(C) PLOS One
This story was originally published by PLOS One and is unaltered.
 . . . . . . . . . .



Frontal and parietal planning signals encode adapted motor commands when learning to control a brain–computer interface [1]

['Enrico Ferrea', 'German Primate Center', 'Göttingen', 'Pierre Morel', 'Ulr De Recherche Pluridisciplinaire Sport Santé Société', 'Université Du Littoral Côte D Opale', 'Université Artois', 'Université Lille', 'Calais', 'Alexander Gail']

Date: 2025-10 

Perturbing visual feedback is a powerful tool for studying visuomotor adaptation. However, unperturbed proprioceptive signals in common paradigms inherently co-varies with physical movements and causes incongruency with the visual input. This can create challenges when interpreting underlying neurophysiological mechanisms. We employed a brain–computer interface (BCI) in rhesus monkeys to investigate spatial encoding in frontal and parietal areas during a 3D visuomotor rotation task where only visual feedback was movement-contingent. We found that both brain regions better reflected the adapted motor commands than the perturbed visual feedback during movement preparation and execution. This adaptive response was observed in both local and remote neurons, even when they did not directly contribute to the BCI input signals. The transfer of adaptive changes in planning activity to corresponding movement corrections was stronger in the frontal than in the parietal cortex. Our results suggest an integrated large-scale visuomotor adaptation mechanism in a motor-reference frame spanning across frontoparietal cortices.

In this study, we conducted a 3D visuomotor rotation task under BCI control to investigate neural adaptation across the distributed frontoparietal network, including M1, PMd, and PRR. We selectively connected only a subset of units, referred to as controlling units, to the decoder to assess whether neural signatures of adaptation extend beyond the units immediately controlling the BCI movements. After demonstrating visuomotor rotation adaptation in BCI control of 3D movements and its compatibility with the idea of fast within-manifold learning, we address our three main research questions. First, we test if adaptation is distributed and also affects the coding in parietal areas, remote from the frontal lobe areas that controlled the BCI movements. Second, we determine the spatial frame of reference in which the noncontrolling units co-vary with the controlling units that determine the behavior. Third, we test the hypothesis that re-association learning leads to adaptation of movement planning signals consistent with the observed adaptation in motor behavior.

Over the course of fast adaptation, the idea, neural dynamics are initialized with changing conditions to compensate for movement errors from trial to trial, while the network structure in the motor cortex remains stable. A BCI learning study in monkeys indicated that such adaptive state initialization probably applies to BCI controlling units in the motor cortex [ 23 ]. In line with this finding, other work suggested that motor learning involves systematic changes in preparatory activity within the motor cortex to adapt to a force field [ 66 ]. It is yet unclear to what extent such adaptation of initial states also applies to changes in preparatory activity in the larger frontoparietal network, including noncontrolling units and areas outside the motor cortex. By introducing an instructed delay in the BCI movement task, we can compare adaptation of the preparatory states with the movement-associated dynamics. We ask whether such a relationship between the two states exists also for visuomotor adaptation and whether the relationship changes over the course of learning.

Third, we propose that motor adaptation is reflected in changes in motor signals during movement preparation already and that planning activities co-vary with signals during movement control. Fast adaptation at the neural level has been previously described from a dynamical system perspective [ 22 , 23 , 56 – 58 ]. According to this view, the neural state, which represents the activity pattern across the population of neurons in motor areas, follows established dynamics to generate movements [ 59 – 63 ]. During movement preparation, the neural state is initialized with different conditions for different movements and then evolves based on the network-inherent dynamics during movement execution. This concept is particularly applicable to ballistic reaches toward spatially segregated targets in space. The fixed neural dynamics are determined by the stable recurrent connectivity of the network itself [ 19 , 61 , 64 ]. According to this view, adaptation could be implemented by updating of the initial states. Hence, adaptation induced neural changes should reflect in late planning activity, at a time when uncertainty about the position of a BCI-controlled cursor position deteriorates planning signals [ 65 ].

During a visual working memory task, frontoparietal areas can even contain spatial information not related to the body, but relative to a visual object, which is an allocentric form of spatial representation [ 46 ]. The possibility of such spatial coding in a reach context, which does not reflect the planned physical arm movement, raises the question in which spatial frame of reference frontoparietal cortex represents reach-associated information during BCI learning and whether parietal and frontal areas share a common frame of reference. Rather than reflecting trial-to-trial updates of the corrected movement to produce adapted motor outputs, as observed in the motor and premotor cortices [ 11 , 28 , 47 , 48 ], parietal dynamics could reflect the unchanged sensory state of the system during the planning period before movement onset, as part of its role in state estimation [ 49 – 55 ].

Second, we ask whether premotor and parietal brain regions not only both adapt but do so in the same spatial frame of reference. Specifically, we test if neural changes reflect the adapted motor command (this is mandatory for the controlling units, but unclear for noncontrolling units) or if the parietal cortex rather reflects the associated adapted sensory feedback. Preparatory activity in frontoparietal motor planning areas includes information about the reach goal represented in different body-related frames of reference. While encoding at the level of neural populations and often also at the level of the individual neurons shows typically mixed representations of different reference frames, different coding schemes predominate across areas. For example, the parietal reach region (PRR) in the medial intraparietal area (MIP) predominantly encodes reach targets in an eye-centered reference frame [ 34 ], whereas area 5d in the posterior parietal cortex represents reach goals more strongly in a hand-centered frame [ 35 ]. PRR representations can relate both to the visual location and the physical goal of the movement [ 36 , 37 ]. Nearby sensorimotor areas V6A in the anterior wall of the parieto-occipital sulcus and PEc at its shoulder show gradually more selectivity for the hand position, additionally to target and gaze-related information [ 38 – 40 ]. Similarly, the PMd encodes reach targets in a mixed reference frame, integrating the relative positions of the hand, eye, and target [ 41 , 42 ]. Thus, while both premotor and parietal regions are involved in reach planning and show overlapping tuning properties during instructed delay, they differ with respect to their neuroanatomical embedding. Posterior parietal networks integrate sensory information from multiple modalities, including visual input, and contain motor-goal information during reach planning without directly driving motor output [ 43 , 44 ]. Premotor cortex, on the other hand, has direct connections to primary motor cortex (M1) and its more caudal parts even to spinal cord [ 45 ]. Here, we test if these differences between frontal and parietal motor planning areas reflect in differences in the predominant frame of reference when motor command and sensory feedback are dissociated during sensorimotor adaptation.

First, we will address the question of whether changes in neural activity during BCI learning occur consistently not only in nearby noncontrolling neurons, as shown previously [ 13 , 18 , 28 ], but also across a larger-scale sensorimotor network, including frontoparietal circuits. Previous studies in humans and monkeys have not extensively examined the extent of adaptation within a distributed neural network, including regions that are not directly involved in generating or updating motor commands but may still play a crucial role in sensorimotor integration. These regions, such as the parietal cortex, are expected to contribute to motor learning [ 29 , 30 ]. In a human BCI motor learning study involving the prefrontal cortex, dorsal premotor cortex (PMd), primary motor and sensory cortices, and the posterior parietal cortex, neural changes occurred with long-term learning and were associated nonspecifically with reduced cognitive demand [ 31 ]. In contrast, we aim to understand the neural characteristics of fast adaptation, which is particularly desirable for BCI learning and, in conventional visuomotor rotation learning, is often associated with the updating of an internal model [ 32 , 33 ]. Fast adaptation is especially important for BCI applications because it enables users to achieve accurate and efficient control with minimal training time, which is critical for practical and clinical use. We compare the contributions of noncontrolling neurons residing locally next to the BCI controlling neurons in the frontal areas or remotely in parietal areas to test if fast adaptation effects generalize to remote brain regions.

A way how the decoder input could be adapted without changing the network structure is by means of “re-association”. According to this view, a specific neural activity pattern associated with compensating for the introduced visuomotor perturbation is recruited from the range of patterns available within the existing network structure [ 15 , 18 – 21 ]. This view implies changes in activity, but under the constraint of an unchanged network structure also for neighboring neurons, such that also noncontrolling units in the controlling area that do not provide input to the decoder adapt their activity in a manner consistent with changes in the BCI controlling units. Based on these previous findings, we expect that the comparatively fast adaptation to visuomotor rotation, achievable within a single session of a few hundred trials, is accompanied by such re-association learning. After establishing that this is indeed the case in our visuomotor adaptation task, we will test three implications of this hypothesis.

In BCI learning, behavioral performance can be improved by exploiting an existing repertoire of neural dynamic states (“within-manifold” learning) or by reconfiguring the neural network structure (“outside-manifold” learning). In response to perturbations of a BCI decoder, short-term learning does not appear to significantly affect the correlation patterns among neurons in the motor cortex [ 15 , 20 , 24 ]. This suggests that at least the local network structure supplying input to the decoder (i.e., the controlling motor area) remains largely unchanged and constrains neural adaptation during short-term learning. However, with longer-term learning, the brain can be trained to generate new activity patterns in the controlling units that deviate from the expected correlation pattern of the network. Such outside-manifold learning aligns with the view that the tuning of individual neurons can be adapted independently during BCI learning [ 13 , 24 – 25 ].

In our study, we employ a BCI visuomotor rotation paradigm in rhesus monkeys to explore the role of visual feedback and the contributions of parietal and premotor sensorimotor areas to BCI adaptation. We ask whether both brain regions (i) contribute to adaptation irrespective of an immediate impact on the produced motor behavior, (ii) reflect the same or different sensory or motor-related spatial dimensions of fast visuomotor adaptation, and (iii) show adaptation of motor planning activity consistent with motor behavior.

Intracortical brain–computer interfaces (BCIs) have shown success in restoring lost motor functions in individuals with tetraplegia and chronic stroke, primarily by extracting signals from the motor cortex [ 1 – 6 ]. Recent advancements have extended this approach to signals obtained from the parietal cortex [ 7 – 10 ]. Besides their translational applications, BCI paradigms provide a crucial tool for studying the neurophysiological basis of motor learning [ 11 – 23 ]. Unlike conventional motor learning paradigms involving physical limb movement, BCI control establishes an experimentally controllable mapping from brain activity to effector motion through the neural decoder. Additionally, BCI paradigms help isolate the impact of visual feedback on movement adaptation by keeping proprioceptive feedback constant during visuomotor perturbations [ 16 ]. This approach reduces potential confounds inherent in the adaptation of physical movements, which co-vary with proprioceptive feedback, and emphasizes the visual aspect of feedback control.

Results

Monkeys learned to control movements in 3D virtual reality with BCI To better understand the neural mechanisms underlying movement planning and execution, we designed an experiment to investigate adaptive changes in neural dynamics during a memory-guided reach task under BCI control. Two rhesus monkeys (Y and Z) performed a memory-guided 3D center-out reach task using a computer cursor in a virtual reality (VR) environment (Figs 1A and S1A). The task involved the presentation of a target, followed by a memory period during which the target was no longer visible. The monkeys were then required to move the cursor to the previously cued target in a corner of a 3D cube (Fig 1B). This delay period between target presentation and movement execution allowed us to investigate adaptive changes in neural dynamics during movement planning, independent of immediate sensory feedback, and compare these to the adapted motor commands during execution. PPT PowerPoint slide

PNG larger image

TIFF original image Download: Fig 1. Experimental setup and decoding scheme for 3D virtual reality (VR) task. (A) Schematic representation of the 3D VR setup allowing control of movements through manual control (MC) and brain–computer interface (BCI). The setup includes four infrared cameras for online tracking of hand position using reflective markers, enabling realistic 3D movements and decoder calibration for the BCI task. The monkey’s other arm was gently restrained, and gaze position was monitored using an infrared eye tracker. The scheme was drawn manually by the authors. (B) Memory-guided center-out reach task in 3D. In each trial, one of the eight corners of the 3D cube was briefly cued as the target and had to be reached after a variable memory period. (C) Averaged 3D trajectories obtained from one experimental session in Monkey Y during 3D reaches performed under the BCI condition. (D) Two alternative decoding schemes. A subset of M1 and PMd cells was always involved in decoding, represented by black circle outlines. In contrast, PRR cells were either fully integrated into the decoding process (decoder FP) or entirely disconnected (decoder FO). https://doi.org/10.1371/journal.pbio.3003408.g001 The monkeys controlled the cursor either through natural hand movements (manual control, MC) or via a BCI driven by intracortical neural activity (Fig 1C). During MC trials, the cursor movements in virtual space were aligned with the animals’ hand movements in physical space. In BCI trials, the monkeys kept their unrestrained hand in a resting position while controlling the cursor mentally. S1B Fig shows that residual hand movements were minimal and barely correlated with cursor speed (Pearson’s correlation: monkey Y, r = 0.085, p < 0.001, n (total number of decoded speeds) = 575,258; monkey Z, r = 0.072, p < 0.001, n = 108,933). This means the animal managed to control the cursor movement without any longer attempting own physical movements, neither during planning nor during movement, which ensured a constant somatosensory input during BCI trials. During baseline trials, i.e., without visuomotor perturbation, monkey Y successfully drove the cursor to the target in 99% of the movement trials (trials in which the animal successfully completed the planning period without, e.g., fixation breaks; see Materials and methods) under MC and in 89% of the BCI trials. Monkey Z succeeded in 97% of the movement trials under MC and in 85% of the BCI trials. Only successful trials were analyzed. We recorded brain activity from three regions: the primary M1, PMd, and PRR. Monkey Y had 64 electrodes in each of M1, PMd, and PRR, while monkey Z had 64 electrodes in M1 and 96 electrodes in each of PMd and PRR (S1C Fig). Units from all three areas were selectively connected (controlling units) or not connected (noncontrolling units) to the decoder (Fig 1D). This design allowed us to investigate whether adaptive mechanisms in individual neurons depended on their immediate causal influence on movement, which is only guaranteed for connected units. Subsets of PMd and M1 units were always involved in the decoder, whereas PRR units were either entirely connected (in combination with PMd-M1; Decoder fronto-parietal (FP), Fig 1D, lower) or completely disconnected (Decoder frontal-only (FO), Fig 1D, upper). This setup enabled us to study adaptive mechanisms in PRR even without its direct involvement in generating movements and to compare it to the case when PRR directly contributes to the control of BCI movements.

Adaptation of BCI movements was induced by perturbed visual feedback in a 3D visuomotor rotation task We utilized a short-term visuomotor rotation (VMR) adaptation paradigm tailored to our 3D experimental setup [67] to induce repeated BCI learning (Fig 2). In the daily sessions, first, the decoder was recalibrated until animals became proficient in controlling unperturbed cursor movements without computer support (see Materials and methods). We then recorded 160 baseline trials before we introduced the perturbation, a rotation of 30° to the visual feedback (cursor) in the fronto-parallel X–Y plane, for 300 trials (Fig 2A). In each daily session, the perturbation was consistently either clockwise or counterclockwise only (S1 Table). The perturbation was applied globally throughout the workspace, which means it affected movements to all eight targets but only in the X and Y dimensions, not in the depth dimension away from the body (Fig 2A, left). Finally, the perturbation was removed during the washout phase, which continued until the animals disengaged from the task (Fig 2A, right). In designing this study, we leveraged previous findings from [67], which showed that human subjects adapted most easily to perturbations applied in the fronto-parallel plane. PPT PowerPoint slide

PNG larger image

TIFF original image Download: Fig 2. Experimental paradigm and BCI adaptation. (A) Left: visual representation of 3D-VMR. In different experimental sessions, the 3D movements were rotated 30° clockwise or counterclockwise in the frontal-parallel plane. The Z-axis points towards the monkey’s head and was unperturbed. Right: the experimental protocol consisted of a baseline phase followed by a 30° VMR and a washout phase where the visual perturbation was removed. Below each phase of the experimental progression, example traces from a single experimental session in Monkey Y are shown. (B) In the plane of the applied perturbation, deviations above the zero line correspond to positions in the visual signal plane. Conversely, negative deflections in trajectories exist in the motor signal plane and correspond to the motor commands issued to counteract the visual perturbation. For averaging across all target directions, the trajectories were rotated into a shared reference frame. One axis was aligned with the perturbed direction, while the other was aligned with the vector from the center to the target. In this plane, trajectories above the zero line represent positions in the visual signal plane, while trajectories below zero pertain to the motor signal plane. Additionally, we calculated the angle at the midpoint of each trajectory as the angle between the perturbation direction and the vector from the center to the target. (C) For the analysis, the 3D trajectories were projected onto the 2D plane of the applied perturbation. The trajectories showed stereotypical adaptation profiles during rotation and negative aftereffects during washout. (D) The angle at the midpoint of the trajectory, plotted as a function of trial number, reveals stereotypical adaptation profiles in response to the introduced VMR. The perturbation angle approaches 30° upon perturbation introduction, gradually decreases with learning, and then deviates in the opposite direction during the washout phase following perturbation removal. Numerical data are available in S1 Data. https://doi.org/10.1371/journal.pbio.3003408.g002 To visualize movement adaptation, we projected the 3D movement trajectories of the controlled cursor into the X–Y plane affected by the perturbation (perturbation plane). During the baseline phase, the trajectories were on average relatively straight toward the target. During early rotation trials, as expected as a consequence of the perturbed feedback, trajectories in the perturbation plane curved in the direction of the applied feedback perturbation (Fig 2A, right). The average trajectories were less curved in late rotation trials. In the early washout phase, the trajectories on average curved in the opposite direction compared to during perturbation. The reduced curvature in late rotation trials and inverse curvature observed during washout indicate successful adaptation, which we quantified jointly across all target directions. To obtain averaged trajectories across targets, we rotated them for each target such that the X-axis aligned with the center-to-target direction and the Y-axis aligned orthogonally to this (perturbation direction, Fig 2B; see Materials and methods). Fig 2C depicts the average cursor trajectories across all eight targets and for all sessions for monkey Y. Additionally, the corresponding “motor” signal, representing the trajectories that would be produced by the unperturbed decoder using the neural activity of the controlling units, is visualized (Fig 2B–2C). To quantify adaptation, we calculated the trial-by-trial angular movement error (α) from the starting direction of the movement. To have a good enough estimate of movement direction while keeping the influence of online movement corrections small, we measured the angle of the cursor position at the halfway point of the trajectory relative to the straight line connecting the starting position and the target position (Fig 2C). On average, during baseline trials, the angular error was close to zero and did not decrease over time, as the fitted data with exponential decay did not show significant differences from zero (Monkey Y: intercept = 0.637, p = 0.26, decay = 0.006, p = 0.43, n = 51; Monkey Z: intercept = 1.5, p = 0.26, decay = 0.0029, p = 0.8, n = 15). It increased to about 20° at the onset of the VMR perturbation and gradually decreased as the monkeys adapted their neural activity to regain better control (Fig 2D). Fitting the data with an exponential decaying function during the perturbation phase indicated significant visuomotor adaptation (Monkey Y: n = 51, intercept = 20.99, p < 0.001, decay = −0.0026, p < 0.001; Monkey Z: n = 15, intercept = 20.69, p < 0.001, decay = −0.0018, p = 0.0017). During the washout phase, movement errors were committed in the opposite direction to the applied perturbation, consistent with a negative aftereffect as typically seen in visuomotor rotation adaptation. Fitting the data with an exponential decay revealed a significant intercept for both animals (Monkey Y: intercept = −11.63, p < 0.001, n = 26; Monkey Z: intercept = −17.255, p = 0.0018, n = 4), indicating significant aftereffects for both animals. These findings suggest a change in the sensorimotor transformation achieved during BCI adaptation trials.

Preserved covariance structure suggests within-manifold learning during BCI-VMR In BCI-controlled movements, behavioral changes represented by the cursor on the screen (i.e., the decoder output) directly mirror the activity of the neural population that controls the decoder. When faced with a VMR-type perturbation, this controlling neural group can restore its performance by re-associating certain target directions with different movement directions [15,18]. The re-association hypothesis means that the neurons can direct the cursor to the desired target using activity patterns consistent with those observed during the center-out reach task on which they were originally trained, associating a direction suited to compensate for the perturbing rotation. In contrast to the re-configuration hypothesis, the covariance structure between different neurons should change very little with re-associations, since the network remains stable. This stability implies that the neural dynamics that define the low-dimensional neural subspace (i.e., the neural manifold), which explains most of the neural variance before adaptation (during baseline), also explain most of the variance during perturbation and after adaptation (washout). To test the re-association versus re-configuration hypothesis, we calculated the alignment index [62] of the neural manifolds between the baseline trials and the other experimental phases (S2 Fig). Alignment should be high in the case of re-association. The alignment index is computed as the ratio of the explained variance when projecting the neural activity from the other experimental phases onto the principal components (PCs) derived from the baseline activity (see Materials and methods section). We focused on the first four PCs, which capture the majority of the variance in the baseline trials (S3A, S3C, S3E, and S3G Fig). Supporting the re-association hypothesis, we find that a substantial portion of the initially explained variance remains constant throughout the learning process. In fact, the average difference between the alignment indices during late rotation and the cross-validated alignment index during baseline is smaller than 1% for all the different neural populations that we calculated (S2 Fig). This suggests a preservation of the manifold structure during VMR adaptation in the frontal motor network.

Noncontrolling units in M1-PMd encode the same corrective task parameters during adaptation of BCI movement as controlling units Our primary research goal was to identify the spatial reference frames of movement parameters encoded by neurons without a direct causal link to the movement (noncontrolling units) in frontal versus parietal brain areas. During BCI movements, noncontrolling units could encode movement parameters in either a visual coordinate system (reflecting the cursor movement) or a physical coordinate system (reflecting intended limb movements). To distinguish between these spatial encoding strategies, we used an offline decoding approach to reconstruct movement trajectories from noncontrolling units. First, a Kalman filter (KF) decoder (as for online decoding) was trained offline with neural activity of the noncontrolling units during BCI baseline trials (training set, Fig 3A) to reproduce the cursor trajectories actually produced by the animal with the controlling units during baseline. During unperturbed baseline trials, these trajectories represent both the motor command (of the controlling units) and visual cursor feedback alike, since both are spatially congruent (identical except for sensory noise). PPT PowerPoint slide

PNG larger image

TIFF original image Download: Fig 3. Offline decoder approach. (A) Schematic illustration of the offline decoding principle during the movement period. A velocity Kalman filter decoder was applied to reconstruct theoretical memory trajectories from neural activity (first column). During the movement period, real BCI trajectories were used for regression with neural firing rates from noncontrolling neurons during baseline trials (second column, training set). After calibration during baseline trials, the decoder was used to reconstruct continuous trajectories during perturbation trials (third column). The example averaged trajectories demonstrate continuous movements toward the target. In the last column, the reconstructed 3D trajectory from the offline decoder is projected onto the perturbation plane for perturbation trials. These projections assess visual-like or motor-like encoding, where positive deviations along this axis correspond to the visual signal, and negative deflections indicate motor output during adaptation. (B) Reconstruction and projection of the offline trajectories from M1-PMd in the adaptive dimension for the late adaptation phase. The depicted trajectories represent the averages across all experiments where nondecoding units were collected. The traces display the average (solid color) and the bootstrapped confidence interval (shaded colors) for baseline (pink) and late rotation (last 50% of the trials per session) trajectories (green). (C) Box plot showing deflections of the trajectories at the end of movement for both animals during late rotation trials. A significant negative deflection indicates motor-like encoding (*p < 0.05, ***p < 0.001). The whiskers represent the 5th and 95th percentiles. Numerical data are available in S2 Data. https://doi.org/10.1371/journal.pbio.3003408.g003 Second, this decoder was then applied to the neural activity during VMR trials of the same neural population (test set, Fig 3A). During VMR trials, the motor command of the controlling units and the visual cursor feedback are spatially incongruent. This allows us to test which spatial reference frame the noncontrolling units better correspond to. More specifically, the projection of the decoded trajectories along the perturbation axis (deflection measure) allowed us to study whether neural activity of the noncontrolling units during adaptation reflects (i) a stationary, nonadaptive signal (H1), (ii) the adapting visual cursor trajectories (H2), or (iii) the adapting motor signal as produced by the controlling units (H3). Straight trajectories to the target as during baseline would reflect a nonadaptive signal in noncontrolling populations, corresponding to a lack of deviation in the perturbation direction (Fig 3A). Deviation from baseline deflecting in the positive direction of the perturbation axis would reflect the visual signal (visual signal plane; corresponding to perturbation direction greater than 0), while deflection in the negative direction would reflect the motor command (motor signal space; corresponding to perturbation direction <0). In our results, the negatively deflected reconstructed trajectories in both animals show that noncontrolling units in M1 and PMd reflect the motor command similarly to controlling units (Fig 3B–3C). To accurately capture this deflection, we used a trajectory deflection measure in our analysis. This approach differs from the angular movement error (α) that we previously calculated to quantify adaptation, where α was measured at the midpoint of the trajectory relative to the straight line connecting the starting and target positions (Fig 2C). We chose the deflection measure for the reconstructed trajectories because it accounts for the overall shape and direction of the entire movement path. The mean deflection of the activity of monkey Y at the end of the movement (100%) was −8.69 mm ± 4.43 mm (mean ± standard deviation), significantly different from zero (one-sample t test, t(50) = −14.010, p < 0.001). For monkey Z, the mean deflection of activity at the end of the movement was also significantly below zero, at −9.88 mm ± 5.27 mm (t(14) = −7.250, p < 0.001). To isolate area-specific adaptation between M1 and PMd, we also tracked how each neuron’s preferred direction (PD) evolved during learning. This analysis revealed robust, systematic PD rotations that were independently measurable in both M1 and PMd (S4 Fig). These tuning shifts are well-aligned to partially compensate for the experimentally induced visual rotation. To gain deeper insight into the level of adaptation achieved by the noncontrolling neuronal population, we calculated the angle representing the deviation in the perturbation direction at the final level of adaptation. This angle was measured between the reconstructed trajectory of each population and the direct path to the target. To compare the adaptation levels across different populations, we then scaled the final angle reached by each neural subpopulation relative to that of the controlling unit population and called this the “relative gain.” The relative gain quantifies the deviation along the perturbation axis relative to the level of adaptation achieved. By expressing these deviations as a fraction of the total adaptation level exhibited by the controlling units, we could assess and compare how much each population contributed to adapting to the perturbation. Specifically, the M1-PMd noncontrolling population reached a relative gain of 59% for Monkey Y and 94% for Monkey Z, underscoring the significant adaptation observed in the M1-PMd noncontrolling population.

Controlling and noncontrolling units in PRR share the same spatial frame of reference during adaptation of BCI movement as controlling units in M1-PMd We next investigated how VMR adaptation affects PRR activity, focusing on whether PRR’s adaptation-associated neural dynamics share the same spatial frame of reference as the frontal regions. By analyzing the adaptation process in PRR both with direct influence on movement control (decoder FP, controlling PRR units) and without it (decoder FO, noncontrolling PRR units), we determined whether PRR predominantly mirrors visual feedback or plays an active role similar to frontal areas. The overall neural yield in PRR was lower than in the combined M1-PMd recordings for both subjects. To still achieve similar baseline performance across conditions and to have enough noncontrolling units in PRR for analysis, the controlling PRR neurons were combined with a subset of M1-PMd units in the FP decoder sessions. This design choice was motivated by the fact that we observed lower performance in pre-testing trials when using PRR alone, possibly due to its weaker motor coding properties. Still, this experimental strategy allowed us to compare adaptation of PRR while partially causing the movement or not causing it directly at all. First, we evaluated whether the respective contributions of M1-PMd and PRR to the cursor movement were balanced during FP decoding, or rather predominated by M1-PMd inputs. Each cell’s contribution was determined by multiplying the norm of the vector that translates decoded speed into its firing rate summed with its baseline firing rate (Fig 4A), a similar approach as described in [68]. The distributions of the contribution values differed only modestly between PRR and M1-PMd (Cohen’s d: d = 0.67 for Monkey Y, d = 0.38 for Monkey Z). This suggests that both the PRR and M1-PMd areas similarly drive the motor output of the decoder. We also ensured that after baseline trials, neither of these areas significantly decreased their firing rate during adaptation which might indicate a substantial silencing of the area. Average firing rates between baseline and adaptation did not differ significantly (Mann–Whitney test, p > 0.05; Fig 4B). Together, these results indicate an active contribution of PRR to decoder adaptation. PPT PowerPoint slide

PNG larger image

TIFF original image Download: Fig 4. PRR controlling and noncontrolling adaptation during movement execution. (A) Distributions of the norm of the linear regression vectors from velocity to neural firing rate for every single unit contributing to the decoder, color-coded depending on the neural population. Numerical data are available in S3 Data. (B) Mean firing rates for PRR and M1-PMd controlling units during baseline and late rotation epochs show that neither PRR nor M1-PMd is reducing their firing rate when compared to baseline (all p values nonsignificant with Mann–Witney test, n = 11 for Monkey Y, n = 5 for Monkey Z). Numerical data are available in S3 Data. (C) Similar to Fig 3C, the bar plots show the deflection of the trajectories at the end of the reach. Each distribution consists of all PRR neurons and is tested against zero for the two different decoding schemes and the two monkeys separately (**p < 0.01, ***p < 0.001). Numerical data are available in S2 Data. https://doi.org/10.1371/journal.pbio.3003408.g004 Changes in PRR activity in response to the VMR exhibited motor-like characteristics similar to M1-PMd (Fig 4C). Across both decoder configurations (FP and FO), significant deflections from zero were observed at the end of movement. For monkey Y, under FP decoding, the mean deflection was −1.64 ± 1.56 mm (t(10) = −3.48, p = 0.006); under FO, −1.85 ± 2.25 mm (t(39) = −5.20, p < 0.001). For monkey Z, under FP decoding, deflection was −10.90 ± 2.87 mm (t(4) = −8.49, p = 0.001); under FO, −5.18 ± 5.08 mm (t(4) = −12.37, p = 0.0001). These results indicate a motor-like encoding during adaptation, qualitatively similar to M1-PMd (Fig 4C). Even when PRR was not contributing to the control of the decoder, an equivalent pattern of adaptation was observed, indicating motor-related changes rather than representation of the visual feedback. For the PRR controlling units, Monkey Y showed a 13% relative gain to the final adaptation, while Monkey Z showed 64%. When PRR was noncontrolling, Monkey Y showed a 14% relative gain, and Monkey Z achieved 37%. In summary, our findings suggest that PRR exhibits substantial motor-like encoding during movement adaptation, even when it is not directly controlling the movement, highlighting its involvement in the adaptation process. This indicates that PRR plays a more integral role in motor adaptation than merely reflecting visual feedback.

Comparing movement planning and execution to uncover neural adaptation dynamics in BCI learning An important aspect of our study design is to compare movement planning and execution to understand the dynamics of neural adaptation. Specifically, we ask whether motor preparation and the associated initial states of neural dynamics are updated trial-by-trial during adaptation, or if only online motor control changes during BCI learning. The transition between planning and movement phase was comparable in the BCI and MC contexts. We applied a PC decomposition and cross-projected the neural activity during planning onto the dimensions that best explained the activity during movement. We found that the explained variance in the planning activity, when projected into the first four dimensions of the movement subspace, was significantly reduced in comparison to the movement activity (S3 Fig, paired t test, all comparisons p < 0.001. Monkey Y, MC: M1-PMd meanₚₗₐₙ = 0.46, meanₘₒᵥ = 0.86, t(49); PRR meanₚₗₐₙ = 0.87, meanₘₒᵥ = 0.91, t(49). BC: M1-PMd meanₚₗₐₙ = 0.67, meanₘₒᵥ = 0.92, t(49); PRR meanₚₗₐₙ = 0.88, meanₘₒᵥ = 0.96, t(49). Monkey Z, MC: M1-PMd meanₚₗₐₙ = 0.53, meanₘₒᵥ = 0.76, t(14); PRR meanₚₗₐₙ = 0.63, meanₘₒᵥ = 0.74, t(14). BC: M1-PMd meanₚₗₐₙ = 0.62, meanₘₒᵥ = 0.83, t(14); PRR meanₚₗₐₙ = 0.79, meanₘₒᵥ = 0.87, t(14)). The subspace misalignment was more pronounced in frontal areas compared to parietal areas in both monkeys. The alignment patterns were equivalent in both manual and BCI trials. These results show substantial differences between the manifold structure in the planning and movement phases not only in MC but also in BCI. We attribute these change in neural state to a transitions from planning while not attempting cursor movements during the delay period to actually producing a cursor move towards the target during the movement period. If the animals had attempted cursor movements during the delay period of the BCI trials already, the release of the cursor blockade at the end of the delay period would have led to an immediate cursor movement, since neural response modulations directly affect cursor movements in BCI control. Instead, what we observed in BCI trials were neural response latencies from the go cue that were similar to, or even longer than those during manual reaches (animal Y: MC activity peak 259.55 ± 31.58 ms versus BCI 255.05 ± 29.09 ms; animal Z: MC activity peak 353.05 ± 43.13 ms versus BCI 463.72 ± 43.86 ms; see S3I Fig).

Motor planning activity in M1-PMd reflects a re-association strategy To better understand how the FP network adjusts to altered feedback, we examined changes not just during the control of movement but also during its planning phase. To measure neural changes during planning, we employed an offline decoder approach similar to the one used for studying the movement phase. For planning, similar to movement decoding, we trained the decoder with baseline trials but used neural activity from the memory period. Unlike movement decoding, this training involved regressing the firing rates against a directional vector that continuously pointed to the cued target. Decoding the direction every 50 ms enabled us to reconstruct hypothetical 3D trajectories offline during the planning phase. For each target direction, we took the 400 ms time interval before the ‘go’ cue. We then calculated the vector sum of the individual unit vectors, each pointing in the direction decoded within that specific time bin (Fig 5A). The hypothetical trajectories we produced stem from a direct projection of the neural space into the 3D task space through the decoder, reflecting the intended direction during planning. In essence, these trajectories transform neural activity into a space where variations are explicitly linked to the task space (via the decoder), such as aiming at a target. To validate our decoding methodology during the planning phase, we identified the intended targets by determining the closest target to the final position attained at the conclusion of the planning phase for each reconstructed trajectory within the task space (using an 8-way discrete classifier). In our baseline trials, the decoder produced hypothetical trajectories in the task space that were consistent with the intended target (S5 Fig). PPT PowerPoint slide

PNG larger image

TIFF original image Download: Fig 5. Memory period decoder. (A) Schematic illustration of the offline decoding principle during the memory period. As previously described for movement decoding, a velocity Kalman filter decoder was applied to reconstruct theoretical memory trajectories from neural activity (first column). During the memory period, a unity vector always pointing at the target was used for regression with neural firing rates (second column, training set). After calibration during baseline trials, the decoder was used to reconstruct continuous trajectories during perturbation trials (third column). The example averaged trajectories demonstrate continuous movements toward the target. In the last column, the reconstructed 3D trajectory from the offline decoder is projected onto the perturbation plane for perturbation trials. These projections assess visual-like or motor-like encoding, where positive deviations along this axis correspond to the visual signal, and negative deflections indicate motor output during adaptation. (B) The reconstructed trajectories in the perturbed dimension exhibit a negative deflection, indicating re-aiming, even during the planning period. At the end of the trajectory, a box plot compares all M1-PMd neurons with zero. The whiskers represent the 5th and 95th percentiles. Numerical data are available in S4 Data. https://doi.org/10.1371/journal.pbio.3003408.g005 Equivalent to the analyses during movement, we tested the decoder’s generalization to quantify adaptation and its spatial frame of reference. This was done by training it during the memory phase of the baseline trials and then applying it during the memory phase of the rotation trials. Much like neural activity observed during movement, any deviations in the reconstructed hypothetical trajectories (once projected onto the perturbation direction) would suggest either motor- or visual-like adaptation. We quantified this in a manner equivalent to how we assessed data during movement control (Fig 5A, right side). For both monkeys, the controlling and noncontrolling units in the M1-PMd regions exhibited motor-like adaptation during the planning phase. This demonstrates the use of a re-association strategy in a motor-reference frame during motor planning (Fig 5B). In the PRR, re-association in a motor-reference frame during planning could also be observed. Differences were statistically significant for the individual monkeys in either only the controlling units (monkey Y) or in the noncontrolling units (monkey Z) with nonsignificant trends in the respective other group of neurons (S6 Fig).

[END]
---
[1] Url: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3003408

Published and (C) by PLOS One
Content appears here under this condition or license: Creative Commons - Attribution BY 4.0.

via Magical.Fish Gopher News Feeds:
gopher://magical.fish/1/feeds/news/plosone/