Where one hand meets the other: limb-specific and action-dependent movement plans decoded from preparatory signals in single human frontoparietal brain areas

Neural Correlates of Online Action Preparation

When performing movements in rapid succession, the brain needs to coordinate ongoing execution with the preparation of an upcoming action. Here we identify the processes and brain areas involved in this ability of online preparation. Human participants (both male and female) performed pairs of single-finger presses or three-finger chords in rapid succession, while 7T fMRI was recorded. In the overlap condition, they could prepare the second movement during the first response and in the nonoverlap condition only after the first response was completed. Despite matched perceptual and movement requirements, fMRI revealed increased brain activity in the overlap condition in regions along the intraparietal sulcus and ventral visual stream. Multivariate analyses suggested that these areas are involved in stimulus identification and action selection. In contrast, the dorsal premotor cortex, known to be involved in planning upcoming movements, showed no discernible signs of heightened activity. This observation suggests that the bottleneck during simultaneous action execution and preparation arises at the level of stimulus identification and action selection, whereas movement planning in the premotor cortex can unfold concurrently with the execution of a current action without requiring additional neural activity.

Human local field potentials in motor and non-motor brain areas encode upcoming movement direction

Limb movement direction can be inferred from local field potentials in motor cortex during movement execution. Yet, it remains unclear to what extent intended hand movements can be predicted from brain activity recorded during movement planning. Here, we set out to probe the directional-tuning of oscillatory features during motor planning and execution, using a machine learning framework on multi-site local field potentials (LFPs) in humans. We recorded intracranial EEG data from implanted epilepsy patients as they performed a four-direction delayed center-out motor task. Fronto-parietal LFP low-frequency power predicted hand-movement direction during planning while execution was largely mediated by higher frequency power and low-frequency phase in motor areas. By contrast, Phase-Amplitude Coupling showed uniform modulations across directions. Finally, multivariate classification led to an increase in overall decoding accuracy (>80%). The novel insights revealed here extend our understanding of the role of neural oscillations in encoding motor plans.

Effector-independent Representations Guide Sequential Target Selection Biases in Action

Previous work shows that automatic attention biases toward recently selected target features transfer across action and perception and even across different effectors such as the eyes and hands on a trial-by-trial basis. Although these findings suggest a common neural representation of selection history across effectors, the extent to which information about recently selected target features is encoded in overlapping versus distinct brain regions is unknown. Using fMRI and a priming of pop-out task where participants selected unpredictable, uniquely colored targets among homogeneous distractors via reach or saccade, we show that color priming is driven by shared, effector-independent underlying representations of recent selection history. Consistent with previous work, we found that the intraparietal sulcus (IPS) was commonly activated on trials where target colors were switched relative to those where the colors were repeated; however, the dorsal anterior insula exhibited effector-specific activation related to color priming. Via multivoxel cross-classification analyses, we further demonstrate that fine-grained patterns of activity in both IPS and the medial temporal lobe encode information about selection history in an effector-independent manner, such that ROI-specific models trained on activity patterns during reach selection could predict whether a color was repeated or switched on the current trial during saccade selection and vice versa. Remarkably, model generalization performance in IPS and medial temporal lobe also tracked individual differences in behavioral priming sensitivity across both types of action. These results represent a first step to clarify the neural substrates of experience-driven selection biases in contexts that require the coordination of multiple actions.

AngoraPy: A Python toolkit for modeling anthropomorphic goal-driven sensorimotor systems

Goal-driven deep learning increasingly supplements classical modeling approaches in computational neuroscience. The strength of deep neural networks as models of the brain lies in their ability to autonomously learn the connectivity required to solve complex and ecologically valid tasks, obviating the need for hand-engineered or hypothesis-driven connectivity patterns. Consequently, goal-driven models can generate hypotheses about the neurocomputations underlying cortical processing that are grounded in macro- and mesoscopic anatomical properties of the network's biological counterpart. Whereas, goal-driven modeling is already becoming prevalent in the neuroscience of perception, its application to the sensorimotor domain is currently hampered by the complexity of the methods required to train models comprising the closed sensation-action loop. This paper describes AngoraPy, a Python library that mitigates this obstacle by providing researchers with the tools necessary to train complex recurrent convolutional neural networks that model the human sensorimotor system. To make the technical details of this toolkit more approachable, an illustrative example that trains a recurrent toy model on in-hand object manipulation accompanies the theoretical remarks. An extensive benchmark on various classical, 3D robotic, and anthropomorphic control tasks demonstrates AngoraPy's general applicability to a wide range of tasks. Together with its ability to adaptively handle custom architectures, the flexibility of this toolkit demonstrates its power for goal-driven sensorimotor modeling.

The Representation of Observed Actions at the Subordinate, Basic, and Superordinate Level

Actions can be planned and recognized at different hierarchical levels, ranging from very specific (e.g., to swim backstroke) to very broad (e.g., locomotion). Understanding the corresponding neural representation is an important prerequisite to reveal how our brain flexibly assigns meaning to the world around us. To address this question, we conducted an event-related fMRI study in male and female human participants in which we examined distinct representations of observed actions at the subordinate, basic and superordinate level. Using multiple regression representational similarity analysis (RSA) in predefined regions of interest, we found that the three different taxonomic levels were best captured by patterns of activations in bilateral lateral occipitotemporal cortex (LOTC), showing the highest similarity with the basic level model. A whole-brain multiple regression RSA revealed that information unique to the basic level was captured by patterns of activation in dorsal and ventral portions of the LOTC and in parietal regions. By contrast, the unique information for the subordinate level was limited to bilateral occipitotemporal cortex, while no single cluster was obtained that captured unique information for the superordinate level. The behaviorally established action space was best captured by patterns of activation in the LOTC and superior parietal cortex, and the corresponding neural patterns of activation showed the highest similarity with patterns of activation corresponding to the basic level model. Together, our results suggest that occipitotemporal cortex shows a preference for the basic level model, with flexible access across the subordinate and the basic level.SIGNIFICANCE STATEMENT The human brain captures information at varying levels of abstraction. It is debated which brain regions host representations across different hierarchical levels, with some studies emphasizing parietal and premotor regions, while other studies highlight the role of the lateral occipitotemporal cortex (LOTC). To shed light on this debate, here we examined the representation of observed actions at the three taxonomic levels suggested by Rosch et al. (1976) Our results highlight the role of the LOTC, which hosts a shared representation across the subordinate and the basic level, with the highest similarity with the basic level model. These results shed new light on the hierarchical organization of observed actions and provide insights into the neural basis underlying the basic level advantage.

Premovement activity in the mesocortical system links peak force but not initiation of force generation under incentive motivation

Motivation facilitates motor performance; however, the neural substrates of the psychological effects on motor performance remain unclear. We conducted a functional magnetic resonance imaging experiment while human subjects performed a ready-set-go task with monetary incentives. Although subjects were only motivated to respond quickly, increasing the incentives improved not only reaction time but also peak grip force. However, the trial-by-trial correlation between reaction time and peak grip force was weak. Extensive areas in the mesocortical system, including the ventral midbrain (VM) and cortical motor-related areas, exhibited motivation-dependent activity in the premovement "Ready" period when the anticipated monetary reward was displayed. This premovement activity in the mesocortical system correlated only with subsequent peak grip force, whereas the activity in motor-related areas alone was associated with subsequent reaction time and peak grip force. These findings suggest that the mesocortical system linking the VM and motor-related regions plays a role in controlling the peak of force generation indirectly associated with incentives but not the initiation of force generation.

Decoding reach-to-grasp from EEG using classifiers trained with data from the contralateral limb

Fundamental to human movement is the ability to interact with objects in our environment. How one reaches an object depends on the object's shape and intended interaction afforded by the object, e.g., grasp and transport. Extensive research has revealed that the motor intention of reach-to-grasp can be decoded from cortical activities using EEG signals. The goal of the present study is to determine the extent to which information encoded in the EEG signals is shared between two limbs to enable cross-hand decoding. We performed an experiment in which human subjects (n = 10) were tasked to interact with a novel object with multiple affordances using either right or left hands. The object had two vertical handles attached to a horizontal base. A visual cue instructs what action (lift or touch) and whether the left or right handle should be used for each trial. EEG was recorded and processed from bilateral frontal-central-parietal regions (30 channels). We trained LDA classifiers using data from trials performed by one limb and tested the classification accuracy using data from trials performed by the contralateral limb. We found that the type of hand-object interaction can be decoded with approximately 59 and 69% peak accuracy in the planning and execution stages, respectively. Interestingly, the decoding accuracy of the reaching directions was dependent on how EEG channels in the testing dataset were spatially mirrored, and whether directions were labeled in the extrinsic (object-centered) or intrinsic (body-centered) coordinates.

How embodied is cognition? fMRI and behavioral evidence for common neural resources underlying motor planning and mental rotation of bodily stimuli

Functional neuroimaging shows that dorsal frontoparietal regions exhibit conjoint activity during various motor and cognitive tasks. However, it is unclear whether these regions serve several, computationally independent functions, or underlie a motor "core process" that is reused to serve higher-order functions. We hypothesized that mental rotation capacity relies on a phylogenetically older motor process that is rooted within these areas. This hypothesis entails that neural and cognitive resources recruited during motor planning predict performance in seemingly unrelated mental rotation tasks. To test this hypothesis, we first identified brain regions associated with motor planning by measuring functional activations to internally-triggered vs externally-triggered finger presses in 30 healthy participants. Internally-triggered finger presses yielded significant activations in parietal, premotor, and occipitotemporal regions. We then asked participants to perform two mental rotation tasks outside the scanner, consisting of hands or letters as stimuli. Parietal and premotor activations were significant predictors of individual reaction times when mental rotation involved hands. We found no association between motor planning and performance in mental rotation of letters. Our results indicate that neural resources in parietal and premotor cortex recruited during motor planning also contribute to mental rotation of bodily stimuli, suggesting a common core component underlying both capacities.

Phasic modulation of beta power at movement-related frequencies during visuomotor conflict

Rhythmic cortical activity is thought to underlie many cognitive functions including the flexible weighting of sensory information depending on the current behavioral context. Here, we tested for potential oscillatory alignment and power modulation at behaviorally relevant frequencies in magnetoencephalography (MEG) data acquired during a virtual reality-based, rhythmic hand-target phase matching task. The task contained conditions differing in terms of visuomotor incongruence and whether or not behavior (grasping movements) had to be adapted to keep vision aligned with the target. We tested for potential oscillatory alignment with movement frequencies and cross-frequency coupling with oscillations in the alpha, beta, and gamma bands. Our results revealed local peaks at 1 Hz power, corresponding to the frequency at which hand movements alternated between open and close; thus, potentially indicating an "entrainment" of neural oscillations at key movement frequencies. We found 1 Hz power was selectively enhanced when participants needed to align incongruent vision with the target. Moreover, the phase of the "movement-entrained" 1 Hz oscillations coupled significantly with the momentary amplitude of beta band oscillations-again, this coupling was selectively enhanced when incongruent vision was task relevant. We propose that this reflected a top-down mechanism, most likely related to selective attention and rhythmic sensory sampling. Thus, phasic low-frequency (beta) power suppression likely indicated a variable (attentional) sampling of visual movement feedback; i.e., related to increased sensitivity for visually matching alternating hand movements to a phasic target at ecologically important time points, rather than continually during the grasping cycle.NEW & NOTEWORTHY Our results reveal an increased spectral power at movement frequencies in a rhythmic hand-target phase matching task under visuomotor conflict; this effect was strongest when incongruent visual movement feedback was required to guide action. Moreover, the phase of these slow frequencies coupled with the momentary power beta oscillations; again, this coupling was selectively strengthened when incongruent vision was task relevant.

Dynamic spatial coding in parietal cortex mediates tactile-motor transformation

Movements towards touch on the body require integrating tactile location and body posture information. Tactile processing and movement planning both rely on posterior parietal cortex (PPC) but their interplay is not understood. Here, human participants received tactile stimuli on their crossed and uncrossed feet, dissociating stimulus location relative to anatomy versus external space. Participants pointed to the touch or the equivalent location on the other foot, which dissociates sensory and motor locations. Multi-voxel pattern analysis of concurrently recorded fMRI signals revealed that tactile location was coded anatomically in anterior PPC but spatially in posterior PPC during sensory processing. After movement instructions were specified, PPC exclusively represented the movement goal in space, in regions associated with visuo-motor planning and with regional overlap for sensory, rule-related, and movement coding. Thus, PPC flexibly updates its spatial codes to accommodate rule-based transformation of sensory input to generate movement to environment and own body alike.

Interhemispheric interplay between the left and right premotor cortex during grasping as assessed by dynamic causal modelling

Research on the contribution of the ipsilateral hemisphere to unilateral movements, and how it is mediated by transcallosal connections, has so far provided contradictory findings. By using dynamic causal modelling (DCM) and Parametric Empirical Bayes analyses applied to fMRI data, we sought to describe effective connectivity during pantomimed and imagined right-hand grasping within the grasping network, namely the anterior intraparietal sulcus, ventral and dorsal (PMd) premotor cortex, supplementary motor area and primary motor cortex (M1). The two-fold aim of the present work was to explore a) whether right and left parieto-frontal areas show similar connectivity couplings, and b) the interhemispheric dynamics between these regions across the two hemispheres. We detected a network architecture comparable across hemispheres during executed but not imagined grasping movements. Furthermore, during pantomimed grasping the interhemispheric crosstalk was mainly driven by premotor areas: we found an inhibitory influence from the right PMd toward the left premotor and motor areas and excitatory couplings between homologous ventral premotor and supplementary motor regions. Overall, our results support the view that dissociable components of unilateral grasping execution are encoded by a non-lateralized set of brain areas complexly intertwined by interhemispheric dynamics, whereas motor imagery obeys different principles.

Effector-selective modulation of the effective connectivity within frontoparietal circuits during visuomotor tasks

Despite extensive research, the functional architecture of the subregions of the dorsal posterior parietal cortex (PPC) involved in sensorimotor processing is far from clear. Here, we draw a thorough picture of the large-scale functional organization of the PPC to disentangle the fronto-parietal networks mediating visuomotor functions. To this aim, we reanalyzed available human functional magnetic resonance imaging data collected during the execution of saccades, hand, and foot pointing, and we combined individual surface-based activation, resting-state functional connectivity, and effective connectivity analyses. We described a functional distinction between a more lateral region in the posterior intraparietal sulcus (lpIPS), preferring saccades over pointing and coupled with the frontal eye fields (FEF) at rest, and a more medial portion (mpIPS) intrinsically correlated to the dorsal premotor cortex (PMd). Dynamic causal modeling revealed feedforward-feedback loops linking lpIPS with FEF during saccades and mpIPS with PMd during pointing, with substantial differences between hand and foot. Despite an intrinsic specialization of the action-specific fronto-parietal networks, our study reveals that their functioning is finely regulated according to the effector to be used, being the dynamic interactions within those networks differently modulated when carrying out a similar movement (i.e. pointing) but with distinct effectors (i.e. hand and foot).

Task-based functional connectivity identifies two segregated networks underlying intentional action

While much of motor behavior is automatic, intentional action is necessary for the selection and initiation of controlled motor acts and is thus an essential part of goal-directed behavior. Neuroimaging studies have shown that self-generated action implicates several dorsal and ventral frontoparietal areas. However, knowledge of the functional coupling between these brain regions during intentional action remains limited. We here studied brain activations and functional connectivity (FC) of thirty right-handed healthy participants performing a finger pressing task instructed to use a specific finger (externally-triggered action) or to select one of four fingers randomly (internally-generated action). Participants performed the task in alternating order either with their dominant right hand or the left hand. Consistent with previous studies, we observed stronger involvement of posterior parietal cortex and premotor regions when contrasting internally-generated with externally-triggered action. Interestingly, this contrast also revealed significant engagement of medial occipitotemporal regions including the left lingual and right fusiform gyrus. Task-based FC analysis identified increased functional coupling among frontoparietal regions as well as increased and decreased coupling between occipitotemporal regions, thus differentiating between two segregated networks. When comparing results of the dominant and nondominant hand we found less activation, but stronger connectivity for the former, suggesting increased neural efficiency when participants use their dominant hand. Taken together, our results reveal that two segregated networks that encompass the frontoparietal and occipitotemporal cortex contribute independently to intentional action.

Distinct patterns of cortical manifold expansion and contraction underlie human sensorimotor adaptation

Sensorimotor learning is a dynamic, systems-level process that involves the combined action of multiple neural systems distributed across the brain. Although much is known about the specialized cortical systems that support specific components of action (such as reaching), we know less about how cortical systems function in a coordinated manner to facilitate adaptive behavior. To address this gap, our study measured human brain activity using functional MRI (fMRI) while participants performed a classic sensorimotor adaptation task and used a manifold learning approach to describe how behavioral changes during adaptation relate to changes in the landscape of cortical activity. During early adaptation, areas in the parietal and premotor cortices exhibited significant contraction along the cortical manifold, which was associated with their increased covariance with regions in the higher-order association cortex, including both the default mode and fronto-parietal networks. By contrast, during Late adaptation, when visuomotor errors had been largely reduced, a significant expansion of the visual cortex along the cortical manifold was associated with its reduced covariance with the association cortex and its increased intraconnectivity. Lastly, individuals who learned more rapidly exhibited greater covariance between regions in the sensorimotor and association cortices during early adaptation. These findings are consistent with a view that sensorimotor adaptation depends on changes in the integration and segregation of neural activity across more specialized regions of the unimodal cortex with regions in the association cortex implicated in higher-order processes. More generally, they lend support to an emerging line of evidence implicating regions of the default mode network (DMN) in task-based performance.

Parietofrontal oscillations show hand-specific interactions with top-down movement plans

To generate a hand-specific reach plan, the brain must integrate hand-specific signals with the desired movement strategy. Although various neurophysiology/imaging studies have investigated hand-target interactions in simple reach-to-target tasks, the whole brain timing and distribution of this process remain unclear, especially for more complex, instruction-dependent motor strategies. Previously, we showed that a pro/anti pointing instruction influences magnetoencephalographic (MEG) signals in frontal cortex that then propagate recurrently through parietal cortex (Blohm G, Alikhanian H, Gaetz W, Goltz HC, DeSouza JF, Cheyne DO, Crawford JD. NeuroImage 197: 306-319, 2019). Here, we contrasted left versus right hand pointing in the same task to investigate 1) which cortical regions of interest show hand specificity and 2) which of those areas interact with the instructed motor plan. Eight bilateral areas, the parietooccipital junction (POJ), superior parietooccipital cortex (SPOC), supramarginal gyrus (SMG), medial/anterior interparietal sulcus (mIPS/aIPS), primary somatosensory/motor cortex (S1/M1), and dorsal premotor cortex (PMd), showed hand-specific changes in beta band power, with four of these (M1, S1, SMG, aIPS) showing robust activation before movement onset. M1, SMG, SPOC, and aIPS showed significant interactions between contralateral hand specificity and the instructed motor plan but not with bottom-up target signals. Separate hand/motor signals emerged relatively early and lasted through execution, whereas hand-motor interactions only occurred close to movement onset. Taken together with our previous results, these findings show that instruction-dependent motor plans emerge in frontal cortex and interact recurrently with hand-specific parietofrontal signals before movement onset to produce hand-specific motor behaviors.NEW & NOTEWORTHY The brain must generate different motor signals depending on which hand is used. The distribution and timing of hand use/instructed motor plan integration are not understood at the whole brain level. Using MEG we show that different action planning subnetworks code for hand usage and integrating hand use into a hand-specific motor plan. The timing indicates that frontal cortex first creates a general motor plan and then integrates hand specificity to produce a hand-specific motor plan.

Action planning modulates the representation of object features in human fronto-parietal and occipital cortex

The visual cortex has been extensively studied to investigate its role in object recognition but to a lesser degree to determine how action planning influences the representation of objects' features. We used functional MRI and pattern classification methods to determine if during action planning, object features (orientation and location) could be decoded in an action‐dependent way. Sixteen human participants used their right dominant hand to perform movements (Align or Open reach) towards one of two 3D‐real oriented objects that were simultaneously presented and placed on either side of a fixation cross. While both movements required aiming towards target location, Align but not Open reach movements required participants to precisely adjust hand orientation. Therefore, we hypothesized that if the representation of object features is modulated by the upcoming action, pre‐movement activity pattern would allow more accurate dissociation between object features in Align than Open reach tasks. We found such dissociation in the anterior and posterior parietal cortex, as well as in the dorsal premotor cortex, suggesting that visuomotor processing is modulated by the upcoming task. The early visual cortex showed significant decoding accuracy for the dissociation between object features in the Align but not Open reach task. However, there was no significant difference between the decoding accuracy in the two tasks. These results demonstrate that movement‐specific preparatory signals modulate object representation in the frontal and parietal cortex, and to a lesser extent in the early visual cortex, likely through feedback functional connections.

Action goals and the praxis network: an fMRI study

The praxis representation network (PRN) of the left cerebral hemisphere is typically linked to the control of functional interactions with familiar tools. Surprisingly, little is known about the PRN engagement in planning and execution of tool-directed actions motivated by non-functional but purposeful action goals. Here we used functional neuroimaging to perform both univariate and multi-voxel pattern analyses (MVPA) in 20 right-handed participants who planned and later executed, with their dominant and non-dominant hands, disparate grasps of tools for different goals, including: (1) planning simple vs. demanding functional grasps of conveniently vs. inconveniently oriented tools with an intention to immediately use them, (2) planning simple—but non-functional—grasps of inconveniently oriented tools with a goal to pass them to a different person, (3) planning reaching movements directed at such tools with an intention to move/push them with the back of the hand, and (4) pantomimed execution of the earlier planned tasks. While PRN contributed to the studied interactions with tools, the engagement of its critical nodes, and/or complementary right hemisphere processing, was differently modulated by task type. E.g., planning non-functional/structural grasp-to-pass movements of inconveniently oriented tools, regardless of the hand, invoked the left parietal and prefrontal nodes significantly more than simple, non-demanding functional grasps. MVPA corroborated decoding capabilities of critical PRN areas and some of their right hemisphere counterparts. Our findings shed new lights on how performance of disparate action goals influences the extraction of object affordances, and how or to what extent it modulates the neural activity within the parieto-frontal brain networks.

Are reaching and grasping effector-independent? Similarities and differences in reaching and grasping kinematics between the hand and foot

While reaching and grasping are highly prevalent manual actions, neuroimaging studies provide evidence that their neural representations may be shared between different body parts, i.e., effectors. If these actions are guided by effector-independent mechanisms, similar kinematics should be observed when the action is performed by the hand or by a cortically remote and less experienced effector, such as the foot. We tested this hypothesis with two characteristic components of action: the initial ballistic stage of reaching, and the preshaping of the digits during grasping based on object size. We examined if these kinematic features reflect effector-independent mechanisms by asking participants to reach toward and to grasp objects of different widths with their hand and foot. First, during both reaching and grasping, the velocity profile up to peak velocity matched between the hand and the foot, indicating a shared ballistic acceleration phase. Second, maximum grip aperture and time of maximum grip aperture of grasping increased with object size for both effectors, indicating encoding of object size during transport. Differences between the hand and foot were found in the deceleration phase and time of maximum grip aperture, likely due to biomechanical differences and the participants’ inexperience with foot actions. These findings provide evidence for effector-independent visuomotor mechanisms of reaching and grasping that generalize across body parts.

Same Action, Different Meaning: Neural Substrates of Action Semantic Meaning

Voluntary actions are shaped by desired goals and internal intentions. Multiple factors, including the planning of subsequent actions and the expectation of sensory outcome, were shown to modulate kinetics and neural activity patterns associated with similar goal-directed actions. Notably, in many real-world tasks, actions can also vary across the semantic meaning they convey, although little is known about how semantic meaning modulates associated neurobehavioral measures. Here, we examined how behavioral and functional magnetic resonance imaging measures are modulated when subjects execute similar actions (button presses) for two different semantic meanings-to answer "yes" or "no" to a binary question. Our findings reveal that, when subjects answer using their right hand, the two semantic meanings are differentiated based on voxel patterns in the frontoparietal cortex and lateral-occipital complex bilaterally. When using their left hand, similar regions were found, albeit only with a more liberal threshold. Although subjects were faster to answer "yes" versus "no" when using their right hand, the neural differences cannot be explained by these kinetic differences. To the best of our knowledge, this is the first evidence showing that semantic meaning is embedded in the neural representation of actions, independent of alternative modulating factors such as kinetic and sensory features.

Motor planning brings human primary somatosensory cortex into action-specific preparatory states

Motor planning plays a critical role in producing fast and accurate movement. Yet, the neural processes that occur in human primary motor and somatosensory cortex during planning, and how they relate to those during movement execution, remain poorly understood. Here, we used 7T functional magnetic resonance imaging and a delayed movement paradigm to study single finger movement planning and execution. The inclusion of no-go trials and variable delays allowed us to separate what are typically overlapping planning and execution brain responses. Although our univariate results show widespread deactivation during finger planning, multivariate pattern analysis revealed finger-specific activity patterns in contralateral primary somatosensory cortex (S1), which predicted the planned finger action. Surprisingly, these activity patterns were as informative as those found in contralateral primary motor cortex (M1). Control analyses ruled out the possibility that the detected information was an artifact of subthreshold movements during the preparatory delay. Furthermore, we observed that finger-specific activity patterns during planning were highly correlated to those during execution. These findings reveal that motor planning activates the specific S1 and M1 circuits that are engaged during the execution of a finger press, while activity in both regions is overall suppressed. We propose that preparatory states in S1 may improve movement control through changes in sensory processing or via direct influence of spinal motor neurons.

Exploring Cognition with Brain-Machine Interfaces

Traditional brain-machine interfaces decode cortical motor commands to control external devices. These commands are the product of higher-level cognitive processes, occurring across a network of brain areas, that integrate sensory information, plan upcoming motor actions, and monitor ongoing movements. We review cognitive signals recently discovered in the human posterior parietal cortex during neuroprosthetic clinical trials. These signals are consistent with small regions of cortex having a diverse role in cognitive aspects of movement control and body monitoring, including sensorimotor integration, planning, trajectory representation, somatosensation, action semantics, learning, and decision making. These variables are encoded within the same population of cells using structured representations that bind related sensory and motor variables, an architecture termed partially mixed selectivity. Diverse cognitive signals provide complementary information to traditional motor commands to enable more natural and intuitive control of external devices.

Motor impairment evoked by direct electrical stimulation of human parietal cortex during object manipulation

In primates, the parietal cortex plays a crucial role in hand-object manipulation. However, its involvement in object manipulation and related hand-muscle control has never been investigated in humans with a direct and focal electrophysiological approach. To this aim, during awake surgery for brain tumors, we studied the impact of direct electrical stimulation (DES) of parietal lobe on hand-muscles during a hand-manipulation task (HMt). Results showed that DES applied to fingers-representation of postcentral gyrus (PCG) and anterior intraparietal cortex (aIPC) impaired HMt execution. Different types of EMG-interference patterns were observed ranging from a partial (task-clumsy) or complete (task-arrest) impairment of muscles activity. Within PCG both patterns coexisted along a medio (arrest)-lateral (clumsy) distribution, while aIPC hosted preferentially the task-arrest. The interference patterns were mainly associated to muscles suppression, more pronounced in aIPC with respect to PCG. Moreover, within PCG were observed patterns with different level of muscle recruitment, not reported in the aIPC. Overall, EMG-interference patterns and their probabilistic distribution suggested the presence of different functional parietal sectors, possibly playing different roles in hand-muscle control during manipulation. We hypothesized that task-arrest, compared to clumsy patterns, might suggest the existence of parietal sectors more closely implicated in shaping the motor output.

Unraveling somatotopic organization in the human brain using machine learning and adaptive supervoxel-based parcellations

In addition to the well-established somatotopy in the pre- and post-central gyrus, there is now strong evidence that somatotopic organization is evident across other regions in the sensorimotor network. This raises several experimental questions: To what extent is activity in the sensorimotor network effector-dependent and effector-independent? How important is the sensorimotor cortex when predicting the motor effector? Is there redundancy in the distributed somatotopically organized network such that removing one region has little impact on classification accuracy? To answer these questions, we developed a novel experimental approach. fMRI data were collected while human subjects performed a precisely controlled force generation task separately with their hand, foot, and mouth. We used a simple linear iterative clustering (SLIC) algorithm to segment whole-brain beta coefficient maps to build an adaptive brain parcellation and then classified effectors using extreme gradient boosting (XGBoost) based on parcellations at various spatial resolutions. This allowed us to understand how data-driven adaptive brain parcellation granularity altered classification accuracy. Results revealed effector-dependent activity in regions of the post-central gyrus, precentral gyrus, and paracentral lobule. SMA, regions of the inferior and superior parietal lobule, and cerebellum each contained effector-dependent and effector-independent representations. Machine learning analyses showed that increasing the spatial resolution of the data-driven model increased classification accuracy, which reached 94% with 1755 supervoxels. Our SLIC-based supervoxel parcellation outperformed classification analyses using established brain templates and random simulations. Occlusion experiments further demonstrated redundancy across the sensorimotor network when classifying effectors. Our observations extend our understanding of effector-dependent and effector-independent organization within the human brain and provide new insight into the functional neuroanatomy required to predict the motor effector used in a motor control task.

Multivariate Analysis of Electrophysiological Signals Reveals the Time Course of Precision Grasps Programs: Evidence for Nonhierarchical Evolution of Grasp Control

Current understanding of the neural processes underlying human grasping suggests that grasp computations involve gradients of higher to lower level representations and, relatedly, visual to motor processes. However, it is unclear whether these processes evolve in a strictly canonical manner from higher to intermediate and to lower levels given that this knowledge importantly relies on functional imaging, which lacks temporal resolution. To examine grasping in fine temporal detail here we used multivariate EEG analysis. We asked participants to grasp objects while controlling the time at which crucial elements of grasp programs were specified. We first specified the orientation with which participants should grasp objects, and only after a delay we instructed participants about which effector to use to grasp, either the right or the left hand. We also asked participants to grasp with both hands because bimanual and left-hand grasping share intermediate-level grasp representations. We observed that grasp programs evolved in a canonical manner from visual representations, which were independent of effectors to motor representations that distinguished between effectors. However, we found that intermediate representations of effectors that partially distinguished between effectors arose after representations that distinguished among all effector types. Our results show that grasp computations do not proceed in a strictly hierarchically canonical fashion, highlighting the importance of the fine temporal resolution of EEG for a comprehensive understanding of human grasp control.SIGNIFICANCE STATEMENT A long-standing assumption of the grasp computations is that grasp representations progress from higher to lower level control in a regular, or canonical, fashion. Here, we combined EEG and multivariate pattern analysis to characterize the temporal dynamics of grasp representations while participants viewed objects and were subsequently cued to execute an unimanual or bimanual grasp. Interrogation of the temporal dynamics revealed that lower level effector representations emerged before intermediate levels of grasp representations, thereby suggesting a partially noncanonical progression from higher to lower and then to intermediate level grasp control.

Hierarchical Decoding Model of Upper Limb Movement Intention From EEG Signals Based on Attention State Estimation

Decoding the motion intention of the human upper limb from electroencephalography (EEG) signals has important practical values. However, existing decoding models are built under the attended state while subjects perform motion tasks. In practice, people are often distracted by other tasks or environmental factors, which may impair decoding performance. To address this problem, in this paper, we propose a hierarchical decoding model of human upper limb motion intention from EEG signals based on attention state estimation. The proposed decoding model includes two components. First, the attention state detection (ASD) component estimates the attention state during the upper limb movement. Next, the motion intention recognition (MIR) component decodes the motion intention by using the decoding models built under the attended and distracted states. The experimental results show that the proposed hierarchical decoding model performs well under the attended and distracted states. This work can advance the application of human movement intention decoding and provides new insights into the study of brain-machine interfaces.

Decoding attempted phantom hand movements from ipsilateral sensorimotor areas after amputation

Objective.The sensorimotor cortex is often selected as target in the development of a Brain-Computer Interface, as activation patterns from this region can be robustly decoded to discriminate between different movements the user executes. Up until recently, such BCIs were primarily based on activity in the contralateral hemisphere, where decoding movements still works even years after denervation. However, there is increasing evidence for a role of the sensorimotor cortex in controlling the ipsilateral body. The aim of this study is to investigate the effects of denervation on the movement representation on the ipsilateral sensorimotor cortex.Approach.Eight subjects with acquired above-elbow arm amputation and nine controls performed a task in which they made (or attempted to make with their phantom hand) six different gestures from the American Manual Alphabet. Brain activity was measured using 7T functional MRI, and a classifier was trained to discriminate between activation patterns on four different regions of interest (ROIs) on the ipsilateral sensorimotor cortex.Main results.Classification scores showed that decoding was possible and significantly better than chance level for both the phantom and intact hands from all ROIs. Decoding both the left (intact) and right (phantom) hand from the same hemisphere was also possible with above-chance level classification score.Significance.The possibility to decode both hands from the same hemisphere, even years after denervation, indicates that implantation of motor-electrodes for BCI control possibly need only cover a single hemisphere, making surgery less invasive, and increasing options for people with lateralized damage to motor cortex like after stroke.

Two Distinct Systems Represent Contralateral and Ipsilateral Sensorimotor Processes in the Human Premotor Cortex: A Dense TMS Mapping Study

Animal brains contain behaviorally committed representations of the surrounding world, which integrate sensory and motor information. In primates, sensorimotor mechanisms reside in part in the premotor cortex (PM), where sensorimotor neurons are topographically clustered according to functional specialization. Detailed functional cartography of the human PM is still under investigation. We explored the topographic distribution of spatially dependent sensorimotor functions in healthy volunteers performing left or right, hand or foot, responses to visual cues presented in the left or right hemispace, thus combining independently stimulus side, effector side, and effector type. Event-related transcranial magnetic stimulation was applied to single spots of a dense grid of 10 points on the participants' left hemiscalp, covering the whole PM. Results showed: (1) spatially segregated hand and foot representations, (2) focal representations of contralateral cues and movements in the dorsal PM, and (3) distributed representations of ipsilateral cues and movements in the ventral and dorso-medial PM. The present novel causal information indicates that (1) the human PM is somatotopically organized and (2) the left PM contains sensory-motor representations of both hemispaces and of both hemibodies, but the hemispace and hemibody contralateral to the PM are mapped on a distinct, nonoverlapping cortical region compared to the ipsilateral ones.

Valid and powerful second-level group statistics for decoding accuracy: Information prevalence inference using the i-th order statistic (i-test)

In functional magnetic resonance imaging (fMRI) decoding studies using pattern classification, a second-level group statistical test is typically performed after first-level decoding analyses for individual participants. In the second-level test, the mean decoding accuracy across participants is often tested against the chance-level accuracy (for example, one-sample Student t-test) to check whether information about the label, such as, experimental condition or cognitive content, is included in brain activation. Meanwhile, Allefeld et al., (2016) highlighted that significant results for such tests only indicate that "there are some people in the population whose fMRI data carry information about the experimental condition." Therefore, such tests failed to conclude whether the effect is typical in the population. Based on this argument, they proposed an alternative method implementing the prevalence inference. In the present study, that method is extended to propose a novel statistical test called as the "information prevalence inference using the i-th order statistic" (i-test). The i-test has a high statistical power compared with the method proposed in Allefeld et al., (2016) and provides an inference regarding the typical effect in the population. In the i-test, the i-th lowest sample decoding accuracy (the i-th order statistic) is compared to the null distribution to verify whether the proportion of higher-than-chance decoding accuracy in the population (information prevalence) is higher than the threshold. Hence, a significant result in the i-test is interpreted as a majority of the population has information about the label in the brain. Theoretical details of the i-test are provided, its high statistical power is identified by numerical calculation, and the application of this method in an fMRI decoding is demonstrated.

Neural encoding and functional interactions underlying pantomimed movements

Pantomimes are a unique movement category which can convey complex information about our intentions in the absence of any interaction with real objects. Indeed, we can pretend to use the same tool to perform different actions or to achieve the same goal adopting different tools. Nevertheless, how our brain implements pantomimed movements is still poorly understood. In our study, we explored the neural encoding and functional interactions underlying pantomimes adopting multivariate pattern analysis (MVPA) and connectivity analysis of fMRI data. Participants performed pantomimed movements, either grasp-to-move or grasp-to-use, as if they were interacting with two different tools (scissors or axe). These tools share the possibility to achieve the same goal. We adopted MVPA to investigate two levels of representation during the planning and execution of pantomimes: (1) distinguishing different actions performed with the same tool, (2) representing the same final goal irrespective of the adopted tool. We described widespread encoding of action information within regions of the so-called “tool” network. Several nodes of the network—comprising regions within the ventral and the dorsal stream—also represented goal information. The spatial distribution of goal information changed from planning—comprising posterior regions (i.e. parietal and temporal)—to execution—including also anterior regions (i.e. premotor cortex). Moreover, connectivity analysis provided evidence for task-specific bidirectional coupling between the ventral stream and parieto-frontal motor networks. Overall, we showed that pantomimes were characterized by specific patterns of action and goal encoding and by task-dependent cortical interactions.

Human Somatosensory Cortex Is Modulated during Motor Planning

Recent data and motor control theory argues that movement planning involves preparing the neural state of primary motor cortex (M1) for forthcoming action execution. Theories related to internal models, feedback control, and predictive coding also emphasize the importance of sensory prediction (and processing) before (and during) the movement itself, explaining why motor-related deficits can arise from damage to primary somatosensory cortex (S1). Motivated by this work, here we examined whether motor planning, in addition to changing the neural state of M1, changes the neural state of S1, preparing it for the sensory feedback that arises during action. We tested this idea in two human functional MRI studies (N = 31, 16 females) involving delayed object manipulation tasks, focusing our analysis on premovement activity patterns in M1 and S1. We found that the motor effector to be used in the upcoming action could be decoded, well before movement, from neural activity in M1 in both studies. Critically, we found that this effector information was also present, well before movement, in S1. In particular, we found that the encoding of effector information in area 3b (S1 proper) was linked to the contralateral hand, similarly to that found in M1, whereas in areas 1 and 2 this encoding was present in both the contralateral and ipsilateral hemispheres. Together, these findings suggest that motor planning not only prepares the motor system for movement but also changes the neural state of the somatosensory system, presumably allowing it to anticipate the sensory information received during movement.SIGNIFICANCE STATEMENT Whereas recent work on motor cortex has emphasized the critical role of movement planning in preparing neural activity for movement generation, it has not investigated the extent to which planning also modulates the activity in the adjacent primary somatosensory cortex. This reflects a key gap in knowledge, given that recent motor control theories emphasize the importance of sensory feedback processing in effective movement generation. Here, we find through a convergence of experiments and analyses, that the planning of object manipulation tasks, in addition to modulating the activity in the motor cortex, changes the state of neural activity in different subfields of the human S1. We suggest that this modulation prepares the S1 for the sensory information it will receive during action execution.

Neural Correlates of Hand-Object Congruency Effects during Action Planning

Selecting hand actions to manipulate an object is affected both by perceptual factors and by action goals. Affordances may contribute to "stimulus-response" congruency effects driven by habitual actions to an object. In previous studies, we have demonstrated an influence of the congruency between hand and object orientations on response times when reaching to turn an object, such as a cup. In this study, we investigated how the representation of hand postures triggered by planning to turn a cup was influenced by this congruency effect, in an fMRI scanning environment. Healthy participants were asked to reach and turn a real cup that was placed in front of them either in an upright orientation or upside-down. They were instructed to use a hand orientation that was either congruent or incongruent with the cup orientation. As expected, the motor responses were faster when the hand and cup orientations were congruent. There was increased activity in a network of brain regions involving object-directed actions during action planning, which included bilateral primary and extrastriate visual, medial, and superior temporal areas, as well as superior parietal, primary motor, and premotor areas in the left hemisphere. Specific activation of the dorsal premotor cortex was associated with hand-object orientation congruency during planning and prior to any action taking place. Activity in that area and its connectivity with the lateral occipito-temporal cortex increased when planning incongruent (goal-directed) actions. The increased activity in premotor areas in trials where the orientation of the hand was incongruent to that of the object suggests a role in eliciting competing representations specified by hand postures in lateral occipito-temporal cortex.

The integrative role of the M1 in motor sequence learning

The primary motor cortex (M1) is crucial in motor learning. Whether the M1 encodes the motor engram for sequential finger tapping formed by an emphasis on speed is still inconclusive. The active states of engrams are hard to discriminate from the motor execution per se. As preparatory activity reflects the upcoming movement parameters, we hypothesized that the retrieval of motor engrams generated by different learning modes is reflected as a learning-related increase in the preparatory activity of the M1. To test this hypothesis, we evaluated the preparatory activity during the learning of sequential finger-tapping with the non-dominant left hand using a 7T functional MRI. Participants alternated between performing a tapping sequence as quickly as possible (maximum mode) or at a constant speed of 2 Hz paced by a sequence-specifying visual cue (constant mode). We found a training-related increase in preparatory activity in the network covering the bilateral anterior intraparietal sulcus and inferior parietal lobule extending to the right M1 during the maximum mode and the right M1 during the constant mode. These findings indicate that the M1, as the last effector of the motor output, integrates the motor engram distributed through the networks despite training mode differences.

Knowing how to do it or doing it? A double dissociation between tool-gesture production and tool-gesture knowledge

Deciding how to manipulate an object to fulfill a goal requires accessing different types of object-related information. How these different types of information are integrated and represented in the brain is still an open question. Here, we focus on examining two types of object-related information-tool-gesture knowledge (i.e., how to manipulate an object), and tool-gesture production (i.e., the actual manipulation of an object). We show a double dissociation between tool-gesture knowledge and tool-gesture production: Patient FP presents problems in pantomiming tool use in the context of a spared ability to perform judgments about an object's manipulation, whereas Patient LS can pantomime tool use, but is impaired at performing manipulation judgments. Moreover, we compared the location of the lesions in FP and LS with those sustained by two classic ideomotor apraxic patients (IMA), using a cortical thickness approach. Patient FP presented lesions in common with our classic IMA that included the left inferior parietal lobule (IPL), and specifically the supramarginal gyrus, the left parietal operculum, the left premotor cortex and the left inferior frontal gyrus, whereas Patient LS and our classic IMA patients presented common lesions in regions of the superior parietal lobule (SPL), motor areas (as primary somatosensory cortex, premotor cortex and primary motor cortex), and frontal areas. Our results show that tool-gesture production and tool-gesture knowledge can be behaviorally and neurally doubly dissociated and put strong constraints on extant theories of action and object recognition and use.

Motor Planning Modulates Neural Activity Patterns in Early Human Auditory Cortex

It is well established that movement planning recruits motor-related cortical brain areas in preparation for the forthcoming action. Given that an integral component to the control of action is the processing of sensory information throughout movement, we predicted that movement planning might also modulate early sensory cortical areas, readying them for sensory processing during the unfolding action. To test this hypothesis, we performed 2 human functional magnetic resonance imaging studies involving separate delayed movement tasks and focused on premovement neural activity in early auditory cortex, given the area's direct connections to the motor system and evidence that it is modulated by motor cortex during movement in rodents. We show that effector-specific information (i.e., movements of the left vs. right hand in Experiment 1 and movements of the hand vs. eye in Experiment 2) can be decoded, well before movement, from neural activity in early auditory cortex. We find that this motor-related information is encoded in a separate subregion of auditory cortex than sensory-related information and is present even when movements are cued visually instead of auditorily. These findings suggest that action planning, in addition to preparing the motor system for movement, involves selectively modulating primary sensory areas based on the intended action.

Interhemispheric interactions between the right angular gyrus and the left motor cortex: a transcranial magnetic stimulation study

The interconnection of the angular gyrus of right posterior parietal cortex (PPC) and the left motor cortex (LM1) is essential for goal-directed hand movements. Previous work with transcranial magnetic stimulation (TMS) showed that right PPC stimulation increases LM1 excitability, but right PPC followed by left PPC-LM1 stimulation (LPPC-LM1) inhibits LM1 corticospinal output compared with LPPC-LM1 alone. It is not clear if right PPC-mediated inhibition of LPPC-LM1 is due to inhibition of left PPC or to combined effects of right and left PPC stimulation on LM1 excitability. We used paired-pulse TMS to study the extent to which combined right and left PPC stimulation, targeting the angular gyri, influences LM1 excitability. We tested 16 healthy subjects in five paired-pulsed TMS experiments using MRI-guided neuronavigation to target the angular gyri within PPC. We tested the effects of different right angular gyrus (RAG) and LM1 stimulation intensities on the influence of RAG on LM1 and on influence of left angular gyrus (LAG) on LM1 (LAG-LM1). We then tested the effects of RAG and LAG stimulation on LM1 short-interval intracortical facilitation (SICF), short-interval intracortical inhibition (SICI), and long-interval intracortical inhibition (LICI). The results revealed that RAG facilitated LM1, inhibited SICF, and inhibited LAG-LM1. Combined RAG-LAG stimulation did not affect SICI but increased LICI. These experiments suggest that RAG-mediated inhibition of LAG-LM1 is related to inhibition of early indirect (I)-wave activity and enhancement of GABA_B receptor-mediated inhibition in LM1. The influence of RAG on LM1 likely involves ipsilateral connections from LAG to LM1 and heterotopic connections from RAG to LM1.NEW & NOTEWORTHY Goal-directed hand movements rely on the right and left angular gyri (RAG and LAG) and motor cortex (M1), yet how these brain areas functionally interact is unclear. Here, we show that RAG stimulation facilitated right hand motor output from the left M1 but inhibited indirect (I)-waves in M1. Combined RAG and LAG stimulation increased GABA_B, but not GABA_A, receptor-mediated inhibition in left M1. These findings highlight unique brain interactions between the RAG and left M1.

Reduced Facilitation of Parietal-Motor Functional Connections in Older Adults

Age-related changes in cortico-cortical connectivity in the human motor network in older adults are associated with declines in hand dexterity. Posterior parietal cortex (PPC) is strongly interconnected with motor areas and plays a critical role in many aspects of motor planning. Functional connectivity measures derived from dual-site transcranial magnetic stimulation (dsTMS) studies have found facilitatory inputs from PPC to ipsilateral primary motor cortex (M1) in younger adults. In this study, we investigated whether facilitatory inputs from PPC to M1 are altered by age. We used dsTMS in a conditioning-test paradigm to characterize patterns of functional connectivity between the left PPC and ipsilateral M1 and a standard pegboard test to assess skilled hand motor function in 13 young and 13 older adults. We found a PPC-M1 facilitation in young adults but not older adults. Older adults also showed a decline in motor performance compared to young adults. We conclude that the reduced PPC-M1 facilitation in older adults may be an early marker of age-related decline in the neural control of movement.

Assessing the effective connectivity of premotor areas during real vs imagined grasping: a DCM-PEB approach

The parieto-frontal circuit underlying grasping, which requires the serial involvement of the anterior intraparietal area (aIPs) and the ventral premotor cortex (PMv), has been recently extended enlightening the role of the dorsal premotor cortex (PMd). The supplementary motor area (SMA) has been also suggested to encode grip force for grasping actions; furthermore, both PMd and SMA are known to play a crucial role in motor imagery. Here, we aimed at assessing the dynamic couplings between left aIPs, PMv, PMd, SMA and primary motor cortex (M1) by comparing executed and imagined right-hand grasping, using Dynamic Causal Modelling (DCM) and Parametrical Empirical Bayes (PEB) analyses. 24 subjects underwent an fMRI exam (3T) during which they were asked to perform or imagine a grasping movement visually cued by photographs of commonly used objects. We tested whether the two conditions a) exert a modulatory effect on both forward and feedback couplings among our areas of interest, and b) differ in terms of strength and sign of these parameters. Results of the real condition confirmed the serial involvement of aIPs, PMv and M1. PMv also exerted a positive influence on PMd and SMA, but received an inhibitory feedback only from PMd. Our results suggest that a general motor program for grasping is planned by the aIPs-PMv circuit; then, PMd and SMA encode high-level features of the movement. During imagery, the connection strength from aIPs to PMv was weaker and the information flow stopped in PMv; thus, a less complex motor program was planned. Moreover, results suggest that SMA and PMd cooperate to prevent motor execution. In conclusion, the comparison between execution and imagery reveals that during grasping premotor areas dynamically interplay in different ways, depending on task demands.

Evidence for an effector-independent action system from people born without hands

What are the principles of brain organization? In the motor domain, separate pathways were found for reaching and grasping actions performed by the hand. To what extent is this organization specific to the hand or based on abstract action types, regardless of which body part performs them? We tested people born without hands who perform actions with their feet. Activity in frontoparietal association motor areas showed preference for an action type (reaching or grasping), regardless of whether it was performed by the foot in people born without hands or by the hand in typically-developed controls. These findings provide evidence that some association areas are organized based on abstract functions of action types, independent of specific sensorimotor experience and parameters of specific body parts.

Many parts of the visuomotor system guide daily hand actions, like reaching for and grasping objects. Do these regions depend exclusively on the hand as a specific body part whose movement they guide, or are they organized for the reaching task per se, for any body part used as an effector? To address this question, we conducted a neuroimaging study with people born without upper limbs—individuals with dysplasia—who use the feet to act, as they and typically developed controls performed reaching and grasping actions with their dominant effector. Individuals with dysplasia have no prior experience acting with hands, allowing us to control for hand motor imagery when acting with another effector (i.e., foot). Primary sensorimotor cortices showed selectivity for the hand in controls and foot in individuals with dysplasia. Importantly, we found a preference based on action type (reaching/grasping) regardless of the effector used in the association sensorimotor cortex, in the left intraparietal sulcus and dorsal premotor cortex, as well as in the basal ganglia and anterior cerebellum. These areas also showed differential response patterns between action types for both groups. Intermediate areas along a posterior–anterior gradient in the left dorsal premotor cortex gradually transitioned from selectivity based on the body part to selectivity based on the action type. These findings indicate that some visuomotor association areas are organized based on abstract action functions independent of specific sensorimotor parameters, paralleling sensory feature-independence in visual and auditory cortices in people born blind and deaf. Together, they suggest association cortices across action and perception may support specific computations, abstracted from low-level sensorimotor elements.

Selective Modulation of Early Visual Cortical Activity by Movement Intention

The primate visual system contains myriad feedback projections from higher- to lower-order cortical areas, an architecture that has been implicated in the top-down modulation of early visual areas during working memory and attention. Here we tested the hypothesis that these feedback projections also modulate early visual cortical activity during the planning of visually guided actions. We show, across three separate human functional magnetic resonance imaging (fMRI) studies involving object-directed movements, that information related to the motor effector to be used (i.e., limb, eye) and action goal to be performed (i.e., grasp, reach) can be selectively decoded-prior to movement-from the retinotopic representation of the target object(s) in early visual cortex. We also find that during the planning of sequential actions involving objects in two different spatial locations, that motor-related information can be decoded from both locations in retinotopic cortex. Together, these findings indicate that movement planning selectively modulates early visual cortical activity patterns in an effector-specific, target-centric, and task-dependent manner. These findings offer a neural account of how motor-relevant target features are enhanced during action planning and suggest a possible role for early visual cortex in instituting a sensorimotor estimate of the visual consequences of movement.

The Motor Cortex Has Independent Representations for Ipsilateral and Contralateral Arm Movements But Correlated Representations for Grasping

Motor commands for the arm and hand generally arise from the contralateral motor cortex, where most of the relevant corticospinal tract originates. However, the ipsilateral motor cortex shows activity related to arm movement despite the lack of direct connections. The extent to which the activity related to ipsilateral movement is independent from that related to contralateral movement is unclear based on conflicting conclusions in prior work. Here we investigate bilateral arm and hand movement tasks completed by two human subjects with intracortical microelectrode arrays implanted in the left hand and arm area of the motor cortex. Neural activity was recorded while they attempted to perform arm and hand movements in a virtual environment. This enabled us to quantify the strength and independence of motor cortical activity related to continuous movements of each arm. We also investigated the subjects' ability to control both arms through a brain-computer interface. Through a number of experiments, we found that ipsilateral arm movement was represented independently of, but more weakly than, contralateral arm movement. However, the representation of grasping was correlated between the two hands. This difference between hand and arm representation was unexpected and poses new questions about the different ways the motor cortex controls the hands and arms.

Primary motor cortical activity during unimanual movements with increasing demand on precision

In functional magnetic resonance imaging (fMRI) studies, performance of unilateral hand movements is associated with primary motor cortex activity ipsilateral to the moving hand (M1_ipsi), in addition to contralateral activity (M1_contra). The magnitude of M1_ipsi activity increases with the demand on precision of the task. However, it is unclear how demand-dependent increases in M1_ipsi recruitment relate to the control of hand movements. To address this question, we used fMRI to measure blood oxygenation level-dependent (BOLD) activity during performance of a task that varied in demand on precision. Participants (n = 23) manipulated an MRI-compatible joystick with their right or left hand to move a cursor into targets of different sizes (small, medium, large, extra large). Performance accuracy, movement time, and number of velocity peaks scaled with target size, whereas reaction time, maximum velocity, and initial direction error did not. In the univariate analysis, BOLD activation in M1_contra and M1_ipsi was higher for movements to smaller targets. Representational similarity analysis, corrected for mean activity differences, revealed multivoxel BOLD activity patterns during movements to small targets were most similar to those for medium targets and least similar to those for extra-large targets. Only models that varied with demand (target size, performance accuracy, and number of velocity peaks) correlated with the BOLD dissimilarity patterns, though differently for right and left hands. Across individuals, M1_contra and M1_ipsi similarity patterns correlated with each other. Together, these results suggest that increasing demand on precision in a unimanual motor task increases M1 activity and modulates M1 activity patterns.NEW & NOTEWORTHY Contralateral primary motor cortex (M1) predominantly controls unilateral hand movements, but the role of ipsilateral M1 is unclear. We used functional magnetic resonance imaging (fMRI) to investigate how M1 activity is modulated by unimanual movements at different levels of demand on precision. Our results show that task characteristics related to demand on precision influence bilateral M1 activity, suggesting that in addition to contralateral M1, ipsilateral M1 plays a key role in controlling hand movements to meet performance precision requirements.

The Topography of Visually Guided Grasping in the Premotor Cortex: A Dense-Transcranial Magnetic Stimulation (TMS) Mapping Study

Visuomotor transformations at the cortical level occur along a network where posterior parietal regions are connected to homologous premotor regions. Grasping-related activity is represented in a diffuse, ventral and dorsal system in the posterior parietal regions, but no systematic causal description of a premotor counterpart of a similar diffuse grasping representation is available. To fill this gap, we measured the kinematics of right finger movements in 17 male and female human participants during grasping of three objects of different sizes. Single-pulse transcranial magnetic stimulation was applied 100 ms after visual presentation of the object over a regular grid of 8 spots covering the left premotor cortex (PMC) and 2 Sham stimulations. Maximum finger aperture during reach was used as the feature to classify object size in different types of classifiers. Classification accuracy was taken as a measure of the efficiency of visuomotor transformations for grasping. Results showed that transcranial magnetic stimulation reduced classification accuracy compared with Sham stimulation when it was applied to 2 spots in the ventral PMC and 1 spot in the medial PMC, corresponding approximately to the ventral PMC and the dorsal portion of the supplementary motor area. Our results indicate a multifocal representation of object geometry for grasping in the PMC that matches the known multifocal parietal maps of grasping representations. Additionally, we confirm that, by applying a uniform spatial sampling procedure, transcranial magnetic stimulation can produce cortical functional maps independent of a priori spatial assumptions.SIGNIFICANCE STATEMENT Visually guided actions activate a large frontoparietal network. Here, we used a dense grid of transcranial magnetic stimulation spots covering the whole premotor cortex (PMC), to identify with accurate spatial mapping the functional specialization of the human PMC during grasping movement. Results corroborate previous findings about the role of the ventral PMC in preshaping the fingers according to the size of the target. Crucially, we found that the medial part of PMC, putatively covering the supplementary motor area, plays a direct role in object grasping. In concert with findings in nonhuman primates, these results indicate a multifocal representation of object geometry for grasping in the PMC and expand our understanding of how our brain integrates visual and motor information to perform visually guided actions.

A Revised Computational Neuroanatomy for Motor Control

We discuss a new framework for understanding the structure of motor control. Our approach integrates existing models of motor control with the reality of hierarchical cortical processing and the parallel segregated loops that characterize cortical-subcortical connections. We also incorporate the recent claim that cortex functions via predictive representation and optimal information utilization. Our framework assumes that each cortical area engaged in motor control generates a predictive model of a different aspect of motor behavior. In maintaining these predictive models, each area interacts with a different part of the cerebellum and BG. These subcortical areas are thus engaged in domain-appropriate system identification and optimization. This refocuses the question of division of function among different cortical areas. What are the different aspects of motor behavior that are predictively modeled? We suggest that one fundamental division is between modeling of task and body whereas another is the model of state and action. Thus, we propose that the posterior parietal cortex, somatosensory cortex, premotor cortex, and motor cortex represent task state, body state, task action, and body action, respectively. In the second part of this review, we demonstrate how this division of labor can better account for many recent findings of movement encoding, especially in the premotor and posterior parietal cortices.

Decoding Changes of Mind in Voluntary Action-Dynamics of Intentional Choice Representations

Voluntary actions rely on appropriate flexibility of intentions. Usually, we should pursue our goals, but sometimes we should change goals if they become too costly to achieve. Using functional magnetic resonance imaging, we investigated the neural dynamics underlying the capacity to change one's mind based on new information after action onset. Multivariate pattern analyses revealed that in visual areas, neural representations of intentional choice between 2 visual stimuli were unchanged by additional decision-relevant information. However, in fronto-parietal cortex, representations changed dynamically as decisions evolved. Precuneus, angular gyrus, and dorsolateral prefrontal cortex encoded new externally cued rewards/costs that guided subsequent changes of mind. Activity in medial frontal cortex predicted changes of mind when participants detached from externally cued evidence, suggesting a role in endogenous decision updates. Finally, trials with changes of mind were associated with an increase in functional connectivity between fronto-parietal areas, allowing for integration of various endogenous and exogenous decision components to generate a distributed consensus about whether to pursue or abandon an initial intention. In conclusion, local and global dynamics of choice representations in fronto-parietal cortex allow agents to maintain the balance between adapting to changing environments versus pursuing internal goals.

Hierarchical Action Encoding Within the Human Brain

Humans are able to interact with objects with extreme flexibility. To achieve this ability, the brain does not only control specific muscular patterns, but it also needs to represent the abstract goal of an action, irrespective of its implementation. It is debated, however, how abstract action goals are implemented in the brain. To address this question, we used multivariate pattern analysis of functional magnetic resonance imaging data. Human participants performed grasping actions (precision grip, whole hand grip) with two different wrist orientations (canonical, rotated), using either the left or right hand. This design permitted to investigate a hierarchical organization consisting of three levels of abstraction: 1) “concrete action” encoding; 2) “effector-dependent goal” encoding (invariant to wrist orientation); and 3) “effector-independent goal” encoding (invariant to effector and wrist orientation). We found that motor cortices hosted joint encoding of concrete actions and of effector-dependent goals, while the parietal lobe housed a convergence of all three representations, comprising action goals within and across effectors. The left lateral occipito-temporal cortex showed effector-independent goal encoding, but no convergence across the three levels of representation. Our results support a hierarchical organization of action encoding, shedding light on the neural substrates supporting the extraordinary flexibility of human hand behavior.

Frequency-Specific Regional Homogeneity Alterations in Tourette Syndrome

Tourette syndrome (TS) is a developmental neuropsychiatric disorder with onset during childhood. Because of its complex spectrum of phenotypes, the underlying pathophysiology of TS is still unclear. Resting-state functional magnetic resonance imaging demonstrated aberrant spontaneous neural synchronization in conventional frequency band (0.01-0.08 Hz) in TS. No published studies have reported abnormalities of local synchronization across different frequency bands. We estimated the alterations of local synchronization across five bands ranging from 0 to 0.25 Hz. Seventy-nine children with TS and 63 age-, sex-, and handedness-matched healthy children were recruited. Frequency-specific regional homogeneity (ReHo) and independent component analysis were used to identify functional alterations between TS and healthy children. TS patients showed significantly increased ReHo in the left precentral gyrus and decreased ReHo in the right operculum. Abnormal ReHo alterations of the superior frontal gyrus, superior parietal gyrus, anterior cingulate gyrus, putamen, superior temporal gyrus, and operculum were observed in different frequency bands. TS patients showed increased connectivity of the right superior frontal gyrus within the left executive control network. In addition, a significantly negative correlation was found between Yale Global Tic Severity Scale (YGTSS) vocal score and ReHo values of the right operculum in the highest frequency bands (0.198-0.25 Hz), while a significant positive correlation was found between YGTSS motor score and altered connectivity of the right superior frontal gyrus. The present study revealed frequency-specific abnormal alterations of ReHo in the whole brain and altered connectivity within the executive control network of TS children. Its neural importance and clinical practicability require further investigation.