Dissociated neural substrates of motor execution and planning in learning multiple sensorimotor mappings

Dissociated neural substrates of motor execution and planning in learning multiple sensorimotor mappings | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Dissociated neural substrates of motor execution and planning in learning multiple sensorimotor mappings Antoine Caraballo, Sungshin Kim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8838307/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Previous behavioral studies have shown that human can adapt to multiple sensorimotor mappings simultaneously. Motor planning, rather than motor execution, has been shown to play a crucial role in distinguishing between multiple interfering motor tasks and in forming motor memory. However, it remains unclear how the human brain represents distinct motor planning and execution for multiple sensorimotor mappings. To address this, we designed an fMRI experiment specifically to dissociate motor planning from motor execution during multiple sensorimotor mappings learning. Critically, the design allowed us to compare conditions where participants prepared for different visuomotor rotations but executed identical movements, isolating the neural representation of motor planning. We found that the posterior parietal cortex (PPC), including the superior and inferior parietal lobules, exhibited increased activity during the planning. However, using Multivoxel pattern analysis (MVPA) to look at the representation, we found a dissociation between motor execution and planning. While motor execution could be reliably decoded in the sensorimotor cortex, PPC, and cerebellum, planning-related activity for opposite rotations was not decodable in any region of interest. These results suggest that while the PPC is actively recruited for motor planning, the specific neural patterns differentiating conflicting plans may be less distinct than those for execution. Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Human can simultaneously perform multiple motor tasks by learning distinct relationships between actions and their outcomes while building separate internal models [ 1 ], [ 2 ], [ 3 ] . Many real-world motor tasks are conflicting in that they often require identical actions for distinct outcomes or distinct actions for identical outcomes [ 4 ], [ 5 ] . For example, you may press the light button (identical action) to either turn on or off the light (distinct outcomes). In the other case, you may slide or pull the door (distinct actions) to open it (identical outcome). Previous studies have demonstrated that conflicting sensorimotor mappings can be acquired distinctly with contextual cues despite initial interference through extensive training [ 6 ], [ 7 ], [ 8 ], [ 9 ] . Although conflicting sensorimotor mappings (e.g., opposite visuomotor rotations) with similar task structures activate overlapping brain regions, we can successfully discriminate them using distributed patterns of fMRI activity in the sensorimotor system, including the cerebellum [ 10 ], [ 11 ] . However, in our previous fMRI experiment, the conflicting mappings were associated with distinct plans and corresponding execution, without dissociating them [ 11 ] . Indeed, motor planning and execution appear to play a distinct role in motor adaptation, and few behavioral studies have demonstrated a more significant role of planning over execution in the formation and retrieval of distinct motor memories [ 4 ], [ 12 ], [ 13 ] . We, therefore, aimed to understand further how motor planning and execution are distinctively represented in the brain during the learning of multiple sensorimotor mappings. We deliberately designed an fMRI experiment that allowed us to dissociate motor planning and execution. Specifically, participants concurrently adapted to three sensorimotor mappings, one veridical and two mappings with conflicting rotations between actual reaching movements and their visual feedback. In the subsequent three experimental fMRI runs, participants performed different types of trials combining target positions and imposed rotations. We sought to dissociate each of the three components of visuomotor adaptation shared between every pair of two experimental runs: (1) planning to counteract the rotation, (2) execution of movement, and (3) visual direction toward a target. We first used general linear model analysis (GLM) and subsequent conjunction analysis to dissociate the three components, determining brain regions or voxels that were commonly active across different trial types (as an analogy, you could think of it as the logical “AND” between two or more different conditions). Furthermore, we also applied MVPA with a previously suggested cross-classification, training on one set of trials and testing on a different set of trials to see if the neural patterns could generalize to a new context, which is a counterpart of conjunction analysis in GLM. The conjunction analysis and cross-classification ensured the dissociation of the invariant representation of planning, execution, and visual directions [ 10 ] . Results Successful adaptation of multiple visuomotor mappings Thirty-three right-handed participants completed the experiment. For each trial, participants were instructed to reach a circular target that appeared on the left side of the screen using an MRI-compatible tablet and a pen. Figure 1 summarizes the design of the experiment and Table 1 summarizes the different tasks. In fMRI runs 1–4 (prior to the washout), three different visuomotor rotations of the cursor movement (0º; 30º, clockwise (CW); -30º, counterclockwise (CCW)) were pseudorandomly applied with contextual color cues for the three different tasks: baseline task (T 1 , red), task 2 (T 2 , green), and task 3 (T 3 , blue) (Fig. 1 A). To examine the case in which the rotation mappings were turned off, a brief washout phase was introduced at the end of run 4. Thus, in the washout and run 5, the three tasks were only differentiated by the target position, and the ideal movement degree would be identical to the target position (Fig. 1 B). Table 1 Condition matrix Condition Run(s) Target Color Target Position Visuomotor Rotation Required Hand Movement T 1 1–5 Red Middle 0° Horizontal T 2 1–3 Green Middle -30° Upward T 3 1–3 Blue Middle + 30° Downward T 4 4 Green Low + 30° Horizontal T 5 4 Blue High -30° Horizontal T 6 5 Green High 0° Upward T 7 5 Blue Low 0° Downward Participants fully adapted to the mappings throughout fMRI runs 1 and 2, in which the tasks highly interfered with each other due to their presentation in a pseudorandom order. Specifically, in the last block of run 2, the movement directions were not significantly different from their “goal” directions, i.e., counteracting movements to the imposed rotations to reach the target (T 2 : 29.1 ± 0.9° (mean ± SE), T 3 : -29.1 ± 1.1°, T 1 : 0.6 ± 0.7°; p > 0.32, for all three tasks; Fig. 2 ). In the subsequent run 3, in which blocks of the tasks were presented, we maintained the learned associations between the contextual cues for the tasks and the movement directions (T 2 : 29.5 ± 0.7°, T 3 : -29.0 ± 0.9°, T 1 : 0.8 ± 0.5°; p > 0.15 for all three tasks). Throughout runs 1 to 3, participants successfully discriminated the task by gradually dividing the movements into three goal directions (Fig. 2 ). In run 4, in which the goal directions were set to 0º in order to isolate the planning component, participants maintained movement degrees to 0º, successfully reaching the targets regardless of their rotation mappings (T 4 : -0.8 ± 0.5°, T 5 : 0.8 ± 0.6°, T 1 : 1.0 ± 0.5°; p > 0.08 for all three tasks). Meanwhile, in the washout phase, where the rotation mappings were removed, the actual movement degrees gradually diverged into either 30º or -30º (T 6 : 31.1 ± 0.5°, T 7 : -32.7 ± 0.4°). Participants also exhibited the expected patterns of movement degrees in run 5, where the rotation mappings were absent (T 6 : 33.4 ± 0.6°, T 7 : -30 ± 0.5°, T 1 : 0.9 ± 0.4°). Notably, they displayed a slight systematic bias toward the upper-field target in this run. This bias may stem from the slightly awkward arm posture required to reach that position compared to the reaching movement needed for the target located in the lower part of the screen. However, the offset is negligible. An aiming angle of approximately 33.4° still places the cursor on the target, so overall task performance remains unaffected. Given participants' successful performance, we assumed that runs 3, 4 and 5, would reflect the acquired knowledge of different tasks, particularly the various rotational mappings. Conjunction Analysis We conducted a conjunction analysis using a voxel-wise general linear model (GLM) on data from runs 3, 4, and 5 to dissociate the three visuomotor components of visuomotor adaptation: execution, planning, and visual target processing. As participants successfully adapted to the three visuomotor mappings, we hypothesized that the associated visuomotor components would be reflected in the BOLD response. In the whole-brain univariate analysis, we examined two contrasts designed to assess activation differences between conditions: (1) the adaptation-versus-baseline contrast (CW and CCW rotations vs. 0° baseline) and (2) the directional contrast (CW vs. CCW). For run 5, occurring after the washout phase, the conditions are associated with the contextual color cues (Fig. 1 A). For the adaptation vs. baseline contrast, we identified significant regions related to the three visuomotor components (Fig. 4 C). For the planning component (conjunction between runs 3 and 4), we identified clusters in the bilateral superior parietal lobe (SPL) and inferior parietal lobe (IPL), as well as in the right superior frontal (SFG) and middle frontal gyrus (MFG) (For details about the size of the clusters, please refer to Table 2 ). For the execution component (conjunction between runs 3 and 5) and the visual target component (conjunction between runs 4 and 5), we found clusters in the bilateral SPL. These results indicate that the BOLD responses were greater during adaptation trials than baseline trials within the posterior parietal cortex (PPC) across all three visuomotor components and within the frontal cortex, specifically for the planning component. Table 2 Conjunction analysis clusters Peak (MNI) Cluster size (voxels) X Y Z Directional Execution None Planning None Target position R Cuneus 5 -101 7 611 R Lingual Gyrus 9 -81 -9 116 Adaptation vs. baseline Execution R Superior Parietal Lobule 21 -71 47 126 L Superior Parietal Lobule -25 -71 51 120 L Inferior Parietal Lobule -37 -55 51 20 Planning L Inferior Parietal Lobule L Superior Parietal Lobule -39 -43 35 852 R Superior Parietal Lobule 27 -73 37 557 R Inferior Parietal Lobule 35 -49 37 328 R Superior Frontal Gyrus 23 -1 45 93 R Inferior Frontal Gyrus 49 9 21 58 Target position R Superior Parietal Lobule 17 -71 45 205 L Superior Parietal Lobule -25 -71 51 164 Shared R Superior Parietal Lobule 21 -71 47 126 L Superior Parietal Lobule -25 -71 51 59 Abbreviations: L, left; MNI, Montreal Neurological Institute; R, right. For the directional contrast, our conjunction analysis identified two significant clusters: the upper and lower regions of the calcarine sulcus in the right visual cortex, which process information about visual targets in the left visual field (Table 2 , Fig. 4 B). In our experiment, targets in runs 4 and 5 appear in opposite positions (upper and lower left). This finding is consistent with results from previous studies [ 14 ], [ 15 ], [ 16 ] , which have demonstrated that different parts of the visual cortex are involved in processing specific portions of the visual field. However, we found no significant regions for the execution and planning components. No difference in overall BOLD response between CW and CCW rotations is consistent with our previous study [ 11 ] and motivated us to employ multivariate pattern analysis (MVPA) to identify visuomotor components reflected in spatial activity patterns. Multivariate pattern analysis (MVPA) with cross-classification We first consider the classification of two conflicting visuomotor rotations, CW and CCW, to investigate how they are distinctively represented as BOLD activity patterns in the sensorimotor and cerebellar ROIs (Fig. 3 ). Classification accuracy varied significantly across visuomotor components (Fig. 5 ). Although no significant difference in BOLD response between the two rotations was found for the execution component in the whole-brain univariate conjunction analysis, the MVPA revealed significant above-chance classification accuracy in the contralateral (i.e., left) M1/S1 (M1: 53.1%, p(unc) < 0.001, p(FWER) < 0.003, S1: 54.2%, p(unc) < 0.001, p(FWER) < 0.001). Multiple sources of evidence [ 17 ], [ 18 ], [ 19 ] indicate that neurons in the M1 exhibit directional selectivity for movement direction, even in movements differing by up to 60° (CW 30° and CCW 30°). We also found that the mean decoding accuracy was significantly above the chance level in the left inferior parietal cortex (L IPL: 52.6%, p(unc) = 0.001, p(FWER) = 0.010), visual area (VIS: 53.0%, p(unc) < 0.001, p(FWER) < 0.003) and right cerebellar lobule VI (CB6: 52.6%, p(unc) < 0.002, p(FWER) = 0.010). These results indicate directional selectivity for the execution component in the sensorimotor regions, cerebellum, and visual cortex. BOLD activity patterns for the execution of the reaching movement were not accurately decoded in the superior parietal cortex (L SPL: 51.7%, p(unc) < 0.028, p(FWER) = 0.133; R SPL: 51.4%, p(unc) = 0.053; R IPL: 50.8%, p(unc) = 0.180), the right cerebellar lobule VIII (CB8: 48.2%, p(unc) = 0.980) or the supplementary motor area (SMA: 49.7%, p(unc) = 0.612), suggesting a lack of selectivity for the execution of movement in these brain regions (Fig. 5 ). For the visual target position component, the visual area exhibited the highest accuracy (VIS: 58.5%, p(unc) < 0.001, p(FWER) < 0.001), demonstrating its selectivity for the target position. Other regions did not exhibit significant decoding accuracy for this component (M1: 50.3%, p(unc) = 0.356; S1:50%, p(unc) = 0.483; SMA: 50.2%, p(unc) = 0.392; L SPL: 50.5%, p(unc) = 0.307; R SPL: 50.8%, p(unc) = 0.168; L IPL: 50.7%, p(unc) = 0.212; IPL R: 50.1%, p(unc) = 0.435; CB6: 51.4%, p(unc) = 0.055; CB8: 50.2%, p(unc) = 0.393; Fig. 5 ). Contrary to previous studies [ 10 ], [ 11 ] that identified cerebellar involvement in discriminating opposing visuomotor rotations, we did not find significant above-chance accuracy for the planning component in the cerebellum or any other ROI. Specifically, the right cerebellar ROIs (CB6: 51.2%, p(unc) = 0.086; CB8: 50.3%, p(unc) = 0.389) did not discriminate between CW and CCW rotations. Moreover, no ROI showed any significant above-chance accuracy for the planning of movement, including motor areas (M1: 51.4%, p(unc) = 0.051 SMA: 49.5%, p(unc) = 0.728), somatosensory areas (S1: 48.3%, p(unc) = 0.975; L SPL: 49.2%, p(unc) = 0.836; R SPL: 48.5%, p(unc) = 0.956; L IPL: 50.2%, p(unc) = 0.383; R IPL: 50.7%, p(unc) = 0.214), and visual area (VIS: 48.8%, p(unc) = 0.921) (Fig. 5 ). This is also particularly surprising for the posterior parietal cortex (PPC), as it was previously reported to play a significant role in visuomotor adaptation by storing the changes in sensorimotor mappings [ 20 ] . Classification of adaptation vs. baseline conditions for the planning components The adaptation vs. baseline contrast (Figure S1) compares visuomotor adaptation (CW and CCW rotations) with the baseline condition (0° rotation). This contrast investigates the neural substrates of learned sensorimotor mappings, independent of the rotational direction, focusing purely on the presence of adaptation rather than the specifics of opposing rotations. In this context, a key consideration is that, due to the nature of MVPA, which relies on voxel-by-voxel pattern analysis, grouping opposite rotations into the same class is counterintuitive for both the execution and visual target components. Therefore, we will focus exclusively on the planning component and disregard the execution and visual target components for this part of the analysis and the following searchlight analysis. For the planning component, neural activity patterns in the sensorimotor ROIs distinguished between trials with and without visuomotor rotations (Figure S1), including M1, S1, the bilateral posterior parietal cortex (PPC), the visual area, bilatereal SMA and the right cerebellar lobule VI (M1: 54.1%, p(unc) < 0.001, p(FWER) < 0.001; S1: 54%, p(unc) < 0.001, p(FWER) < 0.001; L IPL: 53.3%, p(unc) < 0.001, p(FWER) < 0.001; R IPL: 52.3%, p(unc) = 0.001, p(FWER) < 0.002; L SPL: 52.5%, p(unc) < 0.001, p(FWER) < 0.002; R SPL: 52.7%, p(unc) < 0.001, p(FWER) < 0.001; CB6: 52.5%, p(unc) < 0.001, p(FWER) < 0.002; SMA: 52.4%, p(unc) < 0.001, p(FWER) < 0.002; VIS: 52.4%, p(unc) < 0.001, p(FWER) < 0.002). Meanwhile, the right cerebellar lobule VIII did not show significant above-chance decoding accuracy (CB8: 50.9%, p(unc) = 0.116). These results indicate that the response patterns in the sensorimotor ROIs tested exhibit selectivity for visuomotor rotations in this experiment, although they do not show selectivity between opposing visuomotor rotations (Fig. 5 ). MVPA searchlight We conducted a whole-brain searchlight analysis [ 21 ] using a searchlight sphere with a 5-mm radius, which included 93 voxels, to investigate neural selectivity for visuomotor components. We applied cross-classification to runs that shared common visuomotor components, assessed decoding accuracy with AFNI’s 3dttest++, and corrected for multiple comparisons at a cluster level via the -Clustsim option [ 22 ] . Below, we report clusters that either overlap with our predefined ROIs or reveal additional task-relevant locations; the complete list of clusters for both contrasts is provided in Table 3 . Table 3 MVPA searchlight clusters Peak (MNI) Cluster level (p, voxels) z value at X Y Z peak Directional Execution L Primary Sensory Cortex -45 -17 59 < 0.01 / 58 4.609 R Middle Temporal Gyrus 55 -29 -9 < 001 / 57 4.550 R Superior Parietal Lobule 19 -69 57 < 0.02 / 48 4.287 R Lingual Gyrus 37 -83 -19 < 0.04 / 42 4.160 L Superior Parietal Lobule -21 -51 69 < 0.05 / 38 3.604 Planning None Target position Bilateral Occipital Cortex 3 -89 13 < 0.001 / 8920 13 Adaptation vs. baseline Planning R Angular Gyrus R Inferior Parietal Lobule R Superior Parietal Lobule 45 -71 43 < 0.001 / 1478 4.985 L Inferior Frontal Gyrus -53 17 21 < 0.001 / 350 5.071 L Middle Occipital Cortex -33 -75 25 < 0.001 / 225 4.502 R Superior Frontal Gyrus 33 -3 61 < 0.001 / 192 4.866 L Inferior Parietal Lobule -59 -37 41 < 0.001 / 156 4.872 R Supramarginal Gyrus 65 -21 29 < 0.01 / 84 3.523 L Superior Frontal Gyrus -31 -5 67 < 0.01 / 83 4.132 R Superior Frontal Gyrus 25 69 13 < 0.01 / 80 3.569 R Middle Frontal Gyrus 41 37 37 < 0.01 / 62 3.416 R Inferior Frontal Gyrus 49 15 29 < 0.03 / 47 4.168 R Middle Temporal Gyrus 55 -65 15 < 0.04 / 43 4.928 Abbreviations: L, left; MNI, Montreal Neurological Institute; R, right. Supplementary Figure Because our ROI analysis was restricted to regions contralateral to the reaching hand, it did not probe the right hemisphere. The searchlight results for the directional contrast, therefore, complement the ROI-based findings. Similarly to the ROI-decoding analysis, the searchlight map in the left hemisphere highlights spatial selectivity in the S1 according to the direction of the reaching movement. At the same time, the visual cortex significantly decodes according to the position of the target. M1, however, showed no above-chance movement direction decoding. Additionally, the searchlight revealed a direction-selective cluster in the bilateral superior parietal lobule. For the adaptation vs. baseline (planning) contrast, searchlight decoding matched the ROI effects in the right SPL and bilateral IPL. It also detected additional clusters in the bilateral frontal gyrus and left occipital cortex. Discussion Human possess a remarkable ability to adapt to dynamic environments, supported by neural systems capable of processing complex visuomotor tasks. Paradigms such as prism adaptation (23] , force field learning [ 24 ] , and visuomotor rotations [ 25 ] have been developed to investigate how human adapt to novel or altered environments. Through these paradigms, it has been shown that the brain builds internal models to predict the sensory consequences of motor actions [ 1 ] . When a discrepancy arises between the predicted and actual movement outcomes, these internal models are updated through sensory prediction errors, with the cerebellum playing a critical role in this process [ 26 ], [ 27 ] . In this study, we introduce a novel paradigm to investigate how humans learn conflicting motor tasks through visuomotor adaptation. Our approach employs a design inspired by statistical cross-validation to dissociate the neural representations of motor execution, motor planning, and visual target positions. By systematically varying target locations and rotational mappings across fMRI runs, we leverage the overlapping task elements to identify brain regions involved in motor planning and execution and decode task-specific neural activity patterns using MVPA. Our univariate conjunction analysis revealed that the visual target position was the only component showing differential BOLD activity between opposite visuomotor rotations. We identified two clusters, one located above and one below the calcarine sulcus, corresponding to the top-left and bottom-left visual fields, respectively. This functional segregation of the visual field was reported in prior studies [ 14 ], [ 15 ], [ 16 ] and aligns with the established role of the upper bank of the calcarine sulcus in processing information from the lower visual field and the lower bank of the calcarine sulcus in processing information from the upper visual field. Moreover, neural patterns in the visual ROI exhibited visual directional selectivity, depending on the target position on the screen, confirming distinct neural representations in the visual cortex for visual targets. There was no difference in the overall BOLD activity in the sensorimotor areas between opposite visuomotor rotations, as shown in the previous studies [ 10 ], [ 11 ] . Considering the current experimental design, the only significant difference between the two adaptation tasks (CCW and CW) would be the opposite visuomotor rotations applied. Thus, it is less likely that motor execution in different directions involves segregated sensorimotor regions, such as the somatotopic homunculus map. Instead, sensorimotor regions may encode directional information at a finer level, which could be identified through the MVPA approach. Indeed, multiple studies show that the neurons in the motor cortex are tuned to a preferred direction of movement [ 17 ], [ 18 ], [ 28 ] and remain tuned to their preferred direction even after visuomotor adaptation [ 19 ] . Indeed, our MVPA analysis revealed BOLD activity patterns in sensorimotor regions, including M1/S1, the left IPL, and the ipsilateral cerebellar lobule VI, which exhibited directional selectivity for the direction of movement, even when the target to reach was at the same position. Interestingly, we also found significant decoding accuracy for two opposing movement directions in the visual cortices (V1/V2, which corresponds to Brodmann areas 17 and 18). Considering that visual imagery activates early visual areas, such as V1 and V2 [ 29 ], [ 30 ], [ 31 ] , participants may have been engaging in visual imagery, mentally simulating the movement of the cursor toward the targets. If this were the case, the activity in V1 and V2 could be attributed to imagined visual feedback from the movement rather than motor directional selectivity per se. Our experimental design implies that we did not directly study visuomotor adaptation itself but rather the learned sensorimotor mappings that resulted from the adaptation process. From this, we found increased activity throughout the PPC in our univariate analysis for the motor planning component when comparing adaptation trials to baseline trials. This finding aligns with prior studies showing increased PPC activity during the late stages of visuomotor adaptation [ 32 ], [ 33 ] , consistent with the timing of our experiment. Specifically, our analysis localized the motor planning component primarily within the PPC, emphasizing its critical role in storing the learned visuomotor mappings. In contrast, no significant activity was observed in the cerebellum, which is likely due to the participants' advanced stage of learning. The cerebellum is known to play a critical role in predicting sensory errors [ 34 ], [ 35 ], [ 36 ], [ 37 ], [ 38 ] , which is crucial during early stages of adaptation. However, since participants had already adapted to the visuomotor rotations by this stage of the experiment, the number of errors was minimal, thus reducing the cerebellum's overall activity. Tzvi et al. (2020) [ 27 ] found that activity in the right cerebellar lobule VI gradually decreased during adaptation to a visual rotation, which coincides with our results and the decrease in errors made by the participants (Fig. 2 ). Nevertheless, we expected that different neural populations in sensorimotor regions would respond selectively to the planning of opposite visuomotor rotations. However, we found no regions with significant decoding accuracy for the planning component in the directional contrast. This null result contrasts with previous studies [ 10 ], [ 11 ] , which reported decoding in sensorimotor regions, including M1/S1, SMA, parietal regions, and the cerebellum. Notably, those studies demonstrated that movement kinematics (e.g., speed, mean, and error variance) could not account for the significant decoding results. However, unlike the current design, they did not fully counterbalance all three visuomotor components (target position, execution, and planning). One possible explanation for the inconsistent results is that decoding in previous studies may have partially reflected differences in hand movement direction, which our design aimed to minimize. Alternatively, relatively larger visuomotor rotations in the previous studies (Ogawa et al., 2013 [ 10 ] : 90°; Kim et al., 2015 [ 11 ] : 40° ) than those in the current study (30°) may involve more cognitive strategy in planning [ 39 ] , resulting in more distinct activation patterns in the sensorimotor ROIs. Lastly, the sensorimotor representation of motor planning may be dependent on the tool used in the experiment, as seen in a previous study with a joystick and wrist movement, compared to the current study with a tablet and arm movement. Future studies would warrant elucidating the effects of the rotation degree and different tools. Although the neural populations in the cerebellum, bilateral PPC, SMA, S1, and M1, did not distinguish between CW and CCW rotations for the motor planning, they could accurately distinguish between trials with applied visuomotor rotations and trials without them. These results highlight the crucial role of the PPC in storing sensorimotor mappings that result from adaptation in visuomotor tasks. Our study presents notable limitations. First, we included only three runs for the data analysis, which constitutes a relatively small sample of trials for robust decoding analysis, especially in cross-validation frameworks. The limited number of trials may reduce the reliability of the decoding results and constrain the statistical power of the analyses. A more extensive design with additional runs and trials would be beneficial in addressing this limitation. Second, participants initiated their reaching movement as soon as the target appeared (without a preparatory interval), allowing the retrieval of the adaptation plan and the execution of the movement to overlap in time. Consequently, the BOLD signal observed during the tasks reflects a mixture between cognitive retrieval and motor execution, which could prevent a clean dissociation of planning and execution. Our design limits the complete isolation of a pure pre-movement planning. Future studies introducing an explicit preparation time using a Go / No-Go paradigm would separate planning from execution at the behavioral level [ 40 ] . It would also have been interesting to combine both cross-classification with a Go/No-Go cue to obtain a within-run planning-only baseline. In summary, by separating planning and execution through our experimental design, we present new insights into the distributed neural representations underlying visuomotor adaptation. As expected, the primary visual cortex, the first cortical area to receive visual information, can effectively distinguish spatial target positions, reflecting its well-established retinotopic organization. We found that after learning to counteract the rotations, the parietal cortex showed increased neural activity, which likely implies that this region is central to motor memory storage following visuomotor adaptation. However, we found that the cerebellum and PPC (SPL and IPL) could not discriminate the neural patterns associated with opposite motor planning, but could with opposite motor execution. This result seems contradictory to our previous studies, raising open questions about how distinct parameters of visuomotor mapping are represented in the human brain. Materials and Methods Participants We collected behavioral and fMRI data from 44 neurologically healthy participants (22 females; mean age, 24.6 years; range, 19–34 years). All participants were assessed as right-handed by a modified version of the Edinburgh Handedness Inventory [ 41 ] . All individuals had normal or corrected-to-normal vision and were unaware of the study's purpose. We excluded eleven participants: one due to dizziness, eight due to excessive in-scanner movement, and two due to technical problems. In total, 33 participants are included in the data analysis (18 females; mean age = 24.5 years, range = 19–33 years). Participants provided written informed consent, approved by the Sungkyunkwan University Institutional Review Board, Suwon, Republic of Korea (IRB No. 2018-05-003-032) and were compensated monetarily. All the research methods were performed in accordance with the Declaration of Helsinki. Experimental design The experiment consisted of five functional MR imaging (fMRI) runs, each lasting approximately 10 to 14 mins (runs 1–2: 14 mins; run 3: 10 mins; run 4: 13 mins; run 5: 10 mins), with short breaks between runs. Inter-trial intervals (ITIs) were randomly selected from 4 to 14 s in 2-s increments and generated from an exponential distribution [ 42 ] . The sequences of ITIs are different for all participants. Within each trial of the five runs, a white cursor was presented at the center of the screen, indicating both the initial cursor position and the fixation point. A target circle with a diameter of 1.3 cm then appeared on a dark background. Participants were instructed to move the cursor to the target using the pen on the tablet within 1.5 seconds (i.e., a maximum movement time of 1.5 seconds). The trial was considered a miss if they failed to reach the target within this time. To encourage faster responses, the cursor turned red if no movement was made within 800 ms. The trajectory of the cursor feedback was not provided during the movement but was presented after the maximum movement time. The cursor’s final position was shown at a distance of 9 cm from the center for 500 ms to indicate the angular error. The angular error was calculated as the difference between the cursor’s final direction and the target direction, both measured with respect to the center of the screen. According to run- and trial-specific task designs, different types of manipulations were applied to the target position and cursor movement (Fig. 1 ). Within each trial, the target appeared in one of the following locations: (i) middle: 9 cm to the left of the center; (ii) high: 5.2 cm vertically above the middle position; or (iii) low: 5.2 cm vertically below the middle position. Meanwhile, one of the following types of manipulation was applied to the cursor movement: (i) baseline (0º): no perturbation; (ii) +30º rotational perturbation: mapping rotated counterclockwise by 30º, requiring participants to make a movement tilted by + 30º from the desired cursor direction (as illustrated in Fig. 1 A, “Run 1 & 2”, middle panel); or (iii) -30º rotational perturbation: mapping rotated clockwise by 30º, requiring participants to make a movement tilted by -30º from the desired cursor direction (as illustrated in Fig. 1 A, “Run 1 & 2”, bottom panel). In each run, three different colored targets -red, green, and blue- were used to indicate three distinct run-specific tasks. Target colors were counterbalanced across participants to account for any confounding effects. The sequence of tasks T 2 and T 3 was also counterbalanced across runs and participants, with two possible task orders: one starting with T 2 and the other with T 3 . Participants performed 80 trials of the baseline task (T 1 ) in the scanner to familiarize themselves with the experimental environment before fMRI scanning. In runs 1 and 2, participants reached targets located in the same position (middle) but with different rotational mappings applied (0º, + 30º, and − 30º). Accordingly, the three distinct tasks in runs 1 and 2 were as follow: (i) baseline task T 1 (target: red circle; location: middle; rotation: 0º; desired movement: horizontal); (ii) task T 2 (target: green circle; location: middle; rotation: -30º; desired movement: tilted upward); (iii) task T 3 (target: blue circle; location: middle; rotation: +30º; desired movement: tilted downward). Moreover, runs 1 and 2 (each consisting of 144 trials) included three baseline blocks (B C , 12 trials each) that only contained T 1 and three pseudorandomly mixed blocks (B M , 36 trials each) that contained T 1 , T 2 , and T 3 . The baseline and mixed blocks alternated in a fixed order: B C -B M -B C -B M -B C -B M . The order of tasks T 1 , T 2 , and T 3 in the mixed blocks was pseudorandomized and identical for all participants. In each mixed block, the six possible sequences of the three tasks (e.g., T 1 -T 2 -T 3 and T 2 -T 3 -T 1 ) appeared once in each half of the blocks. Additionally, to avoid consecutive presentations of the same task type, the last task of each sequence differed from the first task of the following sequence (so that, for instance, two sequences of task T 2 do not appear consecutively). These structures were identical for runs 1 and 2, except that the order of task types within the mixed blocks was counterbalanced. While runs 1 to 2 were designed to help participants learn different rotational mappings, runs 3 to 5 were deliberately designed to dissociate the components of motor execution, motor planning, and visual information related to the target position. Run 3 was designed to isolate motor execution and motor planning while keeping visual target information constant. By maintaining a fixed target position (middle) but altering rotational mappings, we created conditions where the required physical movement (Execution) and the decision-making processes (Planning) varied, but the visual input remained identical. In run 3 (108 trials), participants reached targets located in the same position (middle) but with different rotational mappings, similar to those in runs 1 and 2. Thus, run 3 also included the same tasks: T 1 , T 2 , and T 3 . However, run 3 consisted of single-task blocks (12 trials each) of T 1 , T 2 , and T 3 (notated as B 1 , B 2 , and B 3 , respectively), resulting in schedules such as B 3 -B 1 -B 2 -B 3 -B 2 -B 1 -B 3 -B 1 -B 2 . If participants successfully learned the tasks presented in runs 1 and 2, they would have accumulated task-related knowledge (e.g., information on different rotational mappings) to guide their motor planning process in run 3. We assumed that run 3 (illustrated as Fig. 1 B, uppermost rectangle) involved information about different types of motor execution (i.e., movements in different directions; Fig. 1 B, “Execution”), as well as motor planning (i.e., distinct decision-making processes for targets with distinct rotational mappings; Fig. 1 B, “Planning”). Run 4 is designed to isolate motor planning and target position information while controlling for motor execution. By manipulating the target locations and rotational perturbations such that they offset each other, participants were required to plan distinct rotations for different visual targets, yet resulted in the identical physical hand movement (horizontal) across the different conditions (Table 1 . T 4 and T 5 ). In the first part of run 4 (108 trials, with rotational mappings intact), participants reached targets at different positions and with varying rotational mappings. However, they made movements in the same direction (i.e., horizontal movement) for all targets, based on the following three tasks: (i) T 1 (target: red circle; location: middle; rotation: 0º; desired movement: horizontal); (ii) T 4 (target: green circle; location: low; rotation: -30º; desired movement: horizontal); (iii) T 5 (target: blue circle; location: high; rotation: +30º; desired movement: horizontal). This part of run 4 consisted of nine alternating blocks of T 1 , T 4 , and T 5 . Although the tasks required distinct motor planning (based on knowledge about different rotational mappings), participants made similar movements for all task types. Thus, we assumed that run 4 (illustrated in Fig. 1 B, middle rectangle) involved information regarding distinct motor planning (Fig. 1 B, “Planning”) and distinct target position information (Fig. 1 B, “Target position”). The second part of run 4 comprised two washout blocks (24 trials), in which all rotational perturbations were removed. Participants made direct movements toward the targets, performing the following tasks: (i) (target: red circle; location: middle; rotation: 0º; desired movement: horizontal); (ii) (target: green circle; location: low; rotation: 0º (turned off); desired movement: tilted downward); (iii) (target: blue circle; location: high; rotation: 0º (turned off); desired movement: tilted upward)—the washout block eliminates any aftereffects of rotational perturbations before run 5. Adaptation to visuomotor rotations typically results in strong aftereffects, that corresponds to strong deviations in movement trajectory opposite to the applied rotation, when the perturbation is suddenly removed (add ref). We wanted to ensure that participants could perform the tasks within run 5 accurately from the very first trial, avoiding the need for a “de-adaptation” process during run 5. Run 5 was designed to isolate motor execution and target position in the absence of motor planning. With the visuomotor rotations removed, the task required varying physical movement directed at varying visual target locations, mimicking the kinematics of previous runs but without compensating for rotations (Table 1 . T 6 and T 7 ). In run 5 (108 trials), all rotational perturbations were removed, and participants made direct movements toward the targets. The tasks were as follow: (i) T 1 (target: red circle; location: middle; rotation: 0º; desired movement: horizontal); (ii) T 6 (target: green circle; location: high; rotation: 0º (turned off); desired movement: tilted upward); (iii) T 7 (target: blue circle; location: low; rotation: 0º (turned off); desired movement: tilted downward). The block schedule was B 1 -B 7 -B 6 -B 1 -B 6 -B 7 -B 1 -B 7 -B 6 , where B 6 and B 7 respectively denote a block of 12 T 6 and 12 T 7 trials. In run 5, motor movements similar to those in runs 1 through 3 (i.e., horizontal, upward, and downward movements) were expected, but in response to different target positions (middle, high, and low). We hypothesized that run 5 (illustrated as Fig. 1 B, lower rectangle) involves information regarding different types of motor execution (Fig. 1 B, “Execution”), as well as different target position information (Fig. 1 B, “Target position”). MRI acquisition fMRI data was acquired using a 3-T Siemens scanner with a 64-channel head coil. The participants were provided with earplugs to minimize noise from the fMRI and foam pads to reduce head motion. Echo planar imaging (EPI) sequence was used to acquire functional scans (repetition time (TR), 2000 ms; echo time (TE), 35 ms; flip angle (FA), 90º; field of view (FOV), 200 × 200 mm; matrix, 100 × 100; axial slices, 72; and slice thickness, 2mm). For anatomical reference, a whole-brain T1-weighted anatomical scan with an MP-RAGE sequence (TR, 2300 ms; TE, 2.28 ms; FA, 8º, FOV, 256 × 256 mm; matrix, 256 × 256; axial slices, 192; and slice thickness of 1mm) was acquired before run 3. fMRI data preprocessing Imaging data were preprocessed using AFNI software (Analysis of Functional NeuroImages, NIH, https://afni.nimh.nih.gov [ 43 ], [ 44 ] ). All functional images were first corrected for slice-time acquisition and realigned to adjust for motion-related artifacts. Then, the realigned images were spatially registered to the anatomical data, which were later transformed into the Montreal Neurological Institute (MNI) template and resampled into 2-mm-cube voxels. All images were spatially smoothed using a Gaussian kernel with a full width at half-maximum of 4 x 4 x 4 mm. ROI definition To define visuomotor-related Regions of Interest (ROIs), we first determined regions with important task activation throughout the brain using a whole-brain general linear model (GLM) analysis. To avoid double-dipping, we used the first two runs of each participant to compute the activation maps. For each run, the three task regressors are added to the design matrix along with six regressors of non-interest, which model the six head motion parameters and polynomials for fMRI signal drifts, resulting in two activation maps for each participant. Subsequently, we average the two activation maps for each participant and utilize AFNI's 3dttest + + function [ 22 ] for second-level group analysis. Afterward, we identify highly activated regions from task-related activity by thresholding the activation maps at uncorrected p < 0.001. To further determine our ROIs, we extracted predefined ROIs from the Automated Anatomical Labeling (AAL) toolbox [ 45 ] and Brodmann atlas, included in MRIcro software ( https://www.mricro.com ). These ROIs included the left precentral cortex (M1), left postcentral cortex (S1), bilateral supplementary motor area (SMA), right cerebellar lobule VI (CB6) and VIII (CB8), bilateral inferior parietal lobule (IPL), and bilateral superior parietal lobule (SPL). For the visual area (VIS), we used Broadman areas 17 and 18. Using the task-related activation map and the predefined ROIs, we then defined our visuomotor-related ROIs by combining them. This resulted in 10 ROIs that we used for further analysis. Whole-brain univariate analysis As our experiment is designed to separate the visuomotor components of motor movements, we perform GLM analysis for the third, fourth, and fifth runs separately using AFNI's 3dDeconvolve. After preprocessing the runs as described above, we perform a GLM with the three task regressors separately, along with regressors of non-interest, consisting of the six head motion parameters and polynomials for fMRI signal drifts. To examine the differences between visuomotor rotation tasks (CCW and CW rotations) and between adaptation tasks and the baseline task, we define two contrasts for all subsequent analyses. The first contrast is the directional contrast, which examines the difference between the two visuomotor rotation tasks by contrasting CCW and CW rotations. This contrast allows us to assess whether there are directional differences in motor movement when participants adapt to opposing visuomotor rotations. The second contrast is the adaptation vs. baseline contrast, which compares the two visuomotor rotation tasks (CCW and CW) with the baseline task (0° rotation). This contrast helps us examine the impact of visuomotor rotation on motor movement by determining how the presence of a rotational perturbation affects motor execution, planning, and visual information processing compared to a task without rotation. For runs 3 and 4, the visuomotor rotation tasks are defined as the actual rotation (CCW and CW), while the baseline task is defined as the task without rotation (0°). However, for run 5, where no rotations were applied, we define the contrasts based on the target positions on the screen. The upper and lower target positions correspond to the adaptation tasks, while the middle target position represents the baseline task (see the experimental design for more details). Conjunction analysis The conjunction analysis uses the statistical maps generated from the whole-brain univariate analysis to discriminate the neural components involved in the execution, planning, and visual information processing of the motor movement. This approach enables us to identify brain regions where multiple conditions, tasks, or contrasts exhibit overlapping significant activation. Given that participants successfully adapted to the visuomotor rotations, we hypothesize that a conjunction analysis can effectively separate these three visuomotor components. For the execution component, we analyze the statistical map derived from runs 3 and 5. The planning component utilizes statistical maps from runs 3 and 4, while the visual information processing components utilize runs 4 and 5. Statistical maps are obtained by applying a threshold at an uncorrected voxel-level p-value of 0.001 (two-sided) and a false positive rate (FPR) for multiple comparisons on a cluster level at a threshold of α = 0.05. These thresholds are established using AFNI's 3dttest + + function with the Clustsim option [ 22 ] to control for multiple comparisons. Subsequently, for each contrast (directional and adaptation vs. baseline), we compare the statistically significant clusters of voxels identified for each visuomotor component, allowing us to reveal overlapping activations across components. MVPA ROI analysis To determine whether sensorimotor regions encode distinct visuomotor features, we employed cross-classification, a multivariate approach analogous to conjunction analysis [ 10 ] . Our design (Fig. 1 C) manipulated three components per trial: execution (movement direction: up or 30°, middle or 0°, low or − 30°), planning (visuomotor rotation: CW 30°, baseline 0°, CCW 30°), and visual target position (upper, middle, lower visual field). Each run contained two of these components, allowing us to train a classifier on one run and test it on another that shared the component of interest. For example, runs 3 and 5 both required identical reaching movement directions, so cross-classifying 3 and 5 isolates execution-specific coding. Analogous pairings also isolate planning or visual-target component-specific coding. Above-chance performance identifies ROIs that are selective for the component of interest. This approach allows us to dissociate the representation of each visuomotor component. We applied multivoxel pattern analysis (MVPA), a multivariate approach designed to detect differences in activation patterns across multiple voxels [ 5 ] , to compute decoding accuracies within the ROIs. To estimate response amplitudes, we employed the least squares sum method, which minimizes collinearity between adjacent trials [ 46 ] . For each voxel, the model included one regressor for the current trial, three regressors for all other trials split by task type, six head-motion parameters, and polynomial drift terms. Missed trials are excluded from the analysis (see behavioral analysis for details). We converted β estimates to t -values to down-weight high-variance voxels [ 47 ] . Our analysis focused on whether the directional and adaptation vs. baseline contrasts could be discriminated within the defined ROIs. For classification, we used a linear support vector machine classifier (SVC) implemented in scikit-learn [ 48 ] with default parameters (version 1.6.1, regularization parameter C = 1). The experimental design provided a natural two-fold cross-validation: train on run A and test on run B, and vice versa. Because for both contrasts, the number of trials in the training set was unbalanced, class weights were set inversely to class frequency to prevent bias towards the overrepresented class. To assess statistical significance, we used a permutation test instead of relying on the theoretical chance level of 50%. For each ROI × component × contrast, we generated participant-specific null distributions by permuting class labels 2 000 times. We then bootstrapped 10 5 group-level samples by resampling (with replacement) a single chance decoding accuracy per participant (N = 33) and averaged it to obtain a mean chance decoding accuracy. The mean decoding accuracy for each ROI was considered significant if it exceeded the 97.5th percentile of this distribution (two-tailed α = 0.05). To correct multiple comparisons across ROIs, we controlled the family-wise error rate (FWER) using the Holm-Bonferroni correction [ 49 ] . MVPA searchlight analysis In addition to the ROI analysis, we used a searchlight analysis [ 21 ] to compute decoding accuracy across the entire brain. We used t -values, as we did for the MVPA ROI, with a radius of 5 mm (93 voxels) and a linear support vector machine classifier (SVC) using the default parameters implemented in scikit-learn (version 1.6.1). After computing decoding accuracy for each visuomotor component at each searchlight within the brain volume, we use AFNI’s function 3dttest++ [ 22 ] to determine whether each voxel exhibits above-chance decoding accuracy. For statistical significance, we use uncorrected p < 0.001 with the Clustsim option to account for FPR in multiple comparisons at a cluster level, with a threshold of α = 0.05. Behavioral measure We obtained the hand direction degree for each trial to ensure that the participants were learning the different visuomotor mappings. The measurements were determined as the angle between the first point and the endpoint of the trajectories drawn by the tablet for each trial. Missed trials that did not reach the target within the maximum movement time were excluded from the analysis. Because the rotated mappings of T 2 and T 3 were presented in the opposite direction according to the counterbalanced group, the measurements of the second group participants were plotted as negative values when the average hand direction degree was visualized (Fig. 1 B). For statistical analysis, we conducted one-sample t-tests comparing each participant’s mean movement angle to the angle corresponding to the center of the target (30°, 0°, or − 30°, depending on the condition). Declarations Competing interests The authors declare no competing interests. Funding Declaration This work was supported by the National Research Foundation of Korea (NRF-2021R1A2C2011648, RS-2024-00356694), Hanyang University and Korea Basic Science Institute (KBSI) (HY-202400000003862), Center for Neuroscience Imaging Research, Institute for Basic Science, Korea (IBS-R015-D1), Yangyoung Foundation. Author Contribution S.K. conceived the idea presented in the study and designed the experiment. S.K. carried out the experiment. A.C. performed formal analysis; A.C., and S.K. wrote the manuscript. Acknowledgements Neuroimaging was performed at the Center for Neuroscience Imaging Research located at Sungkyunkwan University, supported by the Institute for Basic Science. Data Availability All MRI data used in this study were archived in the Hanyang University Network Attached Storage (NAS). All the dataset and codes related to this study are available from the corresponding author on reasonable request. References Wolpert, D. M. & Kawato, M. Multiple paired forward and inverse models for motor control. Neural Netw. 11 (7–8), 1317–1329 (1998). Imamizu, H. et al. Modular organization of internal models of tools in the human cerebellum. Proc. Natl. Acad. Sci. U S A . 100 (9), 5461–5466 (2003). Imamizu, H. et al. Functional magnetic resonance imaging examination of two modular architectures for switching multiple internal models. J. Neurosci. 24 (5), 1173–1181 (2004). Hirashima, M. & Nozaki, D. Distinct motor plans form and retrieve distinct motor memories for physically identical movements. Curr. Biol. 22 (5), 432–436 (2012). Krasovsky, A. et al. Differentiating intended sensory outcome from underlying motor actions in the human brain. J. Neurosci. 34 (46), 15446–15454 (2014). Osu, R. et al. Random presentation enables subjects to adapt to two opposing forces on the hand. Nat. Neurosci. 7 (2), 111–112 (2004). Krakauer, J. W. et al. Generalization of motor learning depends on the history of prior action. PLoS Biol. 4 (10), E316 (2006). Lee, J. Y. & Schweighofer, N. Dual adaptation supports a parallel architecture of motor memory. J. Neurosci. 29 (33), 10396–10404 (2009). Woolley, D. G. et al. Dual adaptation to two opposing visuomotor rotations when each is associated with different regions of workspace. Exp. Brain Res. 179 (2), 155–165 (2007). Ogawa, K. & Imamizu, H. Human sensorimotor cortex represents conflicting visuomotor mappings. J. Neurosci. 33 (15), 6412–6422 (2013). Kim, S. et al. Neural Substrates Related to Motor Memory with Multiple Timescales in Sensorimotor Adaptation. PLoS Biol. 13 (12), E1002312 (2015). Sheahan, H. R., Franklin, D. W. & Wolpert, D. M. Motor Planning, Not Execution. Separates Motor Memories Neuron . 92 (4), 773–779 (2016). Lee, K. et al. Sensory prediction errors, not performance errors, update memories in visuomotor adaptation. Sci. Rep. 8 (1), 16483 (2018). Schneider, W., Noll, D. & Cohen, J. Functional topographic mapping of the cortical ribbon in human vision with conventional MRI scanners. Nature 365 , 150–153 (1993). Sereno, M. I. et al. Borders of Multiple Visual Areas in Humans Revealed by Functional Magnetic Resonance Imaging. Science 268 , 889–893 (1995). DeYoe, E. A. et al. Mapping striate and extrastriate visual areas in Human cerebral cortex. Proc. Natl. Acad. Sci. U.S.A. 93 (6) 2382–2386 (1996). Eisenberg, M., Shmuelof, L., Vaadia, E. & Zohary, E. Functional organization of human motor cortex: directional selectivity for movement. J. Neurosci. 30 , 8897–8905 (2010). Fabbri, S., Caramazza, A. & Lingnau, A. Tuning curves for movement direction in the human visuomotor system. J. Neurosci. Oct. 6 (40), 13488–13498 (2010). Haar, S., Donchin, O. & Dinstein, I. Dissociating visual and motor directional selectivity using visuomotor adaptation. J Neurosci. ;35:6813–21, 2015. (2015). Tanaka, H., Sejnowski, T. J. & Krakauer, J. W. Adaptation to Visuomotor Rotation Through Interaction Between Posterior Parietal and Motor Cortical Areas. J. Neurophysiol. 102 (5), 2921–2932 (2009). Kriegeskorte, N., Goebel, R. & Bandettini, P. A. Information-based functional brain mapping. Proc. Natl. Acad. Sci. U.S.A. 103 (10) 3863–3868, (2006). Cox, R. W., Chen, G., Glen, D. R., Reynolds, R. C. & Taylor, P. A. fMRI clustering and false-positive rates. Proc. Natl. Acad. Sci. USA . 114 (17), E3370–E3371 (2017). Von Helmholtz, H. Handbuch der Physiologischen Optik. Leipzig, Germany: Voss, 1867 . Shadmehr, R. & Mussa-Ivaldi, F. A. Adaptive representation of dynamics during learning of a motor task. J. Neurosci. 14 , 3208–3224 (1994). Krakauer, J. W., Pine, Z. M., Ghilardi, M-F. & Ghez, C. Learning of visuomotor transformations for vectorial planning of reaching trajectories. J. Neurosci. 20 , 8916–8924 (2000). Taylor, J. A., Klemfuss, N. M. & Ivry, R. B. An explicit strategy prevails when the cerebellum fails to compute movement errors. Cerebellum 9 , 580–586 (2010). Tzvi, E., Koeth, F., Karabanov, A. N., Siebner, H. R. & Krämer, U. M. Cerebellar – premotor cortex interactions underlying visuomotor adaptation. Neuroimage (2020). Truccolo, W., Friehs, G. M., Donoghue, J. P. & Hochberg, L. R. Primary motor cortex tuning to intended movement kinematics in humans with tetraplegia. J Neurosci. ;28:1163–1178, 2008. (2008). Kosslyn, S. M. et al. Visual mental imagery activates topographically organized visual cortex: PET investigations. J. Cogn. Neurosci. 7 (3), 332–345 (1993). Slotnick, S. D., Thompson, W. L. & Kosslyn, S. M. Visual mental imagery induces retinotopically organized activation of early visual areas. Cereb. Cortex . 15 (10), 1570–1583 (2005). Albers, A. M., Kok, P., Toni, I., Dijkerman, H. C. & de Lange, F. P. Shared representations for working memory and mental imagery in early visual cortex. Curr. Biol. 23 (15), 1427–1431 (2013). Della-Maggiore, V., Malfait, N., Ostry, D. J. & Paus, T. Stimulation of the posterior parietal cortex interferes with arm trajectory adjustments during the learning of new dynamics. J. Neurosci. 24 , 9971–9976 (2004). Krakauer, J. W. et al. Differential cortical and subcortical activations in learning rotations and gains for reaching: A PET study. J. Neurophysiol. 91 , 924–933 (2004). Blakemore, S. J., Frith, C. D. & Wolpert, D. M. The cerebellum is involved in predicting the sensory consequences of action. NeuroReport 12 (19), 1879–1884 (2001). Tseng, Y. W., Diedrichsen, J., Krakauer, J. W., Shadmehr, R. & Bastian, A. J. Sensory prediction errors drive cerebellum-dependent adaptation of reaching. J. Neurophysiol. 98 , 54–62 (2007). Schlerf, J., Ivry, R. B. & Diedrichsen, J. Encoding of sensory prediction errors in the human cerebellum. J. Neurosci. 32 , 4913–4922 (2012). Morehead, J. R., Taylor, J. A., Parvin, D. E. & Ivry, R. B. Characteristics of implicit sensorimotor adaptation revealed by task-irrelevant clamped feedback. J. Cogn. Neurosci. 29 , 1061–1074 (2017). Tzvi, E., Loens, S. & Donchin, O. Mini-review: the role of the cerebellum in visuomotor adaptation. Cerebellum 21 (2), 306–313 (2022). Taylor, J. A., Krakauer, J. W. & Ivry, R. B. Explicit Explicit and Implicit Contributions to Learning in a Sensorimotor Adaptation Task. J. Neurosci. 34 (8), 3023–3032 (2014). Ariani, G., Pruszynski, J. A. & Diedrichsen, J. Motor planning brings human primary somatosensory cortex into action-specific preparatory states. elife 11 , e69517 (2022). Oldfield, R. C. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9 (1), 97–113 (1971). Burock, M. A. et al. Randomized event-related experimental designs allow for extremely rapid presentation rates using functional MRI. Neuroreport 9 (16), 3735–3739 (1998). Cox, R. W. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29 (3), 162–173 (1996). Cox, R. W. & Hyde, J. S. Software tools for analysis and visualization of FMRI Data. NMR Biomed. 10 , 171–178 (1997). Tzourio-Mazoyer, N. et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. ;15(1):273 – 89. (2002). 10.1006/nimg.2001.0978 . PMID: 11771995. Mumford, J. A., Turner, B. O., Ashby, F. G. & Poldrack, R. A. Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses. Misaki, M., Kim, Y., Bandettini, P. A. & Kriegeskorte, N. Comparison of multivariate classifier and response normalizations for pattern-information fMRI. NeuroImage, Volume 53, Issue 1, Pages 103–118.Neuroimage. 2012;59(3):2636-43. (2010). 10.1016/j.neuroimage.2011.08.076 . Epub 2011 Sep 5. PMID: 21924359; PMCID: PMC3251697. Pedregosa et al. Scikit-learn: Machine Learning in Python. JMLR 12, pp. 2825–2830, (2011). Holm, S. & Simple Sequentially, A. Rejective Multiple Test Procedure. Scand. J. Stat. 6 (2), 65–70 (1979). Additional Declarations No competing interests reported. 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(A) During adaptation trials (T\u003csub\u003e2\u003c/sub\u003e and T\u003csub\u003e3\u003c/sub\u003e), cursor rotations of 30° were applied, either clockwise or counterclockwise, while the baseline trials (T\u003csub\u003e1\u003c/sub\u003e) had no visuomotor rotation applied. Participants used an MRI-compatible pen and a tablet to reach targets located on the left side of the screen, receiving feedback about the angular error at the end of each trial. The angular error was calculated as the deviation between the cursor’s final direction and the target’s direction. The first two fMRI runs were dedicated to learning the visuomotor rotations, while the last three fMRI runs were designed to separate visuomotor components. During fMRI runs 1 to 3, targets were positioned horizontally at 9 cm to the left of the screen's center, and visuomotor rotations were applied during adaptation trials. This design captures the neural activity related to both motor execution (reaching movements) and planning (rotational mappings). fMRI run 4 consisted of two phases: During the first phase, visuomotor rotations remained active to further assess motor planning and visual target position processing (T\u003csub\u003e4\u003c/sub\u003e and T\u003csub\u003e5\u003c/sub\u003e). The second phase introduced a washout period of 24 trials, in which visuomotor rotations were removed to prevent interference from the learned visuomotor rotations in the subsequent fMRI run. In fMRI run 5, participants performed reaching movements toward the targets while the visuomotor rotations were removed (T\u003csub\u003e6\u003c/sub\u003e and T\u003csub\u003e7\u003c/sub\u003e). We expect participants to accumulate information regarding the motor execution and visual target position. (B) Scheme of experimental design allowing separation of visuomotor components. Each fMRI run comprises two of the three visuomotor components, allowing for the use of a cross-validation scheme to dissociate the visuomotor components related to motor execution, motor planning, and visual information processing about the target position.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8838307/v1/8c2a81912cf3b878a7481be3.png"},{"id":105319451,"identity":"1997f7f5-7b4a-412d-bc99-62cab29f078c","added_by":"auto","created_at":"2026-03-24 17:04:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":33585,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBehavioral measurement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe average performance of the participants was measured as the hand direction at every trial. Hand direction is calculated as the angle between the first point and the endpoint of the trajectories drawn by the participants on the tablet. Bar errors represent the standard error across participants.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8838307/v1/da0a5aa51778eb9e29664568.png"},{"id":105564597,"identity":"c5dadc93-a2b9-409c-aa69-8a76df447af9","added_by":"auto","created_at":"2026-03-27 12:50:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":124231,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRepresentative brain areas of regions of interest.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROIs were defined as the conjunction of activation maps derived from the motor adaptation task (thresholded at uncorrected p \u0026lt; 0.001) and predefined ROIs from the AAL Atlas. (A) L, left; R, right; M1, left primary motor area; S1, left primary somatosensory cortex; SMA, supplementary motor area; SPL, superior parietal lobule; IPL, inferior parietal lobule; VIS, early visual area (Brodmann area 17 \u0026amp; 18). (B) CBL6/CBL8, right cerebellar lobule VII and lobule VIII.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8838307/v1/18239c360746264439e632f0.png"},{"id":105319455,"identity":"3ff00c9c-e688-4d11-ba65-4dc82e605374","added_by":"auto","created_at":"2026-03-24 17:04:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":219225,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMass-univariate conjunction analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Venn Diagram of the cross-validation experimental design. (B) Conjunction map displayed on a flattened surface of the right hemisphere in visual-related regions for the directional contrast with the inflated cortical surface above, highlighting the area of interest. (C) Cortical surface map of the left hemisphere (left panel) and right hemisphere (right panel) centered on the sensorimotor-related regions highlighted on the inflated cortical surface above for the adaptation vs. baseline contrast. Statistical maps are thresholded at uncorrected p \u0026lt; 0.001, cluster-level correction at α = 0.05. Shared activations are color-coded: red for execution, green for visual target position, blue for learning, and black for the three overlapping components. Major sulci are indicated in white dotted lines. CS = central sulcus; PrCS = precentral sulcus; PoCS = postcentral sulcus; PMC = premotor cortex; M1 = primary motor area; S1 = primary somatosensory area; SPL = superior parietal lobule; IPL = inferior parietal lobule; SFG = superior frontal gyrus; MFG = middle frontal gyrus.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8838307/v1/a78def9c4ca28baa703ce425.png"},{"id":105319453,"identity":"cdc0b192-7db1-47cb-b12e-c4f051188113","added_by":"auto","created_at":"2026-03-24 17:04:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":24721,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDirectional decoding.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe calculated the mean decoding accuracies for the directional contrast across participants using a cross-validation approach to dissociate visuomotor components for each ROI. Decoding was performed for the three visuomotor components: execution (red), planning (blue), and visual target position (green). The dotted line marks the theoretical decoding chance accuracy (50%, two visuomotor rotations), and error bars indicate SEM across participants. Significant above-chance decoding accuracy (randomization analysis, Holm-Bonferroni correction) is indicated with asterisks (* \u0026lt; 0.05, ** \u0026lt; 0.01, *** \u0026lt; 0.001). Abbreviations: R (right), L (left), M1 (left primary motor area), S1 (left primary somatosensory area), SMA (supplementary motor area), SPL (superior parietal lobule), IPL (inferior parietal lobule), VIS (early visual area – Brodmann 17 \u0026amp; 18), CB6/CB8 (right cerebellar lobules VI and VIII, respectively).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8838307/v1/6fc66909ec7c1638e5497124.png"},{"id":107320927,"identity":"9bcac46c-11fe-4fd4-906d-15acda33c81b","added_by":"auto","created_at":"2026-04-20 10:28:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1293610,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8838307/v1/1c610439-cbbe-4976-94e7-c1334f8d9adb.pdf"},{"id":105319450,"identity":"c4e68964-ebe0-4a9a-9381-36fb63277291","added_by":"auto","created_at":"2026-03-24 17:04:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":39954,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-8838307/v1/9a3f166f4667389df7723ddc.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dissociated neural substrates of motor execution and planning in learning multiple sensorimotor mappings","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHuman can simultaneously perform multiple motor tasks by learning distinct relationships between actions and their outcomes while building separate internal models\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Many real-world motor tasks are conflicting in that they often require identical actions for distinct outcomes or distinct actions for identical outcomes\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. For example, you may press the light button (identical action) to either turn on or off the light (distinct outcomes). In the other case, you may slide or pull the door (distinct actions) to open it (identical outcome).\u003c/p\u003e \u003cp\u003ePrevious studies have demonstrated that conflicting sensorimotor mappings can be acquired distinctly with contextual cues despite initial interference through extensive training\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Although conflicting sensorimotor mappings (e.g., opposite visuomotor rotations) with similar task structures activate overlapping brain regions, we can successfully discriminate them using distributed patterns of fMRI activity in the sensorimotor system, including the cerebellum\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. However, in our previous fMRI experiment, the conflicting mappings were associated with distinct plans and corresponding execution, without dissociating them\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Indeed, motor planning and execution appear to play a distinct role in motor adaptation, and few behavioral studies have demonstrated a more significant role of planning over execution in the formation and retrieval of distinct motor memories\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe, therefore, aimed to understand further how motor planning and execution are distinctively represented in the brain during the learning of multiple sensorimotor mappings. We deliberately designed an fMRI experiment that allowed us to dissociate motor planning and execution. Specifically, participants concurrently adapted to three sensorimotor mappings, one veridical and two mappings with conflicting rotations between actual reaching movements and their visual feedback. In the subsequent three experimental fMRI runs, participants performed different types of trials combining target positions and imposed rotations. We sought to dissociate each of the three components of visuomotor adaptation shared between every pair of two experimental runs: (1) planning to counteract the rotation, (2) execution of movement, and (3) visual direction toward a target.\u003c/p\u003e \u003cp\u003eWe first used general linear model analysis (GLM) and subsequent conjunction analysis to dissociate the three components, determining brain regions or voxels that were commonly active across different trial types (as an analogy, you could think of it as the logical \u0026ldquo;AND\u0026rdquo; between two or more different conditions). Furthermore, we also applied MVPA with a previously suggested cross-classification, training on one set of trials and testing on a different set of trials to see if the neural patterns could generalize to a new context, which is a counterpart of conjunction analysis in GLM. The conjunction analysis and cross-classification ensured the dissociation of the invariant representation of planning, execution, and visual directions\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSuccessful adaptation of multiple visuomotor mappings\u003c/h2\u003e \u003cp\u003eThirty-three right-handed participants completed the experiment. For each trial, participants were instructed to reach a circular target that appeared on the left side of the screen using an MRI-compatible tablet and a pen. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the design of the experiment and Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the different tasks. In fMRI runs 1\u0026ndash;4 (prior to the washout), three different visuomotor rotations of the cursor movement (0\u0026ordm;; 30\u0026ordm;, clockwise (CW); -30\u0026ordm;, counterclockwise (CCW)) were pseudorandomly applied with contextual color cues for the three different tasks: baseline task (T\u003csub\u003e1\u003c/sub\u003e, red), task 2 (T\u003csub\u003e2\u003c/sub\u003e, green), and task 3 (T\u003csub\u003e3\u003c/sub\u003e, blue) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). To examine the case in which the rotation mappings were turned off, a brief washout phase was introduced at the end of run 4. Thus, in the washout and run 5, the three tasks were only differentiated by the target position, and the ideal movement degree would be identical to the target position (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCondition matrix\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCondition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRun(s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTarget Color\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTarget Position\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVisuomotor Rotation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRequired Hand Movement\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHorizontal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGreen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-30\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUpward\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;30\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDownward\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGreen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;30\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHorizontal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-30\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHorizontal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGreen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUpward\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDownward\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e Participants fully adapted to the mappings throughout fMRI runs 1 and 2, in which the tasks highly interfered with each other due to their presentation in a pseudorandom order. Specifically, in the last block of run 2, the movement directions were not significantly different from their \u0026ldquo;goal\u0026rdquo; directions, i.e., counteracting movements to the imposed rotations to reach the target (T\u003csub\u003e2\u003c/sub\u003e: 29.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u0026deg; (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE), T\u003csub\u003e3\u003c/sub\u003e: -29.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u0026deg;, T\u003csub\u003e1\u003c/sub\u003e: 0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u0026deg;; p\u0026thinsp;\u0026gt;\u0026thinsp;0.32, for all three tasks; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the subsequent run 3, in which blocks of the tasks were presented, we maintained the learned associations between the contextual cues for the tasks and the movement directions (T\u003csub\u003e2\u003c/sub\u003e: 29.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u0026deg;, T\u003csub\u003e3\u003c/sub\u003e: -29.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u0026deg;, T\u003csub\u003e1\u003c/sub\u003e: 0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u0026deg;; p\u0026thinsp;\u0026gt;\u0026thinsp;0.15 for all three tasks). Throughout runs 1 to 3, participants successfully discriminated the task by gradually dividing the movements into three goal directions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In run 4, in which the goal directions were set to 0\u0026ordm; in order to isolate the planning component, participants maintained movement degrees to 0\u0026ordm;, successfully reaching the targets regardless of their rotation mappings (T\u003csub\u003e4\u003c/sub\u003e: -0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u0026deg;, T\u003csub\u003e5\u003c/sub\u003e: 0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u0026deg;, T\u003csub\u003e1\u003c/sub\u003e: 1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u0026deg;; p\u0026thinsp;\u0026gt;\u0026thinsp;0.08 for all three tasks). Meanwhile, in the washout phase, where the rotation mappings were removed, the actual movement degrees gradually diverged into either 30\u0026ordm; or -30\u0026ordm; (T\u003csub\u003e6\u003c/sub\u003e: 31.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u0026deg;, T\u003csub\u003e7\u003c/sub\u003e: -32.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u0026deg;). Participants also exhibited the expected patterns of movement degrees in run 5, where the rotation mappings were absent (T\u003csub\u003e6\u003c/sub\u003e: 33.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u0026deg;, T\u003csub\u003e7\u003c/sub\u003e: -30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u0026deg;, T\u003csub\u003e1\u003c/sub\u003e: 0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u0026deg;). Notably, they displayed a slight systematic bias toward the upper-field target in this run. This bias may stem from the slightly awkward arm posture required to reach that position compared to the reaching movement needed for the target located in the lower part of the screen. However, the offset is negligible. An aiming angle of approximately 33.4\u0026deg; still places the cursor on the target, so overall task performance remains unaffected. Given participants' successful performance, we assumed that runs 3, 4 and 5, would reflect the acquired knowledge of different tasks, particularly the various rotational mappings.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eConjunction Analysis\u003c/h3\u003e\n\u003cp\u003eWe conducted a conjunction analysis using a voxel-wise general linear model (GLM) on data from runs 3, 4, and 5 to dissociate the three visuomotor components of visuomotor adaptation: execution, planning, and visual target processing. As participants successfully adapted to the three visuomotor mappings, we hypothesized that the associated visuomotor components would be reflected in the BOLD response. In the whole-brain univariate analysis, we examined two contrasts designed to assess activation differences between conditions: (1) the adaptation-versus-baseline contrast (CW and CCW rotations vs. 0\u0026deg; baseline) and (2) the directional contrast (CW vs. CCW). For run 5, occurring after the washout phase, the conditions are associated with the contextual color cues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eFor the adaptation vs. baseline contrast, we identified significant regions related to the three visuomotor components (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). For the planning component (conjunction between runs 3 and 4), we identified clusters in the bilateral superior parietal lobe (SPL) and inferior parietal lobe (IPL), as well as in the right superior frontal (SFG) and middle frontal gyrus (MFG) (For details about the size of the clusters, please refer to Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For the execution component (conjunction between runs 3 and 5) and the visual target component (conjunction between runs 4 and 5), we found clusters in the bilateral SPL. These results indicate that the BOLD responses were greater during adaptation trials than baseline trials within the posterior parietal cortex (PPC) across all three visuomotor components and within the frontal cortex, specifically for the planning component.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConjunction analysis clusters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePeak (MNI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCluster size\u003c/p\u003e \u003cp\u003e(voxels)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eY\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDirectional\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExecution\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNone\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlanning\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNone\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTarget position\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR Cuneus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR Lingual Gyrus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdaptation vs. baseline\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExecution\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR Superior Parietal Lobule\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL Superior Parietal Lobule\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL Inferior Parietal Lobule\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlanning\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL Inferior Parietal Lobule\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eL Superior Parietal Lobule\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR Superior Parietal Lobule\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR Inferior Parietal Lobule\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR Superior Frontal Gyrus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR Inferior Frontal Gyrus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTarget position\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR Superior Parietal Lobule\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL Superior Parietal Lobule\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eShared\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR Superior Parietal Lobule\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL Superior Parietal Lobule\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: L, left; MNI, Montreal Neurological Institute; R, right.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor the directional contrast, our conjunction analysis identified two significant clusters: the upper and lower regions of the calcarine sulcus in the right visual cortex, which process information about visual targets in the left visual field (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). In our experiment, targets in runs 4 and 5 appear in opposite positions (upper and lower left). This finding is consistent with results from previous studies\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, which have demonstrated that different parts of the visual cortex are involved in processing specific portions of the visual field. However, we found no significant regions for the execution and planning components. No difference in overall BOLD response between CW and CCW rotations is consistent with our previous study\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e and motivated us to employ multivariate pattern analysis (MVPA) to identify visuomotor components reflected in spatial activity patterns.\u003c/p\u003e\n\u003ch3\u003eMultivariate pattern analysis (MVPA) with cross-classification\u003c/h3\u003e\n\u003cp\u003eWe first consider the classification of two conflicting visuomotor rotations, CW and CCW, to investigate how they are distinctively represented as BOLD activity patterns in the sensorimotor and cerebellar ROIs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Classification accuracy varied significantly across visuomotor components (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Although no significant difference in BOLD response between the two rotations was found for the execution component in the whole-brain univariate conjunction analysis, the MVPA revealed significant above-chance classification accuracy in the contralateral (i.e., left) M1/S1 (M1: 53.1%, p(unc)\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p(FWER)\u0026thinsp;\u0026lt;\u0026thinsp;0.003, S1: 54.2%, p(unc)\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p(FWER)\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Multiple sources of evidence\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e indicate that neurons in the M1 exhibit directional selectivity for movement direction, even in movements differing by up to 60\u0026deg; (CW 30\u0026deg; and CCW 30\u0026deg;). We also found that the mean decoding accuracy was significantly above the chance level in the left inferior parietal cortex (L IPL: 52.6%, p(unc)\u0026thinsp;=\u0026thinsp;0.001, p(FWER)\u0026thinsp;=\u0026thinsp;0.010), visual area (VIS: 53.0%, p(unc)\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p(FWER)\u0026thinsp;\u0026lt;\u0026thinsp;0.003) and right cerebellar lobule VI (CB6: 52.6%, p(unc)\u0026thinsp;\u0026lt;\u0026thinsp;0.002, p(FWER)\u0026thinsp;=\u0026thinsp;0.010). These results indicate directional selectivity for the execution component in the sensorimotor regions, cerebellum, and visual cortex. BOLD activity patterns for the execution of the reaching movement were not accurately decoded in the superior parietal cortex (L SPL: 51.7%, p(unc)\u0026thinsp;\u0026lt;\u0026thinsp;0.028, p(FWER)\u0026thinsp;=\u0026thinsp;0.133; R SPL: 51.4%, p(unc)\u0026thinsp;=\u0026thinsp;0.053; R IPL: 50.8%, p(unc)\u0026thinsp;=\u0026thinsp;0.180), the right cerebellar lobule VIII (CB8: 48.2%, p(unc)\u0026thinsp;=\u0026thinsp;0.980) or the supplementary motor area (SMA: 49.7%, p(unc)\u0026thinsp;=\u0026thinsp;0.612), suggesting a lack of selectivity for the execution of movement in these brain regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the visual target position component, the visual area exhibited the highest accuracy (VIS: 58.5%, p(unc)\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p(FWER)\u0026thinsp;\u0026lt;\u0026thinsp;0.001), demonstrating its selectivity for the target position. Other regions did not exhibit significant decoding accuracy for this component (M1: 50.3%, p(unc)\u0026thinsp;=\u0026thinsp;0.356; S1:50%, p(unc)\u0026thinsp;=\u0026thinsp;0.483; SMA: 50.2%, p(unc)\u0026thinsp;=\u0026thinsp;0.392; L SPL: 50.5%, p(unc)\u0026thinsp;=\u0026thinsp;0.307; R SPL: 50.8%, p(unc)\u0026thinsp;=\u0026thinsp;0.168; L IPL: 50.7%, p(unc)\u0026thinsp;=\u0026thinsp;0.212; IPL R: 50.1%, p(unc)\u0026thinsp;=\u0026thinsp;0.435; CB6: 51.4%, p(unc)\u0026thinsp;=\u0026thinsp;0.055; CB8: 50.2%, p(unc)\u0026thinsp;=\u0026thinsp;0.393; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eContrary to previous studies\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e that identified cerebellar involvement in discriminating opposing visuomotor rotations, we did not find significant above-chance accuracy for the planning component in the cerebellum or any other ROI. Specifically, the right cerebellar ROIs (CB6: 51.2%, p(unc)\u0026thinsp;=\u0026thinsp;0.086; CB8: 50.3%, p(unc)\u0026thinsp;=\u0026thinsp;0.389) did not discriminate between CW and CCW rotations. Moreover, no ROI showed any significant above-chance accuracy for the planning of movement, including motor areas (M1: 51.4%, p(unc)\u0026thinsp;=\u0026thinsp;0.051 SMA: 49.5%, p(unc)\u0026thinsp;=\u0026thinsp;0.728), somatosensory areas (S1: 48.3%, p(unc)\u0026thinsp;=\u0026thinsp;0.975; L SPL: 49.2%, p(unc)\u0026thinsp;=\u0026thinsp;0.836; R SPL: 48.5%, p(unc)\u0026thinsp;=\u0026thinsp;0.956; L IPL: 50.2%, p(unc)\u0026thinsp;=\u0026thinsp;0.383; R IPL: 50.7%, p(unc)\u0026thinsp;=\u0026thinsp;0.214), and visual area (VIS: 48.8%, p(unc)\u0026thinsp;=\u0026thinsp;0.921) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This is also particularly surprising for the posterior parietal cortex (PPC), as it was previously reported to play a significant role in visuomotor adaptation by storing the changes in sensorimotor mappings\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eClassification of adaptation vs. baseline conditions for the planning components\u003c/h3\u003e\n\u003cp\u003eThe adaptation vs. baseline contrast (Figure S1) compares visuomotor adaptation (CW and CCW rotations) with the baseline condition (0\u0026deg; rotation). This contrast investigates the neural substrates of learned sensorimotor mappings, independent of the rotational direction, focusing purely on the presence of adaptation rather than the specifics of opposing rotations. In this context, a key consideration is that, due to the nature of MVPA, which relies on voxel-by-voxel pattern analysis, grouping opposite rotations into the same class is counterintuitive for both the execution and visual target components. Therefore, we will focus exclusively on the planning component and disregard the execution and visual target components for this part of the analysis and the following searchlight analysis.\u003c/p\u003e \u003cp\u003eFor the planning component, neural activity patterns in the sensorimotor ROIs distinguished between trials with and without visuomotor rotations (Figure S1), including M1, S1, the bilateral posterior parietal cortex (PPC), the visual area, bilatereal SMA and the right cerebellar lobule VI (M1: 54.1%, p(unc)\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p(FWER)\u0026thinsp;\u0026lt;\u0026thinsp;0.001; S1: 54%, p(unc)\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p(FWER)\u0026thinsp;\u0026lt;\u0026thinsp;0.001; L IPL: 53.3%, p(unc)\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p(FWER)\u0026thinsp;\u0026lt;\u0026thinsp;0.001; R IPL: 52.3%, p(unc)\u0026thinsp;=\u0026thinsp;0.001, p(FWER)\u0026thinsp;\u0026lt;\u0026thinsp;0.002; L SPL: 52.5%, p(unc)\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p(FWER)\u0026thinsp;\u0026lt;\u0026thinsp;0.002; R SPL: 52.7%, p(unc)\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p(FWER)\u0026thinsp;\u0026lt;\u0026thinsp;0.001; CB6: 52.5%, p(unc)\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p(FWER)\u0026thinsp;\u0026lt;\u0026thinsp;0.002; SMA: 52.4%, p(unc)\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p(FWER)\u0026thinsp;\u0026lt;\u0026thinsp;0.002; VIS: 52.4%, p(unc)\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p(FWER)\u0026thinsp;\u0026lt;\u0026thinsp;0.002). Meanwhile, the right cerebellar lobule VIII did not show significant above-chance decoding accuracy (CB8: 50.9%, p(unc)\u0026thinsp;=\u0026thinsp;0.116). These results indicate that the response patterns in the sensorimotor ROIs tested exhibit selectivity for visuomotor rotations in this experiment, although they do not show selectivity between opposing visuomotor rotations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eMVPA searchlight\u003c/h3\u003e\n\u003cp\u003eWe conducted a whole-brain searchlight analysis\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e using a searchlight sphere with a 5-mm radius, which included 93 voxels, to investigate neural selectivity for visuomotor components. We applied cross-classification to runs that shared common visuomotor components, assessed decoding accuracy with AFNI\u0026rsquo;s 3dttest++, and corrected for multiple comparisons at a cluster level via the -Clustsim option\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Below, we report clusters that either overlap with our predefined ROIs or reveal additional task-relevant locations; the complete list of clusters for both contrasts is provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMVPA searchlight clusters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePeak (MNI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCluster level\u003c/p\u003e \u003cp\u003e(p, voxels)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ez\u003c/em\u003e value at\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eY\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003epeak\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDirectional\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExecution\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL Primary Sensory Cortex\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01 / 58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR Middle Temporal Gyrus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;001 / 57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR Superior Parietal Lobule\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.02 / 48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR Lingual Gyrus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.04 / 42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL Superior Parietal Lobule\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05 / 38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlanning\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNone\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTarget position\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBilateral Occipital Cortex\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 / 8920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdaptation vs. baseline\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlanning\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR Angular Gyrus\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eR Inferior Parietal Lobule\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eR Superior Parietal Lobule\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 / 1478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL Inferior Frontal Gyrus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 / 350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL Middle Occipital Cortex\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 / 225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR Superior Frontal Gyrus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 / 192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL Inferior Parietal Lobule\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 / 156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR Supramarginal Gyrus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01 / 84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL Superior Frontal Gyrus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01 / 83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR Superior Frontal Gyrus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01 / 80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR Middle Frontal Gyrus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01 / 62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR Inferior Frontal Gyrus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.03 / 47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR Middle Temporal Gyrus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.04 / 43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: L, left; MNI, Montreal Neurological Institute; R, right.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eSupplementary Figure\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBecause our ROI analysis was restricted to regions contralateral to the reaching hand, it did not probe the right hemisphere. The searchlight results for the directional contrast, therefore, complement the ROI-based findings. Similarly to the ROI-decoding analysis, the searchlight map in the left hemisphere highlights spatial selectivity in the S1 according to the direction of the reaching movement. At the same time, the visual cortex significantly decodes according to the position of the target. M1, however, showed no above-chance movement direction decoding. Additionally, the searchlight revealed a direction-selective cluster in the bilateral superior parietal lobule. For the adaptation vs. baseline (planning) contrast, searchlight decoding matched the ROI effects in the right SPL and bilateral IPL. It also detected additional clusters in the bilateral frontal gyrus and left occipital cortex.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHuman possess a remarkable ability to adapt to dynamic environments, supported by neural systems capable of processing complex visuomotor tasks. Paradigms such as prism adaptation\u003csup\u003e(23]\u003c/sup\u003e, force field learning\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, and visuomotor rotations\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e have been developed to investigate how human adapt to novel or altered environments. Through these paradigms, it has been shown that the brain builds internal models to predict the sensory consequences of motor actions\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. When a discrepancy arises between the predicted and actual movement outcomes, these internal models are updated through sensory prediction errors, with the cerebellum playing a critical role in this process\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. In this study, we introduce a novel paradigm to investigate how humans learn conflicting motor tasks through visuomotor adaptation. Our approach employs a design inspired by statistical cross-validation to dissociate the neural representations of motor execution, motor planning, and visual target positions. By systematically varying target locations and rotational mappings across fMRI runs, we leverage the overlapping task elements to identify brain regions involved in motor planning and execution and decode task-specific neural activity patterns using MVPA.\u003c/p\u003e \u003cp\u003eOur univariate conjunction analysis revealed that the visual target position was the only component showing differential BOLD activity between opposite visuomotor rotations. We identified two clusters, one located above and one below the calcarine sulcus, corresponding to the top-left and bottom-left visual fields, respectively. This functional segregation of the visual field was reported in prior studies\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e and aligns with the established role of the upper bank of the calcarine sulcus in processing information from the lower visual field and the lower bank of the calcarine sulcus in processing information from the upper visual field. Moreover, neural patterns in the visual ROI exhibited visual directional selectivity, depending on the target position on the screen, confirming distinct neural representations in the visual cortex for visual targets.\u003c/p\u003e \u003cp\u003eThere was no difference in the overall BOLD activity in the sensorimotor areas between opposite visuomotor rotations, as shown in the previous studies\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Considering the current experimental design, the only significant difference between the two adaptation tasks (CCW and CW) would be the opposite visuomotor rotations applied. Thus, it is less likely that motor execution in different directions involves segregated sensorimotor regions, such as the somatotopic homunculus map. Instead, sensorimotor regions may encode directional information at a finer level, which could be identified through the MVPA approach. Indeed, multiple studies show that the neurons in the motor cortex are tuned to a preferred direction of movement\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e and remain tuned to their preferred direction even after visuomotor adaptation\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Indeed, our MVPA analysis revealed BOLD activity patterns in sensorimotor regions, including M1/S1, the left IPL, and the ipsilateral cerebellar lobule VI, which exhibited directional selectivity for the direction of movement, even when the target to reach was at the same position.\u003c/p\u003e \u003cp\u003eInterestingly, we also found significant decoding accuracy for two opposing movement directions in the visual cortices (V1/V2, which corresponds to Brodmann areas 17 and 18). Considering that visual imagery activates early visual areas, such as V1 and V2\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e, participants may have been engaging in visual imagery, mentally simulating the movement of the cursor toward the targets. If this were the case, the activity in V1 and V2 could be attributed to imagined visual feedback from the movement rather than motor directional selectivity per se.\u003c/p\u003e \u003cp\u003eOur experimental design implies that we did not directly study visuomotor adaptation itself but rather the learned sensorimotor mappings that resulted from the adaptation process. From this, we found increased activity throughout the PPC in our univariate analysis for the motor planning component when comparing adaptation trials to baseline trials. This finding aligns with prior studies showing increased PPC activity during the late stages of visuomotor adaptation\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e, consistent with the timing of our experiment. Specifically, our analysis localized the motor planning component primarily within the PPC, emphasizing its critical role in storing the learned visuomotor mappings. In contrast, no significant activity was observed in the cerebellum, which is likely due to the participants' advanced stage of learning. The cerebellum is known to play a critical role in predicting sensory errors\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e, which is crucial during early stages of adaptation. However, since participants had already adapted to the visuomotor rotations by this stage of the experiment, the number of errors was minimal, thus reducing the cerebellum's overall activity. Tzvi et al. (2020)\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e found that activity in the right cerebellar lobule VI gradually decreased during adaptation to a visual rotation, which coincides with our results and the decrease in errors made by the participants (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNevertheless, we expected that different neural populations in sensorimotor regions would respond selectively to the planning of opposite visuomotor rotations. However, we found no regions with significant decoding accuracy for the planning component in the directional contrast. This null result contrasts with previous studies\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, which reported decoding in sensorimotor regions, including M1/S1, SMA, parietal regions, and the cerebellum. Notably, those studies demonstrated that movement kinematics (e.g., speed, mean, and error variance) could not account for the significant decoding results. However, unlike the current design, they did not fully counterbalance all three visuomotor components (target position, execution, and planning). One possible explanation for the inconsistent results is that decoding in previous studies may have partially reflected differences in hand movement direction, which our design aimed to minimize. Alternatively, relatively larger visuomotor rotations in the previous studies (Ogawa et al., 2013\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e: 90\u0026deg;; Kim et al., 2015\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e: 40\u0026deg; ) than those in the current study (30\u0026deg;) may involve more cognitive strategy in planning\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e, resulting in more distinct activation patterns in the sensorimotor ROIs. Lastly, the sensorimotor representation of motor planning may be dependent on the tool used in the experiment, as seen in a previous study with a joystick and wrist movement, compared to the current study with a tablet and arm movement. Future studies would warrant elucidating the effects of the rotation degree and different tools. Although the neural populations in the cerebellum, bilateral PPC, SMA, S1, and M1, did not distinguish between CW and CCW rotations for the motor planning, they could accurately distinguish between trials with applied visuomotor rotations and trials without them. These results highlight the crucial role of the PPC in storing sensorimotor mappings that result from adaptation in visuomotor tasks.\u003c/p\u003e \u003cp\u003eOur study presents notable limitations. First, we included only three runs for the data analysis, which constitutes a relatively small sample of trials for robust decoding analysis, especially in cross-validation frameworks. The limited number of trials may reduce the reliability of the decoding results and constrain the statistical power of the analyses. A more extensive design with additional runs and trials would be beneficial in addressing this limitation. Second, participants initiated their reaching movement as soon as the target appeared (without a preparatory interval), allowing the retrieval of the adaptation plan and the execution of the movement to overlap in time. Consequently, the BOLD signal observed during the tasks reflects a mixture between cognitive retrieval and motor execution, which could prevent a clean dissociation of planning and execution. Our design limits the complete isolation of a pure pre-movement planning. Future studies introducing an explicit preparation time using a Go / No-Go paradigm would separate planning from execution at the behavioral level\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. It would also have been interesting to combine both cross-classification with a Go/No-Go cue to obtain a within-run planning-only baseline.\u003c/p\u003e \u003cp\u003eIn summary, by separating planning and execution through our experimental design, we present new insights into the distributed neural representations underlying visuomotor adaptation. As expected, the primary visual cortex, the first cortical area to receive visual information, can effectively distinguish spatial target positions, reflecting its well-established retinotopic organization. We found that after learning to counteract the rotations, the parietal cortex showed increased neural activity, which likely implies that this region is central to motor memory storage following visuomotor adaptation. However, we found that the cerebellum and PPC (SPL and IPL) could not discriminate the neural patterns associated with opposite motor planning, but could with opposite motor execution. This result seems contradictory to our previous studies, raising open questions about how distinct parameters of visuomotor mapping are represented in the human brain.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eWe collected behavioral and fMRI data from 44 neurologically healthy participants (22 females; mean age, 24.6 years; range, 19\u0026ndash;34 years). All participants were assessed as right-handed by a modified version of the Edinburgh Handedness Inventory\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. All individuals had normal or corrected-to-normal vision and were unaware of the study's purpose. We excluded eleven participants: one due to dizziness, eight due to excessive in-scanner movement, and two due to technical problems. In total, 33 participants are included in the data analysis (18 females; mean age\u0026thinsp;=\u0026thinsp;24.5 years, range\u0026thinsp;=\u0026thinsp;19\u0026ndash;33 years). Participants provided written informed consent, approved by the Sungkyunkwan University Institutional Review Board, Suwon, Republic of Korea (IRB No. 2018-05-003-032) and were compensated monetarily. All the research methods were performed in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eExperimental design\u003c/h2\u003e \u003cp\u003eThe experiment consisted of five functional MR imaging (fMRI) runs, each lasting approximately 10 to 14 mins (runs 1\u0026ndash;2: 14 mins; run 3: 10 mins; run 4: 13 mins; run 5: 10 mins), with short breaks between runs. Inter-trial intervals (ITIs) were randomly selected from 4 to 14 s in 2-s increments and generated from an exponential distribution\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. The sequences of ITIs are different for all participants. Within each trial of the five runs, a white cursor was presented at the center of the screen, indicating both the initial cursor position and the fixation point. A target circle with a diameter of 1.3 cm then appeared on a dark background. Participants were instructed to move the cursor to the target using the pen on the tablet within 1.5 seconds (i.e., a maximum movement time of 1.5 seconds). The trial was considered a miss if they failed to reach the target within this time. To encourage faster responses, the cursor turned red if no movement was made within 800 ms. The trajectory of the cursor feedback was not provided during the movement but was presented after the maximum movement time. The cursor\u0026rsquo;s final position was shown at a distance of 9 cm from the center for 500 ms to indicate the angular error. The angular error was calculated as the difference between the cursor\u0026rsquo;s final direction and the target direction, both measured with respect to the center of the screen.\u003c/p\u003e \u003cp\u003eAccording to run- and trial-specific task designs, different types of manipulations were applied to the target position and cursor movement (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Within each trial, the target appeared in one of the following locations: (i) middle: 9 cm to the left of the center; (ii) high: 5.2 cm vertically above the middle position; or (iii) low: 5.2 cm vertically below the middle position. Meanwhile, one of the following types of manipulation was applied to the cursor movement: (i) baseline (0\u0026ordm;): no perturbation; (ii) +30\u0026ordm; rotational perturbation: mapping rotated counterclockwise by 30\u0026ordm;, requiring participants to make a movement tilted by +\u0026thinsp;30\u0026ordm; from the desired cursor direction (as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, \u0026ldquo;Run 1 \u0026amp; 2\u0026rdquo;, middle panel); or (iii) -30\u0026ordm; rotational perturbation: mapping rotated clockwise by 30\u0026ordm;, requiring participants to make a movement tilted by -30\u0026ordm; from the desired cursor direction (as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, \u0026ldquo;Run 1 \u0026amp; 2\u0026rdquo;, bottom panel).\u003c/p\u003e \u003cp\u003eIn each run, three different colored targets -red, green, and blue- were used to indicate three distinct run-specific tasks. Target colors were counterbalanced across participants to account for any confounding effects. The sequence of tasks T\u003csub\u003e2\u003c/sub\u003e and T\u003csub\u003e3\u003c/sub\u003e was also counterbalanced across runs and participants, with two possible task orders: one starting with T\u003csub\u003e2\u003c/sub\u003e and the other with T\u003csub\u003e3\u003c/sub\u003e. Participants performed 80 trials of the baseline task (T\u003csub\u003e1\u003c/sub\u003e) in the scanner to familiarize themselves with the experimental environment before fMRI scanning.\u003c/p\u003e \u003cp\u003eIn runs 1 and 2, participants reached targets located in the same position (middle) but with different rotational mappings applied (0\u0026ordm;, +\u0026thinsp;30\u0026ordm;, and \u0026minus;\u0026thinsp;30\u0026ordm;). Accordingly, the three distinct tasks in runs 1 and 2 were as follow: (i) baseline task T\u003csub\u003e1\u003c/sub\u003e (target: red circle; location: middle; rotation: 0\u0026ordm;; desired movement: horizontal); (ii) task T\u003csub\u003e2\u003c/sub\u003e (target: green circle; location: middle; rotation: -30\u0026ordm;; desired movement: tilted upward); (iii) task T\u003csub\u003e3\u003c/sub\u003e (target: blue circle; location: middle; rotation: +30\u0026ordm;; desired movement: tilted downward).\u003c/p\u003e \u003cp\u003eMoreover, runs 1 and 2 (each consisting of 144 trials) included three baseline blocks (B\u003csub\u003eC\u003c/sub\u003e, 12 trials each) that only contained T\u003csub\u003e1\u003c/sub\u003e and three pseudorandomly mixed blocks (B\u003csub\u003eM\u003c/sub\u003e, 36 trials each) that contained T\u003csub\u003e1\u003c/sub\u003e, T\u003csub\u003e2\u003c/sub\u003e, and T\u003csub\u003e3\u003c/sub\u003e. The baseline and mixed blocks alternated in a fixed order: B\u003csub\u003eC\u003c/sub\u003e-B\u003csub\u003eM\u003c/sub\u003e-B\u003csub\u003eC\u003c/sub\u003e-B\u003csub\u003eM\u003c/sub\u003e-B\u003csub\u003eC\u003c/sub\u003e-B\u003csub\u003eM\u003c/sub\u003e. The order of tasks T\u003csub\u003e1\u003c/sub\u003e, T\u003csub\u003e2\u003c/sub\u003e, and T\u003csub\u003e3\u003c/sub\u003e in the mixed blocks was pseudorandomized and identical for all participants. In each mixed block, the six possible sequences of the three tasks (e.g., T\u003csub\u003e1\u003c/sub\u003e-T\u003csub\u003e2\u003c/sub\u003e-T\u003csub\u003e3\u003c/sub\u003e and T\u003csub\u003e2\u003c/sub\u003e-T\u003csub\u003e3\u003c/sub\u003e-T\u003csub\u003e1\u003c/sub\u003e) appeared once in each half of the blocks. Additionally, to avoid consecutive presentations of the same task type, the last task of each sequence differed from the first task of the following sequence (so that, for instance, two sequences of task T\u003csub\u003e2\u003c/sub\u003e do not appear consecutively). These structures were identical for runs 1 and 2, except that the order of task types within the mixed blocks was counterbalanced.\u003c/p\u003e \u003cp\u003eWhile runs 1 to 2 were designed to help participants learn different rotational mappings, runs 3 to 5 were deliberately designed to dissociate the components of motor execution, motor planning, and visual information related to the target position.\u003c/p\u003e \u003cp\u003eRun 3 was designed to isolate motor execution and motor planning while keeping visual target information constant. By maintaining a fixed target position (middle) but altering rotational mappings, we created conditions where the required physical movement (Execution) and the decision-making processes (Planning) varied, but the visual input remained identical. In run 3 (108 trials), participants reached targets located in the same position (middle) but with different rotational mappings, similar to those in runs 1 and 2. Thus, run 3 also included the same tasks: T\u003csub\u003e1\u003c/sub\u003e, T\u003csub\u003e2\u003c/sub\u003e, and T\u003csub\u003e3\u003c/sub\u003e. However, run 3 consisted of single-task blocks (12 trials each) of T\u003csub\u003e1\u003c/sub\u003e, T\u003csub\u003e2\u003c/sub\u003e, and T\u003csub\u003e3\u003c/sub\u003e (notated as B\u003csub\u003e1\u003c/sub\u003e, B\u003csub\u003e2\u003c/sub\u003e, and B\u003csub\u003e3\u003c/sub\u003e, respectively), resulting in schedules such as B\u003csub\u003e3\u003c/sub\u003e-B\u003csub\u003e1\u003c/sub\u003e-B\u003csub\u003e2\u003c/sub\u003e-B\u003csub\u003e3\u003c/sub\u003e-B\u003csub\u003e2\u003c/sub\u003e-B\u003csub\u003e1\u003c/sub\u003e-B\u003csub\u003e3\u003c/sub\u003e-B\u003csub\u003e1\u003c/sub\u003e-B\u003csub\u003e2\u003c/sub\u003e. If participants successfully learned the tasks presented in runs 1 and 2, they would have accumulated task-related knowledge (e.g., information on different rotational mappings) to guide their motor planning process in run 3. We assumed that run 3 (illustrated as Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, uppermost rectangle) involved information about different types of motor execution (i.e., movements in different directions; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, \u0026ldquo;Execution\u0026rdquo;), as well as motor planning (i.e., distinct decision-making processes for targets with distinct rotational mappings; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, \u0026ldquo;Planning\u0026rdquo;).\u003c/p\u003e \u003cp\u003eRun 4 is designed to isolate motor planning and target position information while controlling for motor execution. By manipulating the target locations and rotational perturbations such that they offset each other, participants were required to plan distinct rotations for different visual targets, yet resulted in the identical physical hand movement (horizontal) across the different conditions (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. T\u003csub\u003e4\u003c/sub\u003e and T\u003csub\u003e5\u003c/sub\u003e). In the first part of run 4 (108 trials, with rotational mappings intact), participants reached targets at different positions and with varying rotational mappings. However, they made movements in the same direction (i.e., horizontal movement) for all targets, based on the following three tasks: (i) T\u003csub\u003e1\u003c/sub\u003e (target: red circle; location: middle; rotation: 0\u0026ordm;; desired movement: horizontal); (ii) T\u003csub\u003e4\u003c/sub\u003e (target: green circle; location: low; rotation: -30\u0026ordm;; desired movement: horizontal); (iii) T\u003csub\u003e5\u003c/sub\u003e (target: blue circle; location: high; rotation: +30\u0026ordm;; desired movement: horizontal). This part of run 4 consisted of nine alternating blocks of T\u003csub\u003e1\u003c/sub\u003e, T\u003csub\u003e4\u003c/sub\u003e, and T\u003csub\u003e5\u003c/sub\u003e. Although the tasks required distinct motor planning (based on knowledge about different rotational mappings), participants made similar movements for all task types. Thus, we assumed that run 4 (illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, middle rectangle) involved information regarding distinct motor planning (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, \u0026ldquo;Planning\u0026rdquo;) and distinct target position information (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, \u0026ldquo;Target position\u0026rdquo;).\u003c/p\u003e \u003cp\u003eThe second part of run 4 comprised two washout blocks (24 trials), in which all rotational perturbations were removed. Participants made direct movements toward the targets, performing the following tasks: (i) (target: red circle; location: middle; rotation: 0\u0026ordm;; desired movement: horizontal); (ii) (target: green circle; location: low; rotation: 0\u0026ordm; (turned off); desired movement: tilted downward); (iii) (target: blue circle; location: high; rotation: 0\u0026ordm; (turned off); desired movement: tilted upward)\u0026mdash;the washout block eliminates any aftereffects of rotational perturbations before run 5. Adaptation to visuomotor rotations typically results in strong aftereffects, that corresponds to strong deviations in movement trajectory opposite to the applied rotation, when the perturbation is suddenly removed (add ref). We wanted to ensure that participants could perform the tasks within run 5 accurately from the very first trial, avoiding the need for a \u0026ldquo;de-adaptation\u0026rdquo; process during run 5.\u003c/p\u003e \u003cp\u003eRun 5 was designed to isolate motor execution and target position in the absence of motor planning. With the visuomotor rotations removed, the task required varying physical movement directed at varying visual target locations, mimicking the kinematics of previous runs but without compensating for rotations (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. T\u003csub\u003e6\u003c/sub\u003e and T\u003csub\u003e7\u003c/sub\u003e). In run 5 (108 trials), all rotational perturbations were removed, and participants made direct movements toward the targets. The tasks were as follow: (i) T\u003csub\u003e1\u003c/sub\u003e (target: red circle; location: middle; rotation: 0\u0026ordm;; desired movement: horizontal); (ii) T\u003csub\u003e6\u003c/sub\u003e (target: green circle; location: high; rotation: 0\u0026ordm; (turned off); desired movement: tilted upward); (iii) T\u003csub\u003e7\u003c/sub\u003e (target: blue circle; location: low; rotation: 0\u0026ordm; (turned off); desired movement: tilted downward). The block schedule was B\u003csub\u003e1\u003c/sub\u003e-B\u003csub\u003e7\u003c/sub\u003e-B\u003csub\u003e6\u003c/sub\u003e-B\u003csub\u003e1\u003c/sub\u003e-B\u003csub\u003e6\u003c/sub\u003e-B\u003csub\u003e7\u003c/sub\u003e-B\u003csub\u003e1\u003c/sub\u003e-B\u003csub\u003e7\u003c/sub\u003e-B\u003csub\u003e6\u003c/sub\u003e, where B\u003csub\u003e6\u003c/sub\u003e and B\u003csub\u003e7\u003c/sub\u003e respectively denote a block of 12 T\u003csub\u003e6\u003c/sub\u003e and 12 T\u003csub\u003e7\u003c/sub\u003e trials. In run 5, motor movements similar to those in runs 1 through 3 (i.e., horizontal, upward, and downward movements) were expected, but in response to different target positions (middle, high, and low). We hypothesized that run 5 (illustrated as Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, lower rectangle) involves information regarding different types of motor execution (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, \u0026ldquo;Execution\u0026rdquo;), as well as different target position information (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, \u0026ldquo;Target position\u0026rdquo;).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMRI acquisition\u003c/h2\u003e \u003cp\u003efMRI data was acquired using a 3-T Siemens scanner with a 64-channel head coil. The participants were provided with earplugs to minimize noise from the fMRI and foam pads to reduce head motion. Echo planar imaging (EPI) sequence was used to acquire functional scans (repetition time (TR), 2000 ms; echo time (TE), 35 ms; flip angle (FA), 90\u0026ordm;; field of view (FOV), 200 \u0026times; 200 mm; matrix, 100 \u0026times; 100; axial slices, 72; and slice thickness, 2mm).\u003c/p\u003e \u003cp\u003eFor anatomical reference, a whole-brain T1-weighted anatomical scan with an MP-RAGE sequence (TR, 2300 ms; TE, 2.28 ms; FA, 8\u0026ordm;, FOV, 256 \u0026times; 256 mm; matrix, 256 \u0026times; 256; axial slices, 192; and slice thickness of 1mm) was acquired before run 3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003efMRI data preprocessing\u003c/h2\u003e \u003cp\u003eImaging data were preprocessed using AFNI software (Analysis of Functional NeuroImages, NIH, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://afni.nimh.nih.gov\u003c/span\u003e\u003cspan address=\"https://afni.nimh.nih.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e). All functional images were first corrected for slice-time acquisition and realigned to adjust for motion-related artifacts. Then, the realigned images were spatially registered to the anatomical data, which were later transformed into the Montreal Neurological Institute (MNI) template and resampled into 2-mm-cube voxels. All images were spatially smoothed using a Gaussian kernel with a full width at half-maximum of 4 x 4 x 4 mm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eROI definition\u003c/h2\u003e \u003cp\u003eTo define visuomotor-related Regions of Interest (ROIs), we first determined regions with important task activation throughout the brain using a whole-brain general linear model (GLM) analysis. To avoid double-dipping, we used the first two runs of each participant to compute the activation maps. For each run, the three task regressors are added to the design matrix along with six regressors of non-interest, which model the six head motion parameters and polynomials for fMRI signal drifts, resulting in two activation maps for each participant. Subsequently, we average the two activation maps for each participant and utilize AFNI's 3dttest\u0026thinsp;+\u0026thinsp;+\u0026thinsp;function\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e for second-level group analysis. Afterward, we identify highly activated regions from task-related activity by thresholding the activation maps at uncorrected p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. To further determine our ROIs, we extracted predefined ROIs from the Automated Anatomical Labeling (AAL) toolbox\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e and Brodmann atlas, included in MRIcro software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mricro.com\u003c/span\u003e\u003cspan address=\"https://www.mricro.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e These ROIs included the left precentral cortex (M1), left postcentral cortex (S1), bilateral supplementary motor area (SMA), right cerebellar lobule VI (CB6) and VIII (CB8), bilateral inferior parietal lobule (IPL), and bilateral superior parietal lobule (SPL). For the visual area (VIS), we used Broadman areas 17 and 18. Using the task-related activation map and the predefined ROIs, we then defined our visuomotor-related ROIs by combining them. This resulted in 10 ROIs that we used for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eWhole-brain univariate analysis\u003c/h2\u003e \u003cp\u003eAs our experiment is designed to separate the visuomotor components of motor movements, we perform GLM analysis for the third, fourth, and fifth runs separately using AFNI's 3dDeconvolve. After preprocessing the runs as described above, we perform a GLM with the three task regressors separately, along with regressors of non-interest, consisting of the six head motion parameters and polynomials for fMRI signal drifts.\u003c/p\u003e \u003cp\u003eTo examine the differences between visuomotor rotation tasks (CCW and CW rotations) and between adaptation tasks and the baseline task, we define two contrasts for all subsequent analyses. The first contrast is the directional contrast, which examines the difference between the two visuomotor rotation tasks by contrasting CCW and CW rotations. This contrast allows us to assess whether there are directional differences in motor movement when participants adapt to opposing visuomotor rotations. The second contrast is the adaptation vs. baseline contrast, which compares the two visuomotor rotation tasks (CCW and CW) with the baseline task (0\u0026deg; rotation). This contrast helps us examine the impact of visuomotor rotation on motor movement by determining how the presence of a rotational perturbation affects motor execution, planning, and visual information processing compared to a task without rotation. For runs 3 and 4, the visuomotor rotation tasks are defined as the actual rotation (CCW and CW), while the baseline task is defined as the task without rotation (0\u0026deg;). However, for run 5, where no rotations were applied, we define the contrasts based on the target positions on the screen. The upper and lower target positions correspond to the adaptation tasks, while the middle target position represents the baseline task (see the experimental design for more details).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eConjunction analysis\u003c/h2\u003e \u003cp\u003eThe conjunction analysis uses the statistical maps generated from the whole-brain univariate analysis to discriminate the neural components involved in the execution, planning, and visual information processing of the motor movement. This approach enables us to identify brain regions where multiple conditions, tasks, or contrasts exhibit overlapping significant activation. Given that participants successfully adapted to the visuomotor rotations, we hypothesize that a conjunction analysis can effectively separate these three visuomotor components. For the execution component, we analyze the statistical map derived from runs 3 and 5. The planning component utilizes statistical maps from runs 3 and 4, while the visual information processing components utilize runs 4 and 5. Statistical maps are obtained by applying a threshold at an uncorrected voxel-level p-value of 0.001 (two-sided) and a false positive rate (FPR) for multiple comparisons on a cluster level at a threshold of α\u0026thinsp;=\u0026thinsp;0.05. These thresholds are established using AFNI's 3dttest\u0026thinsp;+\u0026thinsp;+\u0026thinsp;function with the Clustsim option\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e to control for multiple comparisons. Subsequently, for each contrast (directional and adaptation vs. baseline), we compare the statistically significant clusters of voxels identified for each visuomotor component, allowing us to reveal overlapping activations across components.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMVPA ROI analysis\u003c/h2\u003e \u003cp\u003eTo determine whether sensorimotor regions encode distinct visuomotor features, we employed cross-classification, a multivariate approach analogous to conjunction analysis\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Our design (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) manipulated three components per trial: execution (movement direction: up or 30\u0026deg;, middle or 0\u0026deg;, low or \u0026minus;\u0026thinsp;30\u0026deg;), planning (visuomotor rotation: CW 30\u0026deg;, baseline 0\u0026deg;, CCW 30\u0026deg;), and visual target position (upper, middle, lower visual field). Each run contained two of these components, allowing us to train a classifier on one run and test it on another that shared the component of interest. For example, runs 3 and 5 both required identical reaching movement directions, so cross-classifying 3 and 5 isolates execution-specific coding. Analogous pairings also isolate planning or visual-target component-specific coding. Above-chance performance identifies ROIs that are selective for the component of interest. This approach allows us to dissociate the representation of each visuomotor component.\u003c/p\u003e \u003cp\u003eWe applied multivoxel pattern analysis (MVPA), a multivariate approach designed to detect differences in activation patterns across multiple voxels\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, to compute decoding accuracies within the ROIs. To estimate response amplitudes, we employed the least squares sum method, which minimizes collinearity between adjacent trials\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. For each voxel, the model included one regressor for the current trial, three regressors for all other trials split by task type, six head-motion parameters, and polynomial drift terms. Missed trials are excluded from the analysis (see behavioral analysis for details). We converted β estimates to \u003cem\u003et\u003c/em\u003e-values to down-weight high-variance voxels\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. Our analysis focused on whether the directional and adaptation vs. baseline contrasts could be discriminated within the defined ROIs.\u003c/p\u003e \u003cp\u003eFor classification, we used a linear support vector machine classifier (SVC) implemented in scikit-learn\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e with default parameters (version 1.6.1, regularization parameter C\u0026thinsp;=\u0026thinsp;1). The experimental design provided a natural two-fold cross-validation: train on run A and test on run B, and vice versa. Because for both contrasts, the number of trials in the training set was unbalanced, class weights were set inversely to class frequency to prevent bias towards the overrepresented class.\u003c/p\u003e \u003cp\u003eTo assess statistical significance, we used a permutation test instead of relying on the theoretical chance level of 50%. For each ROI \u0026times; component \u0026times; contrast, we generated participant-specific null distributions by permuting class labels 2 000 times. We then bootstrapped 10\u003csup\u003e5\u003c/sup\u003e group-level samples by resampling (with replacement) a single chance decoding accuracy per participant (N\u0026thinsp;=\u0026thinsp;33) and averaged it to obtain a mean chance decoding accuracy. The mean decoding accuracy for each ROI was considered significant if it exceeded the 97.5th percentile of this distribution (two-tailed α\u0026thinsp;=\u0026thinsp;0.05). To correct multiple comparisons across ROIs, we controlled the family-wise error rate (FWER) using the Holm-Bonferroni correction\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eMVPA searchlight analysis\u003c/h2\u003e \u003cp\u003eIn addition to the ROI analysis, we used a searchlight analysis\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e to compute decoding accuracy across the entire brain. We used \u003cem\u003et\u003c/em\u003e-values, as we did for the MVPA ROI, with a radius of 5 mm (93 voxels) and a linear support vector machine classifier (SVC) using the default parameters implemented in scikit-learn (version 1.6.1). After computing decoding accuracy for each visuomotor component at each searchlight within the brain volume, we use AFNI\u0026rsquo;s function 3dttest++\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e to determine whether each voxel exhibits above-chance decoding accuracy. For statistical significance, we use uncorrected p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 with the Clustsim option to account for FPR in multiple comparisons at a cluster level, with a threshold of α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eBehavioral measure\u003c/h2\u003e \u003cp\u003eWe obtained the hand direction degree for each trial to ensure that the participants were learning the different visuomotor mappings. The measurements were determined as the angle between the first point and the endpoint of the trajectories drawn by the tablet for each trial. Missed trials that did not reach the target within the maximum movement time were excluded from the analysis. Because the rotated mappings of T\u003csub\u003e2\u003c/sub\u003e and T\u003csub\u003e3\u003c/sub\u003e were presented in the opposite direction according to the counterbalanced group, the measurements of the second group participants were plotted as negative values when the average hand direction degree was visualized (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). For statistical analysis, we conducted one-sample t-tests comparing each participant\u0026rsquo;s mean movement angle to the angle corresponding to the center of the target (30\u0026deg;, 0\u0026deg;, or \u0026minus;\u0026thinsp;30\u0026deg;, depending on the condition).\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding Declaration\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the National Research Foundation of Korea (NRF-2021R1A2C2011648, RS-2024-00356694), Hanyang University and Korea Basic Science Institute (KBSI) (HY-202400000003862), Center for Neuroscience Imaging Research, Institute for Basic Science, Korea (IBS-R015-D1), Yangyoung Foundation.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eS.K. conceived the idea presented in the study and designed the experiment. S.K. carried out the experiment. A.C. performed formal analysis; A.C., and S.K. wrote the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNeuroimaging was performed at the Center for Neuroscience Imaging Research located at Sungkyunkwan University, supported by the Institute for Basic Science.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eAll MRI data used in this study were archived in the Hanyang University Network Attached Storage (NAS). All the dataset and codes related to this study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWolpert, D. M. \u0026amp; Kawato, M. Multiple paired forward and inverse models for motor control. \u003cem\u003eNeural Netw.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (7\u0026ndash;8), 1317\u0026ndash;1329 (1998).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImamizu, H. et al. Modular organization of internal models of tools in the human cerebellum. \u003cem\u003eProc. Natl. Acad. Sci. U S A\u003c/em\u003e. \u003cb\u003e100\u003c/b\u003e (9), 5461\u0026ndash;5466 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImamizu, H. et al. Functional magnetic resonance imaging examination of two modular architectures for switching multiple internal models. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e (5), 1173\u0026ndash;1181 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirashima, M. \u0026amp; Nozaki, D. Distinct motor plans form and retrieve distinct motor memories for physically identical movements. \u003cem\u003eCurr. Biol.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e (5), 432\u0026ndash;436 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrasovsky, A. et al. Differentiating intended sensory outcome from underlying motor actions in the human brain. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cb\u003e34\u003c/b\u003e (46), 15446\u0026ndash;15454 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsu, R. et al. Random presentation enables subjects to adapt to two opposing forces on the hand. \u003cem\u003eNat. Neurosci.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e (2), 111\u0026ndash;112 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrakauer, J. W. et al. Generalization of motor learning depends on the history of prior action. \u003cem\u003ePLoS Biol.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e (10), E316 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, J. Y. \u0026amp; Schweighofer, N. Dual adaptation supports a parallel architecture of motor memory. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e (33), 10396\u0026ndash;10404 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoolley, D. G. et al. Dual adaptation to two opposing visuomotor rotations when each is associated with different regions of workspace. \u003cem\u003eExp. Brain Res.\u003c/em\u003e \u003cb\u003e179\u003c/b\u003e (2), 155\u0026ndash;165 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgawa, K. \u0026amp; Imamizu, H. Human sensorimotor cortex represents conflicting visuomotor mappings. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cb\u003e33\u003c/b\u003e (15), 6412\u0026ndash;6422 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, S. et al. Neural Substrates Related to Motor Memory with Multiple Timescales in Sensorimotor Adaptation. \u003cem\u003ePLoS Biol.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (12), E1002312 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheahan, H. R., Franklin, D. W. \u0026amp; Wolpert, D. M. Motor Planning, Not Execution. \u003cem\u003eSeparates Motor Memories Neuron\u003c/em\u003e. \u003cb\u003e92\u003c/b\u003e (4), 773\u0026ndash;779 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, K. et al. Sensory prediction errors, not performance errors, update memories in visuomotor adaptation. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e (1), 16483 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchneider, W., Noll, D. \u0026amp; Cohen, J. Functional topographic mapping of the cortical ribbon in human vision with conventional MRI scanners. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e365\u003c/b\u003e, 150\u0026ndash;153 (1993).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSereno, M. I. et al. Borders of Multiple Visual Areas in Humans Revealed by Functional Magnetic Resonance Imaging. \u003cem\u003eScience\u003c/em\u003e \u003cb\u003e268\u003c/b\u003e, 889\u0026ndash;893 (1995).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeYoe, E. A. et al. Mapping striate and extrastriate visual areas in Human cerebral cortex. Proc. Natl. Acad. Sci. U.S.A. 93 (6) 2382\u0026ndash;2386 (1996).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEisenberg, M., Shmuelof, L., Vaadia, E. \u0026amp; Zohary, E. Functional organization of human motor cortex: directional selectivity for movement. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e, 8897\u0026ndash;8905 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFabbri, S., Caramazza, A. \u0026amp; Lingnau, A. Tuning curves for movement direction in the human visuomotor system. \u003cem\u003eJ. Neurosci. Oct.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e (40), 13488\u0026ndash;13498 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaar, S., Donchin, O. \u0026amp; Dinstein, I. Dissociating visual and motor directional selectivity using visuomotor adaptation. J Neurosci. ;35:6813\u0026ndash;21, 2015. (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanaka, H., Sejnowski, T. J. \u0026amp; Krakauer, J. W. Adaptation to Visuomotor Rotation Through Interaction Between Posterior Parietal and Motor Cortical Areas. \u003cem\u003eJ. Neurophysiol.\u003c/em\u003e \u003cb\u003e102\u003c/b\u003e (5), 2921\u0026ndash;2932 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKriegeskorte, N., Goebel, R. \u0026amp; Bandettini, P. A. Information-based functional brain mapping. Proc. Natl. Acad. Sci. U.S.A. 103 (10) 3863\u0026ndash;3868, (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCox, R. W., Chen, G., Glen, D. R., Reynolds, R. C. \u0026amp; Taylor, P. A. fMRI clustering and false-positive rates. \u003cem\u003eProc. Natl. Acad. Sci. USA\u003c/em\u003e. \u003cb\u003e114\u003c/b\u003e (17), E3370\u0026ndash;E3371 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVon Helmholtz, H. Handbuch der Physiologischen Optik. Leipzig, Germany: Voss, 1867 .\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShadmehr, R. \u0026amp; Mussa-Ivaldi, F. A. Adaptive representation of dynamics during learning of a motor task. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 3208\u0026ndash;3224 (1994).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrakauer, J. W., Pine, Z. M., Ghilardi, M-F. \u0026amp; Ghez, C. Learning of visuomotor transformations for vectorial planning of reaching trajectories. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, 8916\u0026ndash;8924 (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaylor, J. A., Klemfuss, N. M. \u0026amp; Ivry, R. B. An explicit strategy prevails when the cerebellum fails to compute movement errors. \u003cem\u003eCerebellum\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 580\u0026ndash;586 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTzvi, E., Koeth, F., Karabanov, A. N., Siebner, H. R. \u0026amp; Kr\u0026auml;mer, U. M. Cerebellar \u0026ndash; premotor cortex interactions underlying visuomotor adaptation. \u003cem\u003eNeuroimage\u003c/em\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTruccolo, W., Friehs, G. M., Donoghue, J. P. \u0026amp; Hochberg, L. R. Primary motor cortex tuning to intended movement kinematics in humans with tetraplegia. J Neurosci. ;28:1163\u0026ndash;1178, 2008. (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKosslyn, S. M. et al. Visual mental imagery activates topographically organized visual cortex: PET investigations. \u003cem\u003eJ. Cogn. Neurosci.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e (3), 332\u0026ndash;345 (1993).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlotnick, S. D., Thompson, W. L. \u0026amp; Kosslyn, S. M. Visual mental imagery induces retinotopically organized activation of early visual areas. \u003cem\u003eCereb. Cortex\u003c/em\u003e. \u003cb\u003e15\u003c/b\u003e (10), 1570\u0026ndash;1583 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlbers, A. M., Kok, P., Toni, I., Dijkerman, H. C. \u0026amp; de Lange, F. P. Shared representations for working memory and mental imagery in early visual cortex. \u003cem\u003eCurr. Biol.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e (15), 1427\u0026ndash;1431 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDella-Maggiore, V., Malfait, N., Ostry, D. J. \u0026amp; Paus, T. Stimulation of the posterior parietal cortex interferes with arm trajectory adjustments during the learning of new dynamics. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, 9971\u0026ndash;9976 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrakauer, J. W. et al. Differential cortical and subcortical activations in learning rotations and gains for reaching: A PET study. \u003cem\u003eJ. Neurophysiol.\u003c/em\u003e \u003cb\u003e91\u003c/b\u003e, 924\u0026ndash;933 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlakemore, S. J., Frith, C. D. \u0026amp; Wolpert, D. M. The cerebellum is involved in predicting the sensory consequences of action. \u003cem\u003eNeuroReport\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (19), 1879\u0026ndash;1884 (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTseng, Y. W., Diedrichsen, J., Krakauer, J. W., Shadmehr, R. \u0026amp; Bastian, A. J. Sensory prediction errors drive cerebellum-dependent adaptation of reaching. \u003cem\u003eJ. Neurophysiol.\u003c/em\u003e \u003cb\u003e98\u003c/b\u003e, 54\u0026ndash;62 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchlerf, J., Ivry, R. B. \u0026amp; Diedrichsen, J. Encoding of sensory prediction errors in the human cerebellum. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e, 4913\u0026ndash;4922 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorehead, J. R., Taylor, J. A., Parvin, D. E. \u0026amp; Ivry, R. B. Characteristics of implicit sensorimotor adaptation revealed by task-irrelevant clamped feedback. \u003cem\u003eJ. Cogn. Neurosci.\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e, 1061\u0026ndash;1074 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTzvi, E., Loens, S. \u0026amp; Donchin, O. Mini-review: the role of the cerebellum in visuomotor adaptation. \u003cem\u003eCerebellum\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e (2), 306\u0026ndash;313 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaylor, J. A., Krakauer, J. W. \u0026amp; Ivry, R. B. Explicit Explicit and Implicit Contributions to Learning in a Sensorimotor Adaptation Task. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cb\u003e34\u003c/b\u003e (8), 3023\u0026ndash;3032 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAriani, G., Pruszynski, J. A. \u0026amp; Diedrichsen, J. Motor planning brings human primary somatosensory cortex into action-specific preparatory states. \u003cem\u003eelife\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, e69517 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOldfield, R. C. The assessment and analysis of handedness: the Edinburgh inventory. \u003cem\u003eNeuropsychologia\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e (1), 97\u0026ndash;113 (1971).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurock, M. A. et al. Randomized event-related experimental designs allow for extremely rapid presentation rates using functional MRI. \u003cem\u003eNeuroreport\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e (16), 3735\u0026ndash;3739 (1998).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCox, R. W. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. \u003cem\u003eComput. Biomed. Res.\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e (3), 162\u0026ndash;173 (1996).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCox, R. W. \u0026amp; Hyde, J. S. Software tools for analysis and visualization of FMRI Data. \u003cem\u003eNMR Biomed.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 171\u0026ndash;178 (1997).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTzourio-Mazoyer, N. et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. ;15(1):273\u0026thinsp;\u0026ndash;\u0026thinsp;89. (2002). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1006/nimg.2001.0978\u003c/span\u003e\u003cspan address=\"10.1006/nimg.2001.0978\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 11771995.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMumford, J. A., Turner, B. O., Ashby, F. G. \u0026amp; Poldrack, R. A. Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMisaki, M., Kim, Y., Bandettini, P. A. \u0026amp; Kriegeskorte, N. Comparison of multivariate classifier and response normalizations for pattern-information fMRI. NeuroImage, Volume 53, Issue 1, Pages 103\u0026ndash;118.Neuroimage. 2012;59(3):2636-43. (2010). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2011.08.076\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2011.08.076\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2011 Sep 5. PMID: 21924359; PMCID: PMC3251697.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePedregosa et al. Scikit-learn: Machine Learning in Python. JMLR 12, pp. 2825\u0026ndash;2830, (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolm, S. \u0026amp; Simple Sequentially, A. Rejective Multiple Test Procedure. \u003cem\u003eScand. J. Stat.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e (2), 65\u0026ndash;70 (1979).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8838307/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8838307/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrevious behavioral studies have shown that human can adapt to multiple sensorimotor mappings simultaneously. Motor planning, rather than motor execution, has been shown to play a crucial role in distinguishing between multiple interfering motor tasks and in forming motor memory. However, it remains unclear how the human brain represents distinct motor planning and execution for multiple sensorimotor mappings. To address this, we designed an fMRI experiment specifically to dissociate motor planning from motor execution during multiple sensorimotor mappings learning. Critically, the design allowed us to compare conditions where participants prepared for different visuomotor rotations but executed identical movements, isolating the neural representation of motor planning. We found that the posterior parietal cortex (PPC), including the superior and inferior parietal lobules, exhibited increased activity during the planning. However, using Multivoxel pattern analysis (MVPA) to look at the representation, we found a dissociation between motor execution and planning. While motor execution could be reliably decoded in the sensorimotor cortex, PPC, and cerebellum, planning-related activity for opposite rotations was not decodable in any region of interest. These results suggest that while the PPC is actively recruited for motor planning, the specific neural patterns differentiating conflicting plans may be less distinct than those for execution.\u003c/p\u003e","manuscriptTitle":"Dissociated neural substrates of motor execution and planning in learning multiple sensorimotor mappings","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 17:04:00","doi":"10.21203/rs.3.rs-8838307/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"64b80365-f4e5-4b73-8c4b-e7137b83b16b","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64906407,"name":"Biological sciences/Neuroscience"},{"id":64906408,"name":"Biological sciences/Psychology"},{"id":64906409,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-04-20T10:27:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 17:04:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8838307","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8838307","identity":"rs-8838307","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}