis. While it is generally agreed that it is difficult to establish a cutoff for what consists sufficient evidence, we used the threshold of BF<1/3 as sufficient evidence in support of the null, BF>3 as sufficient evidence in support of the alternative hypothesis, and 1/3<BF<3 as inconclusive evidence, consistent with others in the field108,109.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This well-written and very interesting manuscript provides evidence that challenges the idea of radical cortical reorganization in individuals with congenital limb difference (CLD). By combining behavioral analysis, neuroimaging, and computational modeling, the study supports the proposal that observed brain changes reflect the potentiation of pre-existing neural circuits via homeostatic plasticity and not the emergence of novel functions. The combination of careful empirical work and computational modeling advances basic science while having implications for clinical work. The findings also have relevance for elucidating developmental mechanisms around plasticity.\n\nThe manuscript has many strengths, and I only have a few suggestions for the authors to consider. Carrying out this kind of research is very challenging, and I applaud the authors' efforts and their success in bringing the study to this point.\n\nMajor Strengths:\n• Theoretical Alignment: The study is well-grounded in contemporary debates on cortical plasticity. The authors argue that remapping is a consequence of strengthening existing, latent connections through homeostatic and Hebbian plasticity mechanisms.\n• Methodological Rigor: The experimental design demonstrates exceptional attention to detail. The use of soft pneumatic actuators for consistent tactile stimulation across age groups is a notable achievement. The authors carefully document quality control procedures and provide thorough demographic matching.\n• Sophisticated Analysis: The study employs both univariate and multivariate analyses, accounts for potential confounds, and integrates behavioral and neural measures.\n• Computational Model: The computational model, implementing homeostatic plasticity through synaptic scaling, is a major strength. The model successfully replicates the study's key empirical findings, suggesting that homeostatic plasticity is a viable mechanism to explain the observed remapping.\n\nPossible Areas for Revision:\n• Implications of Focus on S1. While the authors acknowledge comparable representational structures between S1 and M1, they could briefly discuss how their findings align with the literature on S1-M1 interaction, acknowledge potential differences in plasticity mechanisms between sensory and motor areas, and highlight how the S1 findings might inspire future work on motor system remapping.\n• Behavioral Analysis: The correlation between S1 map shifts and behavior was relatively weak. To probe this further, the authors might incorporate more detailed measures of compensatory behaviors, such as frequency and types of movements used during daily tasks, examine temporal dynamics of compensatory behavior and S1 remapping, and consider the degree to which compensatory behaviors might be task specific. This might be too much for the current manuscript but could be considered.\n• Passive vs. Active Stimulation: I do wonder about the distinction between the passive stimulation used in the task, and active stimulation, where the participant performs a movement and receives sensory feedback. That said, I understand the constraints of what can be done in the scanner.\n• Interpretation of S2 Findings: There was a significant interaction effect between age and body part in the secondary somatosensory cortex (S2), but no significant interactions with limb difference. The authors could discuss the different functional roles of S1 and S2 and how they might relate to the observed differences in remapping. If remapping is primarily localized to more primary sensory areas, what are the implications of that?\n• Role of Upper Face in CLD Re-Mapping: More discussion could be included regarding the weak trend of a main effect of limb difference observed in the upper face in S2. The authors could discuss whether this represents a non-specific spread of activation or a small degree of functional reorganization.", "reviewer_2": "This manuscript investigates the representation and plasticity of sensorimotor functions in individuals with congenital limb absence, focusing on the organization of sensorimotor cortical areas, motor intention, and effector-independent action representations. The authors combine functional MRI (fMRI), behavioral tasks, and a cohort of congenital one-handers to show that cortical hand areas are repurposed for multi-effector functions. Notably, they show that sensorimotor hand territories can support actions typically attributed to other body parts (e.g., foot or residual arm), indicating effector-independent functional representation. It’s a significance research and the findings are novel and important, with significant implications for our understanding of cortical plasticity, motor intention, and the functional architecture of the sensorimotor system.\n\nOverall, the experimental design is sound, and the data support the main conclusions. The use of multiple cohorts (including those with congenital limb absence and controls), combined with behavioral measures and neuroimaging, strengthens the validity of the claims. The manuscript is well written and generally clear. The introduction contextualizes the work within current debates on plasticity and sensorimotor organization. The discussion appropriately addresses alternative explanations.\n\nHowever, several aspects require further clarification or expansion:\n• The definition and operationalization of \"effector-independent\" representations could be more clearly explained and distinguished from compensatory plasticity.\n• The statistical approach in some imaging analyses (e.g., ROI selection, correction for multiple comparisons) needs more detailed description, particularly in the supplementary information.\n\nThe behavioral and neuroimaging methodologies are appropriate and robust. The authors use a well-justified set of tasks to isolate motor intent from execution, and they include relevant control conditions. However:\n• Ethical Approval and Data Management: Lines 740-752]: The manuscript states that recruitment was carried out in accordance with the University College London and Cambridge University research ethics committees (17205/001). Could the authors please clarify whether this reference number corresponds to a single ethics application or to multiple approvals for a multi-site study? Given that data collection took place across multiple locations (BUCNI in London and CBU in Cambridge), it would be helpful to provide additional information on how ethical approval was managed across sites.\nSpecifically:\no Was this study approved under a central ethics committee as a multi-center protocol, or were separate local approvals obtained at each institution?\no Were formal data sharing agreements or protocols established between the sites, particularly given the sensitive nature of data collected from children and clinical populations?\n• More transparency is needed regarding the behavioral task performance of participants with limb , particularly in terms of accuracy and timing metrics\no Lines 795-797: The authors mention that four raters evaluated the videos. Were these raters co-authors? If so, please specify their initials for transparency. Additionally, did the raters have prior experience in similar tasks? It would be helpful to provide more detail on the rating protocol — for example, the instructions they received, who served as the primary rater, and how disagreements were resolved.\no Lines 808-810: The computation of component C2 is not clearly described. It is also unclear what is meant by “actually engage.” Please clarify how this component was derived and what behavioral or analytical criteria were used\n• The use of an individualized or group ROI for fMRI analyses should be justified more clearly.\n• Information on fMRI preprocessing, movement exclusion thresholds, and participant level exclusion criteria should be expanded.\no Lines 975–978: The authors mention using a framewise displacement threshold of 0.9 mm to flag volumes for exclusion. It would be helpful to clarify the rationale for selecting this specific threshold, as it can significantly affect data retention. A reference would support this choice, or alternatively, the authors could justify its appropriateness for this particular dataset. Additionally, in line 978, the text states “10” based on the context, this seems likely to be a typo. Please verify and correct as needed.\n\nMinor:\n• Line 115: Supplementary Table S1 no Supplementary Table 1\n• Figure S1/S2: is quite complex and not easy to interpret, particularly due to the arrangement of different body parts across the subplots. To improve readability, I would suggest adding subtle vertical divider lines between the major body regions (e.g., feet, legs, torso, etc.), which could help the reader more easily distinguish the corresponding sections across subplots.\n• Why did the authors choose to report the standard error of the mean in the figures instead of the standard deviation?\n• Figure 1B: I appreciate the illustrative representation showing the co-use of different body parts (below images). However, I found the image selected for the Arm co-use condition a bit confusing. To me, it looks more like a depiction of Hand co-use, as the participant seems to be using the other hand to perform the task. Could you please clarify this?\n• Line 141 ..Supplementary Tables S2 and specify the specific tables\n• Figure 2B: To support the text describing the stimulation locations for the different groups (lines 867–878), I would suggest expanding Figure 2B and adding schematics for the CLD and control participants. Include the placement locations for both groups, label each location (e.g., “1”, “2”, “3”, etc.) clearly within the figure, and indicate “non-dominant” for control participants directly in the figure. These additions would make the figure more informative and help readers easily identify the stimulation sites across groups directly from the figure.\n• Line 260: The text refers to Figure 3A as showing a functional ROI from an exemplar participant. However, the figure legend describes group-averaged Z values and does not clearly indicate whether the ROI brain image represents a single participant or a group-level or template example. For clarity, I suggest the authors explicitly state whether the brain plot is from an individual participant or a standard reference brain in the legend\n• Line 548: Discussion : It might be more reader-friendly to present the main results or main findings in the first paragraph, so that readers have a clear sense of the overall direction of the discussion\n• Line 548: Discussion: When citing the results that support each discussion point, including figure or table references would improve readability\n• Lines 718-719: Could the authors clarify whether a power analysis was conducted to determine the sample size? If not, please explain how the number of participants (16 CLD children and 16 CLD adults) was established and whether it was based on prior studies, feasibility, or other considerations\n• Line 724: was the CLD participant excluded a child or adult participant?\n• Line 865: The reference to Figure 1B appears to be incorrect; it should be Figure 2B.\n• Line 992: While MNI space is commonly understood within the neuroimaging community, I would recommend spelling out the acronym at its first mention, to ensure clarity for readers who may not be experts in the field. Same for FEAT.\n• Line 1002: Figure 2B instead of general Figure 2\n• Line 1026 and 1043: please specify which panel of Figure 2 you are referring\n• Line 1095 you repeated two times lower face\n• Lines 1102–1106: the authors report summary values such as “(2/469; 7 × all participants)” and later “(20/467; 7 × all participants minus 2)” in reference to cases where peak activity was not identifiable and instances of double-peaked activation patterns, While the general meaning can be followed, I recommend clarifying these expressions more explicitly to help readers understand how these numbers were derived.\nIn particular, if I understand correctly, the number of participants included in the fMRI analyses was 15 CLD children, 16 CLD adults, 20 control children, and 15 control adults (as stated in lines 737–740), which would yield a total of 66 participants. Therefore, 7 × all participants should result in 462, not 469. Could the authors please confirm whether this discrepancy is due to a typo, inclusion of additional data, or another factor?", "reviewer_3": "Summary: In this article, the authors introduce several key aspects that fill significant gaps in our current understanding of the differences between control subjects and CLD subjects' somatotopic maps. The authors specifically address differences between children and adults with CLD. Interestingly, they find similarities between the children and the adult CLD subjects that indicate somatotopic map changes occur before age 5 and that these changes are maintained into adulthood. These results are not particularly surprising when viewed in the context of animal research on changes in somatotopy. For these changes in the homunculus, they formulated a biologically inspired model that is very simple in construction. Even with its simplicity, they show that it can reproduce many of the differences seen between the CLD subjects and the controls simply with the inclusion of homeostatic plasticity and several justifiable assumptions, such as differences in the distribution of sensory input to the different body regions of CLD vs. CTR subjects. The use of more ecologically valid Semi-Naturalistic movements is a nice addition. However, some of these movements, or sub-tasks, are likely new to the younger subjects compared to the adults, for whom unscrewing something is much more familiar. Overall, the paper is significant and insightful, bringing new insights forward in a clear, easy-to-understand article. Some issues are raised below; most are meant to increase readability.\n\nExperimental Section:\n1. The co-use index used has some assumptions built in. One is that the children’s apparent variability of body part use is due to some general higher variability in motor planning or a higher expletory rate. Is it possible that the children get fatigued or feel pain when making some of these new movements while interacting with the objects? In essence, how would your measure differentiate subjects with two solutions for completing a sub-task that they switch between rather than something more akin to a higher exploration rate in the motor output space?\n2. In Figure 1, the picture for Torso seems to be of arm use. The picture for the arm shows the use of the elbow joint to hold an item. Are these terms used regularly in this research area for these body parts?\n3. Figure 1, Please use the term co-use for subplot A’s y-axis, or change the name of the measure you use to co-use complexity.\n4. It might be helpful to use different colors for the children vs. the adult CLDs, but it could just be this review’s eyes that had difficulties seeing the differences in the line plots.\n5. Figure 2.c, It might be helpful to put a simple landmark in the three sub-plots on the right-hand side to allow for easy comparisons between Foot, Leg, and Torso, as these are all within the one box on the main associated image.\n6. Figure 5, please indicate what the x-axis is for the reader.\n7. As divisive normalization is a canonical cortical computation known to occur in sensory areas, it would be nice if the authors had any insights on how such normalization mechanisms might show up in the context of CLD and how such neuro-dynamic mechanisms might muddy the waters of the clean model and interpretation the authors would like to champion.\n8. Would it be helpful for the reader if you drop the term Hand loss, as it implies there was a hand and that it was lost, which would be a rather different situation than the hand never having been formed or not formed completely, as the authors are fully aware.\n\nModeling:\n1. The proposed model is elegant in its simplicity. This model could be formulated as a modified Radial Basis Network, which has previously been used in sensorimotor studies. One would simply need to use as input the sparse set of body positions rather than a continuous input space, include a post-activation gain, which is known to exist in biological systems, which would act as the synaptic scaling mechanism, and add the offset included in your work. This may or may not help make your model clearer for comparison with other RBF network publications on sensorimotor map changes. In addition, the simple gain formulation would allow relation to gain-field work in the sensorimotor literature. At least the authors could give the parameters of the function used to determine the initial weight distributions in the model they propose. This would allow others to replicate the results.\n2. Many studies have examined different types of neural plasticity and changes in the somatosensory cortex due to stroke, amputation, nerve block, etc. It is unclear from the literature that has been cited if the authors are fully aware of this literature. Perhaps adding some of this background and results from rodents and primates would help round out the story being presented. That is, make clear the models that are available to ask the types of questions addressed in this work that would include more than just homeostatic mechanisms and are more biologically realistic. (optional)\n3. It seems that the results obtained would be expected given the initial weight distributions, touch probabilities, and coupling of the arm/palm/fingers. It is great to show that this sensible pattern of inputs and a simple Homeostatic Plasticity rule (normalization) give results like those seen in the subjects, but it does seem obvious this model abstraction would give these results.\n4. The model doesn’t include Hebbian plasticity, which leaves the modeling short of describing or addressing the individual differences in body part use compared to the mean for the given groups and how this is correlated with differences in somatotopic responses shown in the human subjects’ data.\n5. As the model seems to be a large part of the paper, it might make sense to put more of the model results in the main section?\n6. It seems you are using weight matrix indexing that is the opposite of the norm where it is usually W i,j where i is the neuron index that the input j is going to that is W to,from, where you are using W from,to. This may confuse some readers.\n7. In the methods section it may be helpful to make the indexing more explicit as well, such as the summation from j = 1:10 as there are 10 body parts.\n8. Line 1205, where did the 15% come from and the % for the other body regions?\n9. Figure 6, it would be nice to see direct comparisons between the model and the mean of the subjects’ data. Along these lines have you tried optimizing the standard deviation of your gaussian weights? The real data seems to show more localized receptive fields as compared to the model, which would seem to be easily rectified?\n10. Figure S9, please indicate the parameters of the assumed gaussian used for the normal receptive fields and the modified ones for the nerves.\n11. Figure S12, same as above for S9.\nThe code: \"Activations should be replaced by weight in the figure.\" perhaps take out such notes.\nThere are grammatical issues in the text that you might want to fix = run spell check etc.\n\nThe code does not state who the author of it is. There are plenty of grammatical errors etc. The code works as it should and seems to produce results as it should."}, "author_response": "We are very grateful for the reviewers’ positive, thoughtful and constructive feedback which helped us refine the clarity, transparency, and theoretical framing of the manuscript. In revising the manuscript, we have clarified and strengthened the theoretical framework, including a more explicit treatment of homeostatic mechanisms in shaping S1 remapping, and a clearer discussion of how compensatory behaviour modulates this process across development. The presentation of results has been improved in response to helpful suggestions regarding methods transparency, and the revised discussion now more clearly articulates how our findings inform current models of cortical plasticity. We also added control analyses to demonstrate the robustness of our key findings, although no major changes to the data or conclusions were necessary.\n\nIn the process of implementing these revisions, we identified a minor human error affecting the behavioural ratings for one participant. While this necessitated slight updates to the behavioural statistical analyses, the overall pattern of results remained unchanged and no conclusions were affected.\n\nBelow, we provide a point-by-point response to each comment. Original comments are in bold with our responses in blue. For the reviewers’ convenience, we also include excerpts in green with the related changes to the revised manuscript.\n\nReviewer #1 (Remarks to the Author):\nThis well-written and very interesting manuscript provides evidence that challenges the idea of radical cortical reorganization in individuals with congenital limb difference (CLD). By combining behavioral analysis, neuroimaging, and computational modeling, the study supports the proposal that observed brain changes reflect the potentiation of pre-existing neural circuits via homeostatic plasticity and not the emergence of novel functions. The combination of careful empirical work and computational modeling advances basic science while having implications for clinical work. The findings also have relevance for elucidating developmental mechanisms around plasticity.\n\nThe manuscript has many strengths, and I only have a few suggestions for the authors to consider. Carrying out this kind of research is very challenging, and I applaud the authors' efforts and their success in bringing the study to this point.\n\nMajor Strengths:\n• Theoretical Alignment: The study is well-grounded in contemporary debates on cortical plasticity. The authors argue that remapping is a consequence of strengthening existing, latent connections through homeostatic and Hebbian plasticity mechanisms.\n• Methodological Rigor: The experimental design demonstrates exceptional attention to detail. The use of soft pneumatic actuators for consistent tactile stimulation across age groups is a notable achievement. The authors carefully document quality control procedures and provide thorough demographic matching.\n• Sophisticated Analysis: The study employs both univariate and multivariate analyses, accounts for potential confounds, and integrates behavioral and neural measures.\n• Computational Model: The computational model, implementing homeostatic plasticity through synaptic scaling, is a major strength. The model successfully replicates the study's key empirical findings, suggesting that homeostatic plasticity is a viable mechanism to explain the observed remapping.\n\nWe thank the reviewer for their positive feedback and support.\n\nPossible Areas for Revision:\n1) Implications of Focus on S1. While the authors acknowledge comparable representational structures between S1 and M1, they could briefly discuss how their findings align with the literature on S1-M1 interaction, acknowledge potential differences in plasticity mechanisms between sensory and motor areas, and highlight how the S1 findings might inspire future work on motor system remapping.\n\nWe thank the reviewer for this thoughtful comment. We agree that, based on current literature, homeostatic plasticity is a hallmark of S1 and other primary sensory cortices (Feldman, 2009; Lee & Kirkwood, 2019; Turrigiano & Nelson, 2004), whereas M1 is more dominantly associated with Hebbian and structural forms of plasticity (Rioult-Pedotti et al., 2000; Xu et al., 2009). However, while these mechanistic differences may exist at the microcircuit level, learning of sensorimotor associations requires plasticity downstream from sensory cortex to transform sensory signals to appropriate behavioural responses (LeMessurier & Feldman, 2018). As such, the two regions are known to be tightly coupled in their function, development, and representational architecture.\n\nThis close relationship is well-documented. For instance, in humans –– tactile learning generalises in a use-dependent manner (Dempsey-Jones et al., 2019), while motor learning alters sensory feedback gains in S1 (Ebrahimi & Ostry, 2024). Therefore, functionally meaningful plasticity often emerges from co-dependent sensory-motor processes. In rodents, tactile information, including object texture and location, is computed in S1 through integration of motor and sensory signals (Petersen, 2019). Following texture discrimination learning, both M1- and S2-projecting populations become more tuned to textures post-learning, suggesting that task-relevant sensory information is more effectively routed to downstream areas (Chen et al., 2015). Considering the multitude of brain regions and mechanisms involved in both perceptual and motor learning, the global plasticity observed in these studies may not come as a surprise.\n\nMost relevant to the current work, our previous studies consistently report mirrored plasticity and stability effects in S1 and M1. In Hahamy et al. (2017; 2019), we demonstrated that adults with congenital limb difference show remapping from multiple non-neighbouring body parts into the deprived hand region across both S1 and M1. In a separate study focused on face remapping (Root et al., 2022), we found again that remapping of compensatory effectors (e.g., lips and tongue) occurs consistently in both S1 and M1, using multivariate pattern analyses. In acquired amputees that do not show remapping, we find similar (stable, i.e. not remapped) hand and body maps in S1 and M1 across studies (e.g. Schone et al., 2025; Wesselink et al., 2019). These converging findings reinforce the view that, in both congenital and acquired limb difference, S1 and M1 share representational architecture and likely undergo plasticity in concert. These insights also align with predictive coding frameworks, which posit that hierarchical cortical circuits continuously update internal models based on sensory prediction errors (Rao, 2024).\n\nIn our revised manuscript, we now highlight that experience-dependent changes in sensory population codes likely reflect plasticity across multiple levels of the sensorimotor hierarchy — from local cortical circuits and ascending sensory pathways, to top-down cognitive projections and descending motor signals (LeMessurier & Feldman, 2018). One proposed mechanism is the release of a global plasticity signal (e.g., triggered by reward), which enhances synaptic change specifically at recently active synapses (Petersen, 2019), thus linking local and distributed plasticity processes.\n\nFor the reviewer’s convenience, below we provide the relevant Discussion revisions:\n‘Could these far more dramatic effects of deprivation- versus use-dependent plasticity observed in the current study be a mere consequence of our focus on sensory versus motor cortex? Indeed, homeostatic plasticity is a hallmark of S1 and other primary sensory cortices (Feldman, 2009; Lee & Kirkwood, 2019; Turrigiano & Nelson, 2004), whereas M1 is more dominantly associated with Hebbian and structural forms of plasticity (Rioult-Pedotti et al., 2000; Xu et al., 2009). Our focus on S1 was primarily derived from methodological considerations for the fMRI experiment. To ensure consistency across participants and groups, and minimise head movements, it was highly impractical to rely on the children to perform instructed movements. […] We were also motivated by the tight functional similarity between primary sensory and motor cortex in controls (Ejaz et al., 2015) and individuals with CLD (Hahamy et al., 2017; Hahamy & Makin, 2019; Striem-Amit et al., 2018; Tucciarelli et al., 2024; Wesselink et al., 2019). Indeed, given the key roles of S1 in motor planning (Ariani et al., 2022), learning (Ebrahimi & Ostry, 2024) and execution (Shelchkova et al., 2023), as well as predictive coding frameworks (Rao, 2024), it is difficult to conceive a unidirectional functional architectural change in one node but not the other. In humans, tactile learning generalises in a use-dependent manner (Dempsey-Jones et al., 2019), and motor learning alters sensory feedback gains in S1 (Ebrahimi & Ostry, 2024), supporting the idea that functionally meaningful plasticity often emerges from co-dependent sensory-motor processes. In rodents, tactile information, including object texture and location, is computed in S1 through integration of motor and sensory signals (Petersen, 2019). Therefore, while mechanistic differences may exist at the microcircuit level, experience-dependent plasticity likely reflects across multiple levels of the sensorimotor hierarchy (LeMessurier & Feldman, 2018). Here it is also important to highlight that our local remapping findings are a replication of previous studies using active movements across both S1 and M1 (Hahamy et al., 2017; Hahamy & Makin, 2019); and that our present results are derived from the higher sub-divisions of S1 (BA1 and 2), which may be less closely associated with thalamic deprivation, relative to BA3A and 3B (see Supplementary Figure S7).\nTaken together, these considerations suggest that the striking deprivation-driven plasticity we observe in S1 is unlikely to be an artefact of our sensory focus, but rather reflects deeply integrated sensorimotor processes that shape cortical organisation from early development. Nevertheless, we cannot rule out the possibility that use-dependent plasticity plays a more prominent role in M1 ‘\n\n2) Behavioral Analysis: The correlation between S1 map shifts and behavior was relatively weak. To probe this further, the authors might incorporate more detailed measures of compensatory behaviors, such as frequency and types of movements used during daily tasks, examine temporal dynamics of compensatory behavior and S1 remapping, and consider the degree to which compensatory behaviors might be task specific. This might be too much for the current manuscript but could be considered.\n\nFor our main analysis, we were looking for an intuitive and relatively straightforward index for both brain and behavioural deviation from the control group, which we believe we successfully captured. By convention, the correlations we report between brain and behaviour in children of 0.59 is generally considered a moderate-to-strong association (please note this value was slightly adjusted following the identification of a small error in one data point for a CLD child during data extraction).\n\nTo further validate this key result, we performed a Leave-One-Subject-Out (LOSO) validation. For each group, we iteratively excluded one participant, then recalculated the mean correlation for the remaining individuals. This process yielded a distribution of \"leave-one-out\" mean correlations for each group. The LOSO validation demonstrated high consistency in our group-averaged correlations. For the CLD children group, the mean of the LOSO correlations was 0.593 (standard deviation = 0.0156, range = 0.0571). This perfectly aligns with the original group mean of 0.593. Similarly, for the CLD adult group, the mean of the LOSO correlations was 0.393 (standard deviation = 0.0215, range = 0.0714), consistent with its original mean of 0.393. The small standard deviations and narrow ranges of the LOSO means within both groups indicate that our reported average correlations are robust and not unduly influenced by any single participant. This rigorous validation, which we included in the supplementary section Supportive analysis for the impact of behaviour on topographic shift, provides increased confidence in the representativeness and stability of our findings.\n\nIn response to the reviewer’s comment, we also developed a complementary measure that is based on frequency. Our compensatory measure for correlating brain with behaviour was initially defined as the proportion of time each body part was used across sub-tasks. This provided an estimate of how intensively a body part was used (e.g., across all tasks, the residual arm was used for 60% of the total task duration, on average). However, as the reviewer correctly noted, this measure did not estimate frequency—that is, in how many sub-tasks a body part was used (e.g., the residual arm was used in 13 out of 15 sub-tasks). Therefore, we conducted a new analysis using an index based on relative frequency (i.e., if a residual arm was in used 13 out of 15 sub-tasks, the index would be 0.867). As in our original approach, we analysed deviations in activity profiles and behavioural performance (this time using relative frequency) for each CLD individual relative to their peer control group. We obtained very similar results. Specifically, the averaged Spearman correlation was 0.52 (p < 0.001) and 0.27 (p = 0.03) for the children and adults, respectively. Just as a reminder, we originally obtained 0.59 (p < 0.001) and 0.39 (p = 0.003) for the children and adults, respectively. Furthermore, also in this new analysis, the children’s mean correlation was significantly larger than the adults’ (t=3.12, p=0.002; originally it was t=2.25, p=0.016). These results corroborate our original observations that unique behavioural adaptations that are favoured by individuals are also reflected in variations in S1 brain topography, particularly during early development.\n\nThis additional analysis is reported in the also reported as supplementary materials as follows:\nTo further validate our correlation analysis, we conducted an additional permutation test. This time, our compensatory behaviour measure was based on the relative frequency each body part was used across all sub-tasks (e.g., if the residual arm was used in 13 out of 15 sub-tasks, the index was 0.867). Similar to our original approach, we analysed deviations in activity profiles and behavioural performance for each CLD individual relative to their peer control group. We observed very consistent results: The averaged Spearman correlation was 0.52 (p < 0.001) for children and 0.27 (p = 0.003) for adults. Moreover, children's mean correlation remained significantly larger than adults' (t=3.12, p=0.002). These findings strongly corroborate our original observations: individual behavioural adaptations are indeed reflected in variations in S1 brain topography, particularly during early development.\n\nTo further validate our key result, we conducted a Leave-One-Subject-Out (LOSO) validation. For each group, we iteratively removed one participant and then recalculated the mean correlation for the remaining individuals. This process created a distribution of \"leave-one-out\" mean correlations for each group. The LOSO validation confirmed high consistency in our group-averaged correlations. For the CLD children, the mean of these LOSO correlations was 0.593 (standard deviation = 0.0156, range = 0.0571), perfectly matching their original group mean of 0.593. Similarly, for the CLD adults, the LOSO mean was 0.393 (standard deviation = 0.0215, range = 0.0714), consistent with their original mean of 0.393. The small standard deviations and narrow ranges observed within both groups indicate that our reported average correlations are robust and not disproportionately affected by any single participant.\n\n3) Passive vs. Active Stimulation: I do wonder about the distinction between the passive stimulation used in the task, and active stimulation, where the participant performs a movement and receives sensory feedback. That said, I understand the constraints of what can be done in the scanner.\n\nWe agree with the reviewer that this issue merits careful consideration. Active stimulation is likely to provide a richer and more naturalistic input to S1, engaging not only multiple sources of afferent input (skin, deeper tissue, proprioceptors), but also efferent signals (e.g. efference signals).\n\nTo evaluate the impact of using passive stimulation in the current study, we first note that the local remapping effects we report in S1 closely replicate our earlier findings in adult congenital one-handers using active paradigms (Hahamy et al., 2017; Hahamy & Makin, 2019), as well as those of others. Importantly, Striem-Amit et al. (2018) also reported comparable remapping using passive and active stimulation, suggesting that the phenomenon is not strictly contingent on stimulation mode.\n\nThese observations also align with our previous systematic comparison of passive and active somatotopy in S1 (Sanders et al., 2023), in which, crucially, we found no significant differences in multivariate representational structure and spatial map layout between the two paradigms, using 7T neuroimaging. This work directly validates the use of passive stimulation for probing functionally relevant topography and plasticity in S1. Further converging evidence comes from a recent study in tetraplegic patients undergoing intracortical electrode implantation, where somatotopic maps derived from finger tapping (motor-driven fMRI) were found to closely correspond with those evoked by direct microstimulation of the same S1 sites (Downey et al., 2024). This provides further direct validation that fMRI responses to motor tasks engage the same topographic architecture as tactile stimulation. Third, in our face remapping study (Root et al., 2022), although we used an active motor paradigm, we included some supplementary analysis using passive stimulation to validate our methodology. Taken together, these findings indicate that remapping in S1 is robust across active and passive stimulation modes.\n\nIn combination with our response to Comment 1 — noting that behaviourally relevant plasticity is likely to shape the entire sensorimotor hierarchy — this convergence of evidence gives us confidence that the passive paradigm used here provides a valid and reliable window into S1 plasticity, particularly in paediatric populations where active paradigms are less feasible due to compliance limitations.\n\nIn the revised manuscript we bolstered our original discussion on this issue as follows:\n‘We were encouraged by previous research both by ourselves and others showing minimal differences in S1 organisation and remapping between active and passive somatotopy protocols (Root et al., 2022; Sanders et al., 2023; Striem-Amit et al., 2018). Somatotopic maps derived from finger tapping (motor-driven fMRI) closely correspond with those evoked by direct microstimulation of the same S1 sites in tetraplegic patients undergoing intracortical implantation (Downey et al., 2024). These studies provide direct validation that fMRI responses to motor tasks engage the same topographic architecture as tactile stimulation.’\n\n4) Interpretation of S2 Findings: There was a significant interaction effect between age and body part in the secondary somatosensory cortex (S2), but no significant interactions with limb difference. The authors could discuss the different functional roles of S1 and S2 and how they might relate to the observed differences in remapping. If remapping is primarily localized to more primary sensory areas, what are the implications of that?\n\nWe appreciate the reviewer’s interest in our S2 findings. Supporting this interpretation, we observed significant correlations between representational dissimilarity patterns in S1 and S2 across participants. These correlations were present across all groups, confirming that S2 carries similar broad-scale somatotopic information. However, the strength of these correlations was not modulated by limb difference, and tended to be weaker in children than in adults, consistent with developmental refinement rather than experience-dependent remapping (Nevalainen et al., 2014). This further supports the view that S2 reflects core body part distinctions but may be less sensitive to the specific plasticity effects observed in S1.\n\nHowever, we encourage a cautious interpretation of these findings for conceptual and methodological reasons. Relative to S1, S2 representations are thought to be less spatially distinct (Ruben et al., 2001) and more influenced by task demands (e.g. object-related features (Reed et al., 2004), multisensory integration (Gentile et al., 2013)). Therefore, the current paradigm (abstract stimulation across individual body parts) may not have provided the ideal task to study S2 representation. Consequently, while activity levels were relatively high, the representational dissimilarity values observed in our dataset were correspondingly reduced. This inherently limits the sensitivity of our analysis and makes it more difficult to detect subtle effects such as remapping.\n\nSecond, in the absence of a dedicated individual-level functional localiser, our S2 ROI was defined using an atlas-based anatomical label constrained by group-level functional activation. This approach, while conservative and consistent with prior work, may not precisely capture inter-individual variability in S2 functional anatomy, further limiting sensitivity.\n\nTaken together with the key result here (non-significant differences), the lack of a remapping effect in S2 should be interpreted with caution, which is why we chose to include this analysis in the Supplementary Materials rather than the main text.\n\nIf we were to speculate about the meaning of these findings, we would argue that the very dramatic remapping we see in S1 might not have functional relevance, after all. This interpretation will also be consistent with the (relatively) far less impressive relationship between remapping and behaviour we report.\n\nWe have clarified this rationale in the revised Discussion and elaborated on these considerations in the revised supplementary section as follows\n‘The impressive compensatory behavioural abilities seen in these clinical cases, such as echolocation-based navigation or Braille reading, may be attributable to learning-related recruitment of higher-level cortical computations that can be functionally re-focused on residual primary area representations. Our current study confirms this perspective and adds to it by showing a lack of clear down-stream readout of the S1 remapping effects by S2 (reported in Supplementary Materials and Figure S14). Together, our findings demonstrate that deprivation effects can “passively” contribute to extensive topographic remapping, above and beyond previous accounts.’\n\n‘This pattern of results complements, yet clearly diverges from, the robust deprivation-related remapping observed in S1. Across all groups, pair-wise representational dissimilarity values in S2 correlated significantly with those in the deprived-hand area of S1, confirming that broad somatotopic motifs are captured in our S2 ROI. While we found some evidence to support that S2 continues to refine across development (see Nevalainen et al., 2014 for related results), neither activity levels nor representational distances in S2 were modulated by limb difference. This dissociation is not unexpected given the different functional roles of the two areas. Compared with S1, S2 representations are less spatially segregated (Ruben et al., 2001) and are more susceptible to task demands, including object-centred features (Reed et al., 2004) and multisensory integration (Gentile et al., 2013). Our abstract, body-part-specific stimulation paradigm may therefore have lacked the ethological richness required to expose functional plasticity in S2. Together with the inconclusive Bayes Factor values, which do not provide sufficient evidence to confirm the null hypothesis, the absence of a clear remapping signature in S2 should be interpreted cautiously.’\n\n5) Role of Upper Face in CLD Re-Mapping: More discussion could be included regarding the weak trend of a main effect of limb difference observed in the upper face in S2. The authors could discuss whether this represents a non-specific spread of activation or a small degree of functional reorganization.\n\nWe thank the reviewer for raising this point. We believe there may have been a misreading of the results, as we did not observe any trending effects for the upper face in S2. Instead, the relevant trend occurred in S1, where we observed a weak main effect of limb difference for the upper face (forehead), a body part not typically used for compensatory behaviour in congenital limb difference.\n\nAlthough this effect did not reach conventional significance (F = 2.99, p = 0.084; BF₁₀ = 0.52), we interpret it as tentatively consistent with homeostatic plasticity, in contrast to the stronger and functionally specific effects observed for compensatory effectors (e.g., lips, tongue, feet), which likely reflect Hebbian mechanisms. The presence of a weak effect for a non-compensatory effector supports the notion that deprived cortex may also become sensitive to irrelevant or neighbouring inputs in an effort to maintain representational balance, as we elaborate in the Results revised Discussion.\n\n‘Interestingly, when adding the upper face in the model, which is the lateral neighbour of the deprived hand and is not widely used for compensatory behaviour, we found a weak trend towards a main effect of limb difference (F 1, 317.97 = 2.99, p = 0.084), which remained ambiguous following the Bayesian analysis (BF10=0.52). While trend results should be interpreted with caution, this might indicate that the shifts observed in children might not be exclusively shaped by behaviour in earlier stage of development.’\n\n‘As further support to this view, we find that some of the limb differences across S1 may be shared to a small extent (as expressed in trends towards significance) with the upper face, which was specifically included in our experimental paradigm as a control body part that is not relevant for compensatory behaviour. The early establishment of these dramatic remapping suggests a significant role for homeostatic plasticity processes: the deprived cortex may increase its responsiveness to surrounding or residual inputs irrespective of behavioural relevance.’\n\nReviewer #2 (Remarks to the Author):\nThis manuscript investigates the representation and plasticity of sensorimotor functions in individuals with congenital limb absence, focusing on the organization of sensorimotor cortical areas, motor intention, and effector-independent action representations. The authors combine functional MRI (fMRI), behavioral tasks, and a cohort of congenital one-handers to show that cortical hand areas are repurposed for multi-effector functions. Notably, they show that sensorimotor hand territories can support actions typically attributed to other body parts (e.g., foot or residual arm), indicating effector-independent functional representation. It’s a significance research and the findings are novel and important, with significant implications for our understanding of cortical plasticity, motor intention, and the functional architecture of the sensorimotor system.\n\nOverall, the experimental design is sound, and the data support the main conclusions. The use of multiple cohorts (including those with congenital limb absence and controls), combined with behavioral measures and neuroimaging, strengthens the validity of the claims. The manuscript is well written and generally clear. The introduction contextualizes the work within current debates on plasticity and sensorimotor organization. The discussion appropriately addresses alternative explanations.\n\nWe thank the reviewer for their positive feedback\n\nHowever, several aspects require further clarification or expansion:\nClarifications/expansions\n6) The definition and operationalization of \"effector-independent\" representations could be more clearly explained and distinguished from compensatory plasticity.\n\nWe thank the reviewer for highlighting this important conceptual distinction. We would like to clarify that we intentionally refrained from using the term “effector-independent representations” in our manuscript, precisely to avoid implying a mechanistic or functional claim that the deprived hand cortex encodes actions or goals in an abstract, effector-invariant manner. Instead, our interpretation is rooted in a plasticity-based framework, distinguishing between deprivation-driven (homeostatic) and use-dependent (Hebbian) mechanisms.\n\nOur findings of broad remapping—including shifts of body parts that are not immediate cortical neighbours of the hand area—may indeed appear consistent with effector-independent coding. However, we do not claim that the hand cortex encodes functions regardless of which body part is used. Rather, we interpret these findings as evidence of remapping driven by loss of typical input (as demonstrated by our computational model), and further shaped by compensatory behaviours, particularly during early development.\n\n7) The statistical approach in some imaging analyses (e.g., ROI selection, correction for multiple comparisons) needs more detailed description, particularly in the supplementary information.\n\nWe are happy to provide further detail on our statistical approach. We have revised the Methods section and Supplementary Materials to provide a more comprehensive description of the imaging analyses. Specifically, we have expanded the explanation of:\nOur ROI selection procedures (as detailed in response to another comment); The specific group-level contrasts performed, including the rationale for each; The method used for correction for multiple comparisons, including details of the cluster-forming threshold and the cluster-level significance threshold. These clarifications pertain to the maps presented in Figure 2C, Supplementary Figures S4, and S5. We have also addressed other methodological concerns raised by the reviewers to improve the overall clarity and rigor of our reporting.\n\nWe have revised the manuscript to clarify the group-level analysis in the Methods section:\nBefore conducting the group-level analysis, we flipped these second-level images along the x-axis for CLD participants missing the right hand, and for control participants who were left-hand dominant. This standardised the left hemisphere as the ‘deprived/non-dominant’ hand hemisphere across all participants. This step was necessary, as the tactile stimulators were aligned with the missing/non-dominant side. We then conducted two types of group analysis using these second-level contrast images. First, we estimated group-level activation to identify regions engaged during stimulation of each body part for visualisation purposes (Figure 2C, S4). Second, we contrasted body part maps between the CLD and control groups to determine differential activity in response to stimulation (Figure S5). Both analyses employed a random-effect model in FSL (FLAME 1) and a cluster correction to control for multiple comparisons with the following parameters: Z threshold=2.58 (corresponds to a two-tailed p-value of approximately 0.01) and cluster p threshold =0.05. The resulting thresholded maps were then projected on a standardised surface for visualisation purposes (Figure 2C, S4 and S5-Arm). Where statistical significance was not reached, uncorrected maps (p < 0.01) are presented (Supplementary Figure S6, except for Arm).\n\nThe behavioral and neuroimaging methodologies are appropriate and robust. The authors use a well-justified set of tasks to isolate motor intent from execution, and they include relevant control conditions. However:\n8) Ethical Approval and Data Management: Lines 740-752]: The manuscript states that recruitment was carried out in accordance with the University College London and Cambridge University research ethics committees (17205/001). Could the authors please clarify whether this reference number corresponds to a single ethics application or to multiple approvals for a multi-site study? Given that data collection took place across multiple locations (BUCNI in London and CBU in Cambridge), it would be helpful to provide additional information on how ethical approval was managed across sites. Specifically:\n9) Was this study approved under a central ethics committee as a multi-center protocol, or were separate local approvals obtained at each institution?\n10) Were formal data sharing agreements or protocols established between the sites, particularly given the sensitive nature of data collected from children and clinical populations?\n\nWe thank the reviewer for these important clarifications and apologise for the lack of detail in the original submission. The ethics reference number (17205/001) refers to a single ethics application approved by the University College London Research Ethics Committee. This approval explicitly covered data collection at both the London (BUCNI) and Cambridge (CBU) sites, including the recruitment of paediatric participants, and permitted data storage and processing at the Cambridge site.\n\nAll data were stored and analysed exclusively on a secure, designated Cambridge University research computing cluster, in full compliance with institutional data protection protocols. These procedures adhered to the ethical guidelines and General Data Protection Regulation (GDPR) standards in place at both institutions, which maintain closely aligned governance frameworks.\n\nAs the data were never transferred externally or beyond the core research group covered by the ethics application, there was no requirement for additional data sharing agreements. The study remained fully within the scope of the approved protocol at all times.\n\nWe have revised the manuscript to clarify this point accordingly:\n‘Ethics approval for this study was granted by the University College of London research ethics committees (17205/001).’\n\n11) More transparency is needed regarding the behavioral task performance of participants with limb , particularly in terms of accuracy and timing metrics\n\nThank you for highlighting this important detail. In terms of timing, for each task we video coded each frame (each 40 msec) of effector use to obtain a series of effector use bouts. Several body parts were often used simultaneously, resulting in a binary ‘y/n’ coding for whether each body part was used at each frame. To calculate e.g. percentage torso use (Fig 1, Fig S1), we summed the duration of all bouts of torso use and divided that by the total duration of task time across all 15 subtasks. This is now explained in more detail in the Behaviour indices section:\n‘to calculate the percentage of use metric, in each subtask we summed the durations of all bouts using a particular body part and expressed that as a percentage of the duration of subtask time. These percentages were then averaged across subtasks.’\n\nAccuracy is not directly provided here, as it is not a straightforward metric, considering the task. Participants were encouraged to keep trying until they had completed a task. If a participant was becoming discouraged the experimenter gave a small, standardised prompt. If the participant could not complete the task or asked for help, the experimenter assisted them manually and encouraged them to try again in the remaining tasks. In this context, coding accuracy would not be straightforward. Further, the question of interest was how the goal was ultimately achieved using various body parts, as discussed above.\n\n12) Lines 795-797: The authors mention that four raters evaluated the videos. Were these raters co-authors? If so, please specify their initials for transparency. Additionally, did the raters have prior experience in similar tasks? It would be helpful to provide more detail on the rating protocol — for example, the instructions they received, who served as the primary rater, and how disagreements were resolved.\n\nThis important information is now provided in the section Materials & Methods/Compensatory behaviour analysis/ video coding. The primary rater, Laura Bird (L.B.), was also an author. She has extensive ~10-years of experience of assessing typical and atypical children’s performance across a range of standardised and non-standardised tasks. Dorothy Cowie (D.C.) used her own experience of video coding to supervise L.B.: we also took advice from other colleagues on best practice. L.B., supervised by D.C., developed a very full written protocol for video coding. This specified how to deal with common issues identified by L.B.’s initial viewing of the videos: for example, what to do if an object is dropped, or when to cease coding in the instance of inattention. All other raters, named in the acknowledgements, were trained on this using three sample videos for checking and discussion. Raters demonstrated almost perfect agreement on which body part was used (Cohen's kappa = 0.972) and excellent reliability for body part usage duration (ICC = 0.989). As agreement rates were almost indistinguishable, coding was left as per the secondary raters’ rating. Any small disagreements concerned sections of on average <500ms within a 5-minute video.\n\nWe have added the information about the raters’ agreement in the Methods section of the manuscript:\nRaters demonstrated almost perfect agreement on body part used (Cohen's kappa = 0.972) and excellent reliability for body part usage duration (ICC = 0.989). As agreement rates were almost indistinguishable, coding was left as per the secondary raters’ rating.\n\n13) Lines 808-810: The computation of component C2 is not clearly described. It is also unclear what is meant by “actually engage.” Please clarify how this component was derived and what behavioral or analytical criteria were used\n\nWe apologise for not making this point clear. We have made the necessary adjustments to the Methods section and added a new section in the Supplementary Materials to report the equations used to calculate the two components and a related description.\n\n‘The co-use measure used for our behavioural analysis (Figure 1A) is an estimated index that reflects both the number of body parts used and how consistently they were used, with higher values indicating more usage or more consistent usage. The function is the sum of two components: C1 + C2. Component C1 (equation 1) counts the number of body parts that were used during the observation period (an integer value). It is calculated by counting any body part with a usage proportion greater than zero.\n𝑛\n𝐶1 = ∑𝑈 (1)\n𝑖\n𝑖=1\nU is a binary indicator variable for the ith body part which is equal to one if the ith\ni\nbody part was used (p>0) or zero if it was not used (p=0), and n is the total number\ni i\nof body parts considered. Let’s indicate with p the proportion of use of the ith body\ni\npart. Component C2 (equation 2) adjusts the index based on the consistency of\nusage across the used body parts. It is computed as the average of the difference\nbetween each used body part and its proportion.\n∑𝑛 (𝑈 −𝑝 )\n𝑖=1 𝑖 𝑖\n𝐶2 = (2)\n∑𝑛 𝑈\n𝑖=1 𝑖\nThe value of C2 is either zero or negative. It's zero if all body parts were used for\nthe entire task duration. However, it becomes negative if at least one body part was\nused for less than the total task time. Essentially, C2 accounts for differences in\nhow long each body part was engaged.\nA smaller penalty from C2 (meaning a value closer to zero) results in a higher\noverall co-use index. This happens when the body parts used have similar or high\nproportions of engagement time. Conversely, if body part usage is highly uneven—\nfor example, one body part is used extensively while others are barely involved—\nC2 will significantly decrease the co-use index. This reflects a less cooperative or\nbalanced usage of body parts.\nThe final equation to estimate the co-use index is indicated in equation 3:’\nco-use =𝐶1+𝐶2 (3)\n\n14) The use of an individualized or group ROI for fMRI analyses should be justified more clearly.\n\nWe are happy to provide further details regarding our ROI selection strategy and apologies if this wasn’t clear. Our analysis investigated both global and local effects of age and deprivation.\n\nFor global effects, we employed a group ROI, defined by Glasser atlas areas 1 and 2 (Glasser et al., 2016) in MNI space. Since individual functional data were also in MNI space, this approach ensured comprehensive coverage of the post-central gyrus across all participants, as we verified visually. This was further validated by our thumb peak analysis (Figure 2D, 2E and S5), which confirmed that all individual peaks were included within the group mask, suggesting that we were using data from the primary somatosensory region. This method provided an unbiased and efficient way of selecting data, whereas individual anatomical ROIs would have been more labor-intensive and susceptible to subjective variability.\n\nFor local effects on the hand area, we used individual ROIs. This was motivated by the known anatomical variability of the hand area around the pre-central gyrus knob (Yousry et al., 1997). By leveraging our thumb data, we were able to define the hand area in the deprived/non-dominant hemisphere with greater precision and objectivity, based on the thumb peak in the intact/dominant hemisphere.\n\nTherefore, we believe this combination of group ROIs for global analyses and individual ROIs for local analyses represents the optimal strategy for achieving unbiased and precise ROI selection.\n\nTo make our choices clearer to the readers, we added these sentences in the “ROIs definition” section:\n‘As we were interested in studying both global (i.e., along S1) and local (i.e., hand territory) changes due to age and/or deprivation, we defined two different types of ROIs within S1. Specifically, we defined a group ROI for the global analysis and individual ROIs for the local analysis. The group ROI, derived from Glasser atlas (Glasser et al., 2016) areas 1 and 2 in MNI space, provided an unbiased and efficient means of capturing global changes across the somatosensory cortex. In contrast, the individual ROIs, defined based on thumb peak locations, allowed for more accurate and sensitive detection of local changes within the hand territory, given its known anatomical variability (Yousry et al., 1997).’\n\n15) Information on fMRI preprocessing, movement exclusion thresholds, and participant level exclusion criteria should be expanded.\n\nWe have carefully considered the reviewer's comment regarding our fMRI preprocessing and have taken proactive steps to enhance the level of detail. Specifically, we have incorporated an explanation of the fieldmap data preparation and its use in correcting inhomogeneity distortions:\n‘Signal distortions caused by B0 field inhomogeneities were corrected using B0 fieldmaps. The fieldmap magnitude and phase images were processed with FSL's fsl_prepare_fieldmap. No slice-timing correction was applied because the relatively short TR (1450 ms).’\nprovided a clearer description of our volume outlier detection:\nThe 0.9 mm threshold was selected based on previous methodological work by Siegel et al. (2014). Across three runs for children (totalling 99 runs for 33 children), the vast majority of the data (85% of all runs) contained 10 or fewer identified volume outliers which is 4.63% or less of the total volumes with stimulation (around 215 volumes out of 277 total volumes per run) were flagged as outliers. This low proportion of outlier data suggests that children generally remained still during the stimulation.\nand updated the description of data exclusion criteria for the peak analysis:\nNote that for two participants, a clear peak activity (z-statistic exceeding zero, indicating a statistically significant positive activation) was not identifiable for the torso, and therefore these two contrasts (out of 462 total contrasts, i.e., 7 x 66 participants) were excluded from further analysis. Additionally, double-peaked activity patterns were observed in a subset of participants (n=20) and contrasts.\n\n16) Lines 975–978: The authors mention using a framewise displacement threshold of 0.9 mm to flag volumes for exclusion. It would be helpful to clarify the rationale for selecting this specific threshold, as it can significantly affect data retention. A reference would support this choice, or alternatively, the authors could justify its appropriateness for this particular dataset. Additionally, in line 978, the text states “10” based on the context, this seems likely to be a typo. Please verify and correct as needed.\n\nThe 0.9mm is a default threshold for motion scrubbing, and is based on previous work by Siegel et al. (2014), which systematically investigated optimal thresholding strategies for task-based fMRI data in an age cohort comparable to our study. We routinely use this threshold in our lab in fMRI publications.\n\n‘The 0.9 mm threshold was selected based on previous methodological work by Siegel et al. (2014).’\n\nThe “10” in line 978 indicates that 85% of all runs across the 33 children (99 runs in totals) contained 10 or fewer outliers. The value 10 represents the 4.63% of the total stimulated volumes (around 215 volumes out of 277 total volumes per run), as reported in the manuscript. We have made this point clearer in the new version of the manuscript:\n‘Across three runs for children (totalling 99 runs for 33 children), the vast majority of the data (85% of all runs) contained 10 or fewer identified volume outliers which is 4.63% or less of the total volumes with stimulation (around 215 volumes out of 277 total volumes per run) were flagged as outliers. This low proportion of outlier data suggests that children generally remained still during the stimulation.’\n\nMinor:\n17) Line 115: Supplementary Table S1 no Supplementary Table 1\n\nWe thank the reviewer for pointing out this error. We have made the necessary correction in the manuscript.\n\n18) Figure S1/S2: is quite complex and not easy to interpret, particularly due to the arrangement of different body parts across the subplots. To improve readability, I would suggest adding subtle vertical divider lines between the major body regions (e.g., feet, legs, torso, etc.), which could help the reader more easily distinguish the corresponding sections across subplots.\n\nWe thank the reviewer for the suggestion, we agree the readability could be improved. Following your recommendation, we have added subtle vertical dividers between the body regions in both figures. We hope this modification enhances clarity.\n\n19) Why did the authors choose to report the standard error of the mean in the figures instead of the standard deviation?\n\nWe chose to present the standard error of the mean (SEM) in our figures to indicate the precision of the sample mean in estimating the population mean. This has been our choice in all lab-published manuscripts, and for consistency we prefer to keep this practice. Additionally, we have included individual data points in the figures to directly represent the variability within the sample.\n\n20) Figure 1B: I appreciate the illustrative representation showing the co-use of different body parts (below images). However, I found the image selected for the Arm co-use condition a bit confusing. To me, it looks more like a depiction of Hand co-use, as the participant seems to be using the other hand to perform the task. Could you please clarify this?\n\nOur intention in Figure 1B was to illustrate the functional contribution of different body parts to everyday behaviours. All our tasks are typically performed bimanually, and adults with two hands in our study consistently used both upper limbs (as indicated by our co-use index). In contrast, individuals with CLD naturally rely on their intact hand for most tasks—what differs is how other body parts contribute to supporting or enabling the action.\n\nIn the Arm example, the participant is using their residual arm to carefully balance and support the object, while the intact hand performs fine-grained manipulation. That supportive role is central to our behavioural framing.\n\nTo avoid misinterpretation, we have revised the figure legend to clarify this intention:\n‘Images are provided to illustrate functional contributions by various body parts to aid the intact hand during task completion. Please note that multiple body-parts might be simultaneously used, e.g. torso, residual arm and hand.’\n\n21) Line 141 ..Supplementary Tables S2 and specify the specific tables\n\nWe thank the reviewer for pointing out this error. We have made the necessary correction in the manuscript.\n\n22) Figure 2B: To support the text describing the stimulation locations for the different groups (lines 867–878), I would suggest expanding Figure 2B and adding schematics for the CLD and control participants. Include the placement locations for both groups, label each location (e.g., “1”, “2”, “3”, etc.) clearly within the figure, and indicate “non-dominant” for control participants directly in the figure. These additions would make the figure more informative and help readers easily identify the stimulation sites across groups directly from the figure.\n\nWe thank the reviewer for their useful suggestions. We carefully considered how to improve Figure 2B, including adding schematics for control participants. However, this made the figure overly complex, so we retained the original configuration. We believe showing schematics only for the CLD group is sufficiently clear, as the locations are identical in the control group. To enhance clarity, as suggested, we added a numerical label to each location, corresponding to the enumeration in the Methods section and now referenced in the caption. Note that the enumeration of the body parts was updated to be consistent with the order used throughout the manuscript. We also made aesthetic adjustments to improve the figure's balance and clarity.\n\nWe report here the updated figure:\n\n23) Line 260: The text refers to Figure 3A as showing a functional ROI from an exemplar participant. However, the figure legend describes group-averaged Z values and does not clearly indicate whether the ROI brain image represents a single participant or a group-level or template example. For clarity, I suggest the authors explicitly state whether the brain plot is from an individual participant or a standard reference brain in the legend\n\nWe thank the reviewer for raising this point and apologies for not making the caption clearer. The Figure (Panel A, B, and C) generally describes group analyses, while the brain plot shows a participant's ROI resampled onto a standard template surface for visualisation purposes. We have made the necessary changes in the revised manuscript:\n‘The top-right panel displays a participant's ROI after it has been resampled onto a standard template brain for visualisation purposes.’\n\n24) Line 548: Discussion : It might be more reader-friendly to present the main results or main findings in the first paragraph, so that readers have a clear sense of the overall direction of the discussion\n\nWe have removed the introductory paragraph from our original discussion, to streamline the presentation of our key results.\n\n25) Line 548: Discussion: When citing the results that support each discussion point, including figure or table references would improve readability\n\nWe appreciate the reviewer’s suggestion to include figure or table references within the Discussion to enhance the overlap with the Results section. However, we have opted to minimise figure citations in this section in order to maintain the interpretative flow and clarity of the broader narrative, in line with standard conventions for the structure of scientific discussions.\n\nThroughout the manuscript, we have provided detailed citations to figures and tables in the Results section, where the empirical findings are systematically presented and contextualised. We believe that duplicating these references in the Discussion risks overburdening the text and potentially detracting from the broader conceptual interpretation.\n\nThat said, we have carefully reviewed the section and ensured that any less obvious references to key results are now clearly linked back to the appropriate earlier descriptions. We hope this strikes a balance between clarity and readability.\n\n26) Lines 718-719: Could the authors clarify whether a power analysis was conducted to determine the sample size? If not, please explain how the number of participants (16 CLD children and 16 CLD adults) was established and whether it was based on prior studies, feasibility, or other considerations\n\nWe apologies for not reporting this information. We conducted a power calculation while applying for funding to conduct the study, and our target sample size was informed by prior research. Specifically, we conducted a power analysis based on Hahamy & Makin (2019), which examined similar univariate effects of interest in adults with congenital hand loss compared to controls across three independent datasets. This analysis indicated that a sample size of approximately 9 participants per group was sufficient to detect the effects observed in that adult population. Given the potential for higher noise levels in fMRI data from children and that we used passive stimulation, our initial aim was to recruit a larger sample size of 15-20 participants per group (children and adults). Ultimately, we were able to recruit 16 participants per group (16 CLD children and 16 CLD adults). To achieve this sample, we had to maintain recruitment and data collection over the course of 3.5 years. This recruitment number also reflects the significant challenges associated with studying children with congenital limb differences in a specific age bracket. This is a relatively rare population, and our recruitment efforts were inherently limited by the number of eligible individuals within the desired age range.\n\n27) Line 724: was the CLD participant excluded a child or adult participant?\n\nThe excluded CLD participant was a child. We have made the necessary change in the manuscript: “CLD participant” was changed to “CLD child”.\n\n28) Line 865: The reference to Figure 1B appears to be incorrect; it should be Figure 2B.\n\nWe thank the reviewer for pointing out this error. We have made the necessary correction in the manuscript: “Figure 1B” was changed to “Figure 2B”.\n\n29) Line 992: While MNI space is commonly understood within the neuroimaging community, I would recommend spelling out the acronym at its first mention, to ensure clarity for readers who may not be experts in the field. Same for FEAT.\n\nWe agree with the reviewer that these acronyms (MNI and FEAT) should be spelt out to ensure clarity. We have done that at the first occurrence of these two terms.\n\n30) Line 1002: Figure 2B instead of general Figure 2\n\nWe made the necessary changes in the manuscript.\n\n31) Line 1026 and 1043: please specify which panel of Figure 2 you are referring\n\nWe made the necessary changes in the manuscript.\n\n32) Line 1095 you repeated two times lower face\n\n“lower face” was changed to “upper face”.\n\n33) Lines 1102–1106: the authors report summary values such as “(2/469; 7 × all participants)” and later “(20/467; 7 × all participants minus 2)” in reference to cases where peak activity was not identifiable and instances of double-peaked activation patterns, While the general meaning can be followed, I recommend clarifying these expressions more explicitly to help readers understand how these numbers were derived.\n\nIn particular, if I understand correctly, the number of participants included in the fMRI analyses was 15 CLD children, 16 CLD adults, 20 control children, and 15 control adults (as stated in lines 737–740), which would yield a total of 66 participants. Therefore, 7 × all participants should result in 462, not 469. Could the authors please confirm whether this discrepancy is due to a typo, inclusion of additional data, or another factor?\n\nThe reviewer is correct, it was a typo. Thanks for noticing it. We realised this section was a bit confusing as it was written, so we changed it to make it clearer.\n\n‘Note that for two participants, a clear peak activity (z-statistic exceeding zero, indicating a statistically significant positive activation) was not identifiable for the torso, and therefore these two contrasts (out of 462 total contrasts, i.e., 7 x 66 participants) were excluded from further analysis. Additionally, double-peaked activity patterns were observed in a subset of participants (n=20) and contrasts. In these cases, the peak closer to the group average for that specific contrast, which typically aligned with the expected location based on the classical homunculus model, was chosen manually.’\n\nReviewer #3 (Remarks to the Author):\nSummary: In this article, the authors introduce several key aspects that fill significant gaps in our current understanding of the differences between control subjects and CLD subjects' somatotopic maps. The authors specifically address differences between children and adults with CLD. Interestingly, they find similarities between the children and the adult CLD subjects that indicate somatotopic map changes occur before age 5 and that these changes are maintained into adulthood. These results are not particularly surprising when viewed in the context of animal research on changes in somatotopy. For these changes in the homunculus, they formulated a biologically inspired model that is very simple in construction. Even with its simplicity, they show that it can reproduce many of the differences seen between the CLD subjects and the controls simply with the inclusion of homeostatic plasticity and several justifiable assumptions, such as differences in the distribution of sensory input to the different body regions of CLD vs. CTR subjects. The use of more ecologically valid Semi-Naturalistic movements is a nice addition. However, some of these movements, or sub-tasks, are likely new to the younger subjects compared to the adults, for whom unscrewing something is much more familiar. Overall, the paper is significant and insightful, bringing new insights forward in a clear, easy-to-understand article. Some issues are raised below; most are meant to increase readability.\n\nWe thank the reviewer for their support.\n\nExperimental Section:\n34) 1. The co-use index used has some assumptions built in. One is that the children’s apparent variability of body part use is due to some general higher variability in motor planning or a higher expletory rate. Is it possible that the children get fatigued or feel pain when making some of these new movements while interacting with the objects? In essence, how would your measure differentiate subjects with two solutions for completing a sub-task that they switch between rather than something more akin to a higher exploration rate in the motor output space?\n\nWe thank the reviewer for raising these nuanced considerations. While we agree that movement variability can stem from multiple sources, including planning, strategy switching, or exploration, we would like to clarify that the tasks assessed in this study were all familiar daily-life behaviours (e.g., buttons, zippers), rather than novel or physically demanding ones. As such, fatigue or pain are unlikely to be meaningful contributors, especially within the short, semi-ecological testing context we used. Most tasks were completed within 90 seconds; L.B. & M.S., who administered the tasks, report no instances of a participant reporting any pain. Qualitatively, children who switched strategies tended to do so based on their current strategy being ineffectual.\n\nOur primary aim was not to interpret co-use variability in terms of action planning mechanisms per se, but rather to assess the range of body-part involvement — a pragmatic proxy for behavioural flexibility and support strategies in action control. Regarding the distinction between motor exploration and the use of multiple stable strategies: we acknowledge that this remains a conceptual challenge in naturalistic tasks. This has been addressed in our prior work using a virtual paradigm (Allen et al., 2024 Psychonomic Bulletin & Review), where we showed that children exhibit more versatile exploration of physical solutions, whereas congenital limb difference –– whether in childhood or adulthood –– influences their cognitive style for solution exploration. Here, we note that such switching — whether between fixed strategies or due to exploration — still reflects meaningful variation in support behaviour, which is precisely what our co-use index aims to quantify. Our design does not assume that variability must reflect randomness or inefficiency, and we agree that a much larger sample size of children with an upper limb difference would be needed to tease apart these nuances across naturalistic task demands.\n\nWe have added a brief clarification to the Results to reflect this interpretation:\n‘While our analysis doesn’t allow us to ascertain whether this also reflects greater exploration or task switching, our finding indicates that adaptive behaviour at the tested age range (5-7 years) still has not matured.’\n\n35) 2. In Figure 1, the picture for Torso seems to be of arm use. The picture for the arm shows the use of the elbow joint to hold an item. Are these terms used regularly in this research area for these body parts?\n\nWe apologise for the confusion. We acknowledge that the images selected for the torso and arm in Figure 1B could be interpreted as overlapping in terms of visible body part involvement. Our intention was not to indicate exclusive use of a single body part, but rather to highlight the primary supportive role played by a given body region within each task context.\n\nSpecifically, in the \"Torso\" example, although the arm is present in the image, the torso is actively used to stabilise the object against the body, which is a common compensatory strategy. Similarly, the \"Arm\" example focuses on the functional contribution of the residual arm to support the object, while the intact hand carries out the manipulation. This is consistent with how such body parts are typically used for compensatory behaviour in populations with limb differences.\n\nWe have also revised the figure legend to clarify that these images are intended to illustrate functional contribution rather than anatomical isolation:\n‘Images are provided to illustrate functional contributions by various body parts to aid the intact hand during task completion. Please note that multiple body-part might be simultaneously used, e.g. torso, residual arm and hand.’\n\nWe also further clarify this in the revised Results section:\n‘For example, while CTR participants tend to use both hands to open a tin box, CLD individuals might stabilise the tin with their legs, torso, residual arm (or any combination of these body parts), while lifting the lid with their intact hand.’\n\nFinally, we have also updated the representative images for “Arm” and “Torso” in Figure 1B with examples that hopefully highlight better the supportive roles of these two body parts.\n\n36) 3. Figure 1, Please use the term co-use for subplot A’s y-axis, or change the name of the measure you use to co-use complexity.\n\nWe agree with the reviewer, the plot axis should be named “co-use”. We have made this change in the figure.\n\n37) 4. It might be helpful to use different colors for the children vs. the adult CLDs, but it could just be this review’s eyes that had difficulties seeing the differences in the line plots.\n\nWe apologise for this, our colour choice was designed to highlight the focus on the CLDs groups, relative to controls, and to provide an intuitive distinction between older participants (darker colour) and younger participants. Given we are using an array of colours in Figures 4 and 6 to indicate the different body parts, our colour options were relatively restricted.\n\n38) 5. Figure 2.c, It might be helpful to put a simple landmark in the three sub-plots on the right-hand side to allow for easy comparisons between Foot, Leg, and Torso, as these are all within the one box on the main associated image.\n\nWe thank the reviewer for this comment and to facilitate comparison, we introduced a common landmark (an arrow) and also made the leg contours visible in the three sub-plots.\n\n39) 6. Figure 5, please indicate what the x-axis is for the reader.\n\nWe apologise if the description of the x-axis was unclear. The x-axis values represent medial-to-lateral coordinates, a convention applied to all similar figures and previously stated in the caption of Figure 2. To enhance clarity and ensure consistency, we have now added the label \"Medial to Lateral\" directly to Figure 5 and included the sentence following in the captions of both Figures 4 and 5:\n‘The x-axis values indicate MNI coordinates (medial to lateral).’\n\n40) 7. As divisive normalization is a canonical cortical computation known to occur in sensory areas, it would be nice if the authors had any insights on how such normalization mechanisms might show up in the context of CLD and how such neuro-dynamic mechanisms might muddy the waters of the clean model and interpretation the authors would like to champion.\n\nWe thank the reviewer for this comment, as it prompted us to investigate the potentially overlooked connection between divisive normalisation and our synaptic scaling model.\n\nIn short, we see divisive normalization as a potential network-level mechanism that contributes to the homeostatic synaptic scaling property (Mayzel & Schneidman, 2024). Divisive normalisation is a well-established computational model describing how a neuron's response to a stimulus is modulated, or normalised, by the combined activity of a population of other neurons (Aqil et al., 2021; Carandini & Heeger, 2012; Heeger, 1992). In essence, a neuron's activity is scaled down by the summed activity of a related neuronal population (Carandini & Heeger, 2012). This operation is proposed as a fundamental mechanism in various sensory regions, including visual (Burg et al., 2021; Carandini & Heeger, 2012) and somatosensory (Arbuckle et al., 2022; Brouwer et al., 2015) areas. In other words, a neuron's response is not absolute but is relative to the activity of its interconnected neighbours.\n\nSynaptic scaling is a form of homeostatic plasticity that ensures stable activity levels within neural circuits, even when faced with disruptions such as the sensory input loss experienced by individuals with congenital limb differences. To counteract this reduction in neuronal firing rates, synaptic weights are uniformly increased by a multiplicative factor (Keck et al., 2017).\n\nThe concept of divisive normalization, initially suggested for neuronal responses (Heeger, 1992), could in principle be extended to synaptic scaling to implement a form of divisive normalization applied to synaptic weights within a specific neural circuit (Mayzel & Schneidman, 2024). Therefore, in scenarios where a neuron might become underactive, such as due to a loss of its original input, homeostatic synaptic normalization (Mayzel & Schneidman, 2024) can contribute to restoring or maintaining neuronal activity by helping to regulate the neuron's firing rate through the adjustment of overall incoming synaptic strengths. However, this interpretation should be cautioned, since our model operates on a different temporal scale than DN. It's quite common for models focusing on slower, long-term processes to not explicitly include faster, transient dynamics. For example, when modelling climate change over decades, we don't necessarily model individual weather events like daily temperature fluctuations — even though those short-term events contribute to the overall climate. Similarly, it's possible that DN contributes to synaptic scaling, alongside other mechanisms operating on faster timescales.\n\nIn the revised manuscript, we highlighted the potential role of divisive normalisation in our Discussion:\n‘The primary function of homeostatic plasticity is to provide stabilising mechanisms that regulate changes in the system, preventing activity levels from becoming excessively high or low (Keck et al., 2017). This can be achieved by network-level computational mechanisms such as divisive normalization (Mayzel & Schneidman, 2024), where the neuron’s response is modulated by the combined activity of a relevant population (Aqil et al., 2021; Carandini & Heeger, 2012). Electrophysiological research suggests that local remapping is primarily driven by the unmasking of silent inputs through disinhibition of homeostatic plasticity mechanisms (Gainey & Feldman, 2017). Through our computational model, in the current study we were able to provide a proof of concept that a simple plasticity rule predicts a much more global remapping pattern, causing topographic shifts across the entire homunculus.’\n\n41) 8. Would it be helpful for the reader if you drop the term Hand loss, as it implies there was a hand and that it was lost, which would be a rather different situation than the hand never having been formed or not formed completely, as the authors are fully aware.\n\nWe thank the reviewer for this sensitive and important observation. We fully agree that the term “hand loss” can be misleading in the context of congenital limb difference, where a hand was not lost but rather not formed during development.\n\nWe have now revised the main text to remove this phrasing and replaced it with:\n‘Congenital malformation of the upper-limb’ [Abstract] and ‘born with a missing hand due to an upper limb developmental malformation’ [Discussion].\n\nModeling:\n42) 1. The proposed model is elegant in its simplicity. This model could be formulated as a modified Radial Basis Network, which has previously been used in sensorimotor studies. One would simply need to use as input the sparse set of body positions rather than a continuous input space, include a post-activation gain, which is known to exist in biological systems, which would act as the synaptic scaling mechanism, and add the offset included in your work. This may or may not help make your model clearer for comparison with other RBF network publications on sensorimotor map changes. In addition, the simple gain formulation would allow relation to gain-field work in the sensorimotor literature. At least the authors could give the parameters of the function used to determine the initial weight distributions in the model they propose. This would allow others to replicate the results.\n\nWe thank the reviewer for this pointing this out. We agree that framing our model as an extended Radial Basis Function (RBF) may help in connecting our work more directly to the broader literature on sensorimotor map changes and gain-field mechanisms. However, due to space constraints and the focus of the current manuscript, we prefer to keep only our current, and arguably simplest, model of homeostatic plasticity. Nevertheless, we find this idea inspiring and will consider exploring it in future work.\n\nAs suggested, we have added a new subsection in the Supplementary Materials (“Setting of Initial Connection Weights”) where we clearly describe all parameters used to determine the initial weight distributions. We hope this will facilitate replication and further development of our approach.\n\nWe included a reference to the new subsection in the Methods section of the main manuscript.\n‘The initial weights are represented by bell-shaped curves, with their peak positions uniformly distributed across the S1 homunculus strip and in line with the typical topography of this region (please see the supplementary section “Setting of Initial Connection Weights” for a detailed description of how these weights are calculated).’\n\n43) 2. Many studies have examined different types of neural plasticity and changes in the somatosensory cortex due to stroke, amputation, nerve block, etc. It is unclear from the literature that has been cited if the authors are fully aware of this literature. Perhaps adding some of this background and results from rodents and primates would help round out the story being presented. That is, make clear the models that are available to ask the types of questions addressed in this work that would include more than just homeostatic mechanisms and are more biologically realistic. (optional)\n\nWe thank the reviewer for suggesting the inclusion of additional literature on plasticity from stroke, amputation, nerve block, and animal models. We fully agree that this is a rich and important body of work, and we were keen to integrate it more fully into our revised manuscript. However, given the breadth of this literature and the word count limitations, we sought clarification via the editor to ensure we could appropriately prioritise any references (or specific models) the reviewer had in mind. As no specific works were suggested within the revision window, we have opted not to modify this section at this stage. We hope the current framing, based on recent reviews (including our own), and the additional references concerning plasticity mechanisms, adequately captures the broader context. We would be happy to revisit this in future iterations if further input becomes available.\n\n44) 3. It seems that the results obtained would be expected given the initial weight distributions, touch probabilities, and coupling of the arm/palm/fingers. It is great to show that this sensible pattern of inputs and a simple Homeostatic Plasticity rule (normalization) give results like those seen in the subjects, but it does seem obvious this model abstraction would give these results.\n\nWhile we agree that the model is intuitive, it is challenging to immediately predict the full scope of local deprivation, e.g. the centre of gravity shifts and increased activity for body parts well beyond the hand area. Moreover, the full pattern of our results across the entire S1 have not been predicted by previous research. As such, we find the model instructive, especially for readers that do not have an intimate understanding of such models.\n\n45) 4. The model doesn’t include Hebbian plasticity, which leaves the modeling short of describing or addressing the individual differences in body part use compared to the mean for the given groups and how this is correlated with differences in somatotopic responses shown in the human subjects’ data.\n\nWe thank the reviewer for this important observation. We fully agree that the absence of Hebbian plasticity in our model is a limitation, and as such, the model cannot account for all aspects of the individual variability observed in the empirical data. For example, behavioural differences within the empirically observed range do not generate the critical activity differences between groups; rather, they tend to shrink them. Only very large departures from baseline probabilities disrupt the model’s ability to reproduce the data. This is now acknowledged in the Discussion as a limitation.\n\nNevertheless, we believe that our model as is provides a significant contribution toward understanding the fundamental mechanisms underlying the plasticity following sensory deprivation, and we see it as a foundation for future extensions that could incorporate other plasticity mechanisms such as Hebbian learning.\n\n‘Our rudimentary computational model did not include a Hebbian plasticity mechanism, and as such it was not equipped to capture the likely more nuanced relationship between use- and deprivation-related plasticity. Together, our study highlights the crucial role of homeostatic plasticity in shaping the developing homunculus, through both deprivation and compensatory behaviours.’\n\n46) 5. As the model seems to be a large part of the paper, it might make sense to put more of the model results in the main section?\n\nWe agree that the modelling component plays a central role in the paper, and we appreciate the suggestion to foreground it more prominently. In the revised manuscript, we have provided extensive additional details of the model results in the Supplementary Materials, including a description of the initial setting of the model’s connection weights (as suggested by the reviewer in comment 42), a parameter sensitivity analysis, the potential role of behaviour, how robust the model is against variations in parameter choices.\n\nHowever, due to the word limit for the main text, we are unable to include more extensive model results without displacing other key empirical findings or theoretical framing. We have therefore aimed to strike a balance by highlighting the primary results of the model in the main section—specifically those most critical to our theoretical claims—while guiding readers to the Supplementary Materials for a complete account.\n\nIf the editor feels that including more model results in the main text would improve the clarity or impact of the paper, we would of course be happy to explore how best to accommodate this within the format constraints.\n\n47) 6. It seems you are using weight matrix indexing that is the opposite of the norm where it is usually W i,j where i is the neuron index that the input j is going to that is W to,from, where you are using W from,to. This may confuse some readers.\n\nWe thank the reviewer for pointing out this potential source of confusion. We have corrected the indexing of the weight matrices to follow the standard convention, as suggested.\n\n48) 7. In the methods section it may be helpful to make the indexing more explicit as well, such as the summation from j = 1:10 as there are 10 body parts.\n\nWe thank the reviewer for this helpful suggestion. We have updated the Materials and Methods section to make the indexing more explicit, including specifying the summation limits.\n\n49) 8. Line 1205, where did the 15% come from and the % for the other body regions?\n\nApologies if this was not clearly stated. We have added a new subsection to clarify this point in detail. As there is no available empirical data to estimate stimulation frequencies for individual body parts across daily activities, we adopted a simplified modelling approach to ensure transparency and interpretability of the model.\n\nIn this approach, all body parts were assigned the same baseline stimulation probability, except for the hand, which was given a higher probability based on the assumption that hands are more frequently engaged in daily tasks.\n\nThe value of 15% for the hand (compared to 5% for other body parts) approximates the empirical data well. However, we tested a range of hand stimulation probabilities (0.05, 0.15, and 0.25), and observed that in this range, the model’s behaviour is highly robust with respect to the key Observations.\n\nThis is now described in the newly added subsection Justification of Stimulation Probability Settings in Supplementary Materials, with corresponding visualizations in Figure S15.\n\n‘Here, we systematically analyse how varying the relative stimulation frequency of the hand (i.e., palm and fingers or the corresponding nerves) influences the model’s ability to satisfy key Observations.\nSince no comprehensive empirical data exist on estimated stimulation frequencies of individual body parts across all daily activities, we adopted a parsimonious approach: all body parts share the same probability of stimulation, except for the hand, which was assigned a higher probability, due to its role in tool and object manipulation.\nWhen the ratio of hand to other body parts' stimulation probabilities is fixed, the specific probability values themselves are less critical. This is because the multiplication of probabilities merely scales the activations after homeostasis; therefore, it does not influence how the model satisfies the key Observations. Such scaling can be counterbalanced by adjusting the homeostatic Mean Neural Activity. For example, doubling all stimulation probabilities yields equivalent activations if the homeostatic Mean Neural Activity of all neurons is also doubled.\nTo explore the effect of hand stimulation probability, we performed an analysis where the hand was stimulated 1, 3, and 5 times more frequently than other body parts. Specifically, the tested hand probabilities were 0.05 (equal to other body parts, Figure S15A), 0.15 (three times higher, used in the main model, Figure S15B), and 0.25 (five times higher, Figure S15C). The results indicate that lower hand stimulation probabilities lead to an increase in the arm activation peak, whereas higher hand stimulation probabilities result in a decrease of the arm activation peak. For a hand probability of 0.05, only Observation 2 was violated for the foot, while all other Observations remained preserved.\nOverall, these findings indicate that the model is highly robust to reasonable changes in hand-stimulation probability. A value of 0.15 was selected for the main model because, based on visual comparison, the relative size of the CLD Arm peak (compared to other peaks) most closely resembled the corresponding relative peak sizes observed in empirical data.’\n\n50) 9. Figure 6, it would be nice to see direct comparisons between the model and the mean of the subjects’ data. Along these lines have you tried optimizing the standard deviation of your gaussian weights? The real data seems to show more localized receptive fields as compared to the model, which would seem to be easily rectified?\n\nThank you for this valuable suggestion. We now explored the effect of varying the standard deviation (σ) of the Gaussian weights to produce more localized receptive fields. The figure below illustrates this comparison across different σ values.\n\nIn our main model, we initially used σ = 5 (see panel A). We then tested a narrower configuration with σ = 4 (panel B), applied only to Gaussians corresponding to body parts excluding the hand and fingers (or their associated nerves). This resulted in more localized receptive fields, as expected from the narrower basis functions. Finally, in panel C, we further reduced the width for those same body parts to σ = 3.\n\nEven with σ = 3, the core qualitative properties (Observations 1–4) remained intact. However, we observed a shift in the peak positions for the foot and lower face toward the centre of the strip, as well as a reduced distance between the CLD and CTR peak centres for the arm (compare subfigures A and C). These changes appeared to reduce the visual similarity to the empirical data in these specific aspects.\n\nWe therefore newly selected σ = 4 as a compromise. This setting produced more localized receptive fields while limiting the more pronounced deviations observed with σ = 3, which were less consistent with the empirical data in the specific aspects mentioned above. The following results were regenerated: Figure 6D, Supplementary Materials: Role of behaviour for homunculus adaptation, Figure S9, S10, S11, S12, S13, Table S4, Table S5.\n\nPlease note that this modification resulted in minor changes to the article: In the section Role of behaviour for homunculus adaptation, we adjusted the leg stimulation increase to elicit comparable behaviour as with the previous setting of sigma. Moreover, we updated the no longer valid sentence “Additionally, for CLD participants, there were slight shifts in the activation peaks for torso and upper face toward the centre of the homunculus strip, consistent with Observation 2” to “Additionally, for CLD participants, there were slight shifts in the activation peaks for arm (lateral direction) and lower face (medial direction), consistent with Observations 1 and 2, respectively.” in the same subsection.\n\nWe chose not to implement full numerical optimization of the parameters or extend the model, as our goal was to preserve its conceptual clarity and simplicity. Achieving a more accurate quantitative fit would require modifications—such as supporting negative activity values (present in the empirical data) and likely adopting a more complex configuration of Gaussians—which would introduce additional assumptions and deviate from our guiding principle of parsimony. The model is thus intended primarily for qualitative comparison with empirical data, rather than precise data fitting.\n\n51) 10. Figure S9, please indicate the parameters of the assumed gaussian used for the normal receptive fields and the modified ones for the nerves.\n\nWe thank the reviewer for this helpful comment. In response, we have added a new subsection (\"Setting of Initial Connection Weights\" in the Supplementary Materials) where we clearly describe all parameters used to determine the initial weight distributions, including those for the initial weights shown in Figure S9.\n\n52) 11. Figure S12, same as above for S9.\n\nWe kindly refer the reviewer to our response to the previous point.\n\n53) The code: \"Activations should be replaced by weight in the figure.\" perhaps take out such notes.\n\nThe code has now been modified and extra care has been taken to avoid unnecessary notes, we are grateful to the reviewer for pointing out this observation.\n\n54) There are grammatical issues in the text that you might want to fix = run spell check etc.\n\nWe deeply apologise for this. We have conducted a thorough review using the spell check tool in Microsoft Word, as suggested, and have corrected the identified errors. We have also carefully proofread the text to address any remaining grammatical inconsistencies.\n\nReviewer #3 (Remarks on code availability):\n55) The code does not state who the author of it is. There are plenty of grammatical errors etc. The code works as it should and seems to produce results as it should.\n\nWe thank the reviewer for this observation. We have carefully gone through the code, corrected grammatical and stylistic issues, and added author information as appropriate."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have thoroughly addressed my comments on the original manuscript. I was glad to be part of the review process around this very interesting work.", "reviewer_2": "I appreciate the authors’ detailed responses and the careful revision of the manuscript.\n\nAll my comments have been addressed adequately, and I find the revised version much improved.\n\nI have no additional suggestions.", "reviewer_3": "My concerns have been addressed and I appreciate the time and effort put into the authors' responses.\n\nThe paper is acceptable for publication."}, "author_response": ""}]

Decision letter