Co-registering kinematics and evoked related potentials during visually guided reach-to-grasp movements

Revealing the different levels of action monitoring in visuomotor transformation task: Evidence from decomposition of cortical potentials

This study investigates the cortical correlates of motor response control and monitoring, using the Theory of Event Coding (TEC) as a framework to investigate signals related to low-level sensory processing of motor reafference and high-level response monitoring, including verification of response outcomes with the internal model. We used a visuomotor paradigm with two targets at different distances from the participant. For the recorded movement-related cortical potentials (MRCPs), we analyzed their different components and assessed the movement phases during which they are active. Residual iteration decomposition (RIDE) and multivariate pattern analysis (MVPA) were used for this analysis. Using RIDE, we separated MRCPs into signals related to different parallel processes of visuomotor transformation: stimulus processing (S-cluster), motor response preparation and execution (R-cluster), and intermediate processes (C-cluster). We revealed sequential activation in the R-cluster, with execution-related negative components and positive contralateral peaks reflecting reafference processing. We also identified the motor post-imperative negative variation within the R-cluster, highlighting the response outcome evaluation process included in the action file. Our findings extend the understanding of C-cluster signals, typically associated with stimulus-response mapping, by demonstrating C-activation from the preparatory stages through to response termination, highlighting its participation in action monitoring. In addition, we highlighted the ability of MVPA to identify movement-related attribute encoding: where statistical analysis showed independence of stimulus processing activity from movement distance, MVPA revealed distance-related differences in the S-cluster within a time window aligned with the lateralized readiness potential (LRP). This highlights the importance of integrating RIDE and MVPA to uncover the intricate neural dynamics of motor control, sensory integration, and response monitoring.

Temporal differences and commonalities between hand and tool neural processing

Object recognition is a complex cognitive process that relies on how the brain organizes object-related information. While spatial principles have been extensively studied, less studied temporal dynamics may also offer valuable insights into this process, particularly when neural processing overlaps for different categories, as it is the case of the categories of hands and tools. Here we focus on the differences and/or similarities between the time-courses of hand and tool processing under electroencephalography (EEG). Using multivariate pattern analysis, we compared, for different time points, classification accuracy for images of hands or tools when compared to images of animals. We show that for particular time intervals (~ 136-156 ms and ~ 252-328 ms), classification accuracy for hands and for tools differs. Furthermore, we show that classifiers trained to differentiate between tools and animals generalize their learning to classification of hand stimuli between ~ 260-320 ms and ~ 376-500 ms after stimulus onset. Classifiers trained to distinguish between hands and animals, on the other hand, were able to extend their learning to the classification of tools at ~ 150 ms. These findings suggest variations in semantic features and domain-specific differences between the two categories, with later-stage similarities potentially related to shared action processing for hands and tools.

Multivariate Analysis of Electrophysiological Signals Reveals the Temporal Properties of Visuomotor Computations for Precision Grips

The frontoparietal networks underlying grasping movements have been extensively studied, especially using fMRI. Accordingly, whereas much is known about their cortical locus much less is known about the temporal dynamics of visuomotor transformations. Here, we show that multivariate EEG analysis allows for detailed insights into the time course of visual and visuomotor computations of precision grasps. Male and female human participants first previewed one of several objects and, upon its reappearance, reached to grasp it with the thumb and index finger along one of its two symmetry axes. Object shape classifiers reached transient accuracies of 70% at ∼105 ms, especially based on scalp sites over visual cortex, dropping to lower levels thereafter. Grasp orientation classifiers relied on a system of occipital-to-frontal electrodes. Their accuracy rose concurrently with shape classification but ramped up more gradually, and the slope of the classification curve predicted individual reaction times. Further, cross-temporal generalization revealed that dynamic shape representation involved early and late neural generators that reactivated one another. In contrast, grasp computations involved a chain of generators attaining a sustained state about 100 ms before movement onset. Our results reveal the progression of visual and visuomotor representations over the course of planning and executing grasp movements.SIGNIFICANCE STATEMENT Grasping an object requires the brain to perform visual-to-motor transformations of the object's properties. Although much of the neuroanatomic basis of visuomotor transformations has been uncovered, little is known about its time course. Here, we orthogonally manipulated object visual characteristics and grasp orientation, and used multivariate EEG analysis to reveal that visual and visuomotor computations follow similar time courses but display different properties and dynamics.

Are Sensory-Motor Relationships Encoded ad hoc or by Default?: An ERP Study

In this event-related potentials study we tested whether sensory-motor relations between concrete words are encoded by default or only under explicit ad hoc instructions. In Exp. 1, participants were explicitly asked to encode sensory-motor relations (e.g., “do the following objects fit in a pencil-cup?”), while other possible semantic relations remained implicit. In Exp. 2, using the same materials other group of participants were explicitly asked to encode semantic relations (e.g., “are the following objects related to a pencil-cup?”), and the possible sensory-motor relations remained implicit. The N400 component was sensitive to semantic relations (e.g., “desk” related to “pencil-cup”) both under implicit (Exp. 1) and explicit instructions (Exp. 2). By contrast, most sensory-motor relations (e.g., “pea” fitting in “pencil-cup”) were encoded ad hoc under explicit instructions (Exp. 1). Interestingly some sensory-motor relations were also encoded implicitly, but only when they corresponded to “functional” actions associated with high-related objects (e.g., “eraser” fitting in “pencil-cup”) and occurring at a late time window (500–650 ms; Exp. 2), suggesting that this type of sensory-motor relations were encoding by default.

Parietal area BA7 integrates motor programs for reaching, grasping, and bimanual coordination

Skillful interaction with the world requires that the brain uses a multitude of sensorimotor programs and subroutines, such as for reaching, grasping, and the coordination of the two body halves. However, it is unclear how these programs operate together. Networks for reaching, grasping, and bimanual coordination might converge in common brain areas. For example, Brodmann area 7 (BA7) is known to activate in disparate tasks involving the three types of movements separately. Here, we asked whether BA7 plays a key role in integrating coordinated reach-to-grasp movements for both arms together. To test this, we applied transcranial magnetic stimulation (TMS) to disrupt BA7 activity in the left and right hemispheres, while human participants performed a bimanual size-perturbation grasping task using the index and middle fingers of both hands to grasp a rectangular object whose orientation (and thus grasp-relevant width dimension) might or might not change. We found that TMS of the right BA7 during object perturbation disrupted the bimanual grasp and transport/coordination components, and TMS over the left BA7 disrupted unimanual grasps. These results show that right BA7 is causally involved in the integration of reach-to-grasp movements of the two arms.

NEW & NOTEWORTHY

Our manuscript describes a role of human Brodmann area 7 (BA7) in the integration of multiple visuomotor programs for reaching, grasping, and bimanual coordination. Our results are the first to suggest that right BA7 is critically involved in the coordination of reach-to-grasp movements of the two arms. The results complement previous reports of right-hemisphere lateralization for bimanual grasps.

Neurophysiology of Grasping Actions: Evidence from ERPs

We use our hands very frequently to interact with our environment. Neuropsychology together with lesion models and intracranial recordings and imaging work yielded important insights into the functional neuroanatomical correlates of grasping, one important function of our hands, pointing toward a functional parietofrontal brain network. Event-related potentials (ERPs) register directly electrical brain activity and are endowed with high temporal resolution but have long been assumed to be susceptible to movement artifacts. Recent work has shown that reliable ERPs can be obtained during movement execution. Here, we review the available ERP work on (uni) manual grasping actions. We discuss various ERP components and how they may be related to functional components of grasping according to traditional distinctions of manual actions such as planning and control phases. The ERP results are largely in line with the assumption of a parietofrontal network. But other questions remain, in particular regarding the temporal succession of frontal and parietal ERP effects. With the low number of ERP studies on grasping, not all ERP effects appear to be coherent with one another. Understanding the control of our hands may help to develop further neurocognitive theories of grasping and to make progress in prosthetics, rehabilitation or development of technical systems for support of human actions.

Action Priority: Early Neurophysiological Interaction of Conceptual and Motor Representations

Handling our everyday life, we often react manually to verbal requests or instruction, but the functional interrelations of motor control and language are not fully understood yet, especially their neurophysiological basis. Here, we investigated whether specific motor representations for grip types interact neurophysiologically with conceptual information, that is, when reading nouns. Participants performed lexical decisions and, for words, executed a grasp-and-lift task on objects of different sizes involving precision or power grips while the electroencephalogram was recorded. Nouns could denote objects that require either a precision or a power grip and could, thus, be (in)congruent with the performed grasp. In a control block, participants pointed at the objects instead of grasping them. The main result revealed an event-related potential (ERP) interaction of grip type and conceptual information which was not present for pointing. Incongruent compared to congruent conditions elicited an increased positivity (100–200 ms after noun onset). Grip type effects were obtained in response-locked analyses of the grasping ERPs (100–300 ms at left anterior electrodes). These findings attest that grip type and conceptual information are functionally related when planning a grasping action but such an interaction could not be detected for pointing. Generally, the results suggest that control of behaviour can be modulated by task demands; conceptual noun information (i.e., associated action knowledge) may gain processing priority if the task requires a complex motor response.

The contributions of vision and haptics to reaching and grasping

This review aims to provide a comprehensive outlook on the sensory (visual and haptic) contributions to reaching and grasping. The focus is on studies in developing children, normal, and neuropsychological populations, and in sensory-deprived individuals. Studies have suggested a right-hand/left-hemisphere specialization for visually guided grasping and a left-hand/right-hemisphere specialization for haptically guided object recognition. This poses the interesting possibility that when vision is not available and grasping relies heavily on the haptic system, there is an advantage to use the left hand. We review the evidence for this possibility and dissect the unique contributions of the visual and haptic systems to grasping. We ultimately discuss how the integration of these two sensory modalities shape hand preference.

Neural correlates of grasping

Prehension, the capacity to reach and grasp objects, comprises two main components: reaching, i.e., moving the hand towards an object, and grasping, i.e., shaping the hand with respect to its properties. Knowledge of this topic has gained a huge advance in recent years, dramatically changing our view on how prehension is represented within the dorsal stream. While our understanding of the various nodes coding the grasp component is rapidly progressing, little is known of the integration between grasping and reaching. With this Mini Review we aim to provide an up-to-date overview of the recent developments on the coding of prehension. We will start with a description of the regions coding various aspects of grasping in humans and monkeys, delineating where it might be integrated with reaching. To gain insights into the causal role of these nodes in the coding of prehension, we will link this functional description to lesion studies. Finally, we will discuss future directions that might be promising to unveil new insights on the coding of prehension movements.

Object size modulates fronto-parietal activity during reaching movements

In both monkeys and humans, reaching-related sensorimotor transformations involve the activation of a wide fronto-parietal network. Recent neurophysiological evidence suggests that some components of this network host not only neurons encoding the direction of arm reaching movements, but also neurons whose involvement is modulated by the intrinsic features of an object (e.g. size and shape). To date, it has yet to be investigated whether a similar modulation is evident in the human reaching-related areas. To fill this gap, we asked participants to reach towards either a small or a large object while kinematic and electroencephalographic signals were recorded. Behavioral results showed that the precision requirements were taken into account and the kinematics of reaching was modulated depending on the object size. Similarly, reaching-related neural activity at the level of the posterior parietal and premotor cortices was modulated by the level of accuracy determined by object size. We therefore conclude that object size is engaged in the neural computations for reach planning and execution, consistent with the results from physiological studies in non-human primates.