Probing the reaching-grasping network in humans through multivoxel pattern decoding

A focus on the multiple interfaces between action and perception and their neural correlates

Successful behaviour relies on the appropriate interplay between action and perception. The well-established dorsal and ventral stream theories depicted two distinct functional pathways for the processes of action and perception, respectively. In physiological conditions, the two pathways closely cooperate in order to produce successful adaptive behaviour. As the coupling between perception and action exists, this requires an interface that is responsible for a common reading of the two functions. Several studies have proposed different types of perception and action interfaces, suggesting their role in the creation of the shared interaction channel. In the present review, we describe three possible perception and action interfaces: i) the motor code, including common coding approaches, ii) attention, and iii) object affordance; we highlight their potential neural correlates. From this overview, a recurrent neural substrate that underlies all these interface functions appears to be crucial: the parieto-frontal circuit. This network is involved in the mirror mechanism which underlies the perception and action interfaces identified as common coding and motor code theories. The same network is also involved in the spotlight of attention and in the encoding of potential action towards objects; these are manifested in the perception and action interfaces for common attention and object affordance, respectively. Within this framework, most studies were dedicated to the description of the role of the inferior parietal lobule; growing evidence, however, suggests that the superior parietal lobule also plays a crucial role in the interplay between action and perception. The present review proposes a novel model that is inclusive of the superior parietal regions and their relative contribution to the different action and perception interfaces.

Changes in EEG alpha-band power during prehension indicates neural motor drive inhibition

Changes in alpha band activity (8-12 Hz) indicate the downregulation of brain regions during cognitive tasks, reflecting real-time cognitive load. Despite this, its feasibility to be used in a more dynamic environment with ongoing motor corrections has not been studied. This research used electroencephalography (EEG) to explore how different brain regions are engaged during a simple grasp and lift task where unexpected changes to the object's weight or surface friction are introduced. The results suggest that alpha activity changes related to motor error correction occur only in motor-related areas (i.e. central areas) but not in error processing areas (i.e., frontoparietal network) during unexpected weight changes. This suggests that oscillations over motor areas reflect the reduction of motor drive related to motor error correction, thus, being a potential cortical electrophysiological biomarker for the process and not solely as a proxy for cognitive demands. This observation is particularly relevant in scenarios where these signals are used to evaluate high cognitive demands co-occurring with high levels of motor errors and corrections, such as prosthesis use. The establishment of electrophysiological biomarkers of mental resource allocation during movement and cognition can help identify indicators of mental workload and motor drive, which may be useful for improving brain-machine interfaces.NEW & NOTEWORTHY We demonstrated that alpha suppression, an EEG phenomenon with high temporal resolution, occurs over the primary sensorimotor area during error correction during lift movements. Interpretations of alpha activity are often attributed to high cognitive demands, thus recognizing that it is also influenced by motor processes is important in situations where cognitive demands are paired with movement errors. This could further have application as a biomarker for error correction in human-machine interfaces, such as neuroprostheses.

Grasping with a Twist: Dissociating Action Goals from Motor Actions in Human Frontoparietal Circuits

In daily life, prehension is typically not the end goal of hand-object interactions but a precursor for manipulation. Nevertheless, functional MRI (fMRI) studies investigating manual manipulation have primarily relied on prehension as the end goal of an action. Here, we used slow event-related fMRI to investigate differences in neural activation patterns between prehension in isolation and prehension for object manipulation. Sixteen (seven males and nine females) participants were instructed either to simply grasp the handle of a rotatable dial (isolated prehension) or to grasp and turn it (prehension for object manipulation). We used representational similarity analysis (RSA) to investigate whether the experimental conditions could be discriminated from each other based on differences in task-related brain activation patterns. We also used temporal multivoxel pattern analysis (tMVPA) to examine the evolution of regional activation patterns over time. Importantly, we were able to differentiate isolated prehension and prehension for manipulation from activation patterns in the early visual cortex, the caudal intraparietal sulcus (cIPS), and the superior parietal lobule (SPL). Our findings indicate that object manipulation extends beyond the putative cortical grasping network (anterior intraparietal sulcus, premotor and motor cortices) to include the superior parietal lobule and early visual cortex.SIGNIFICANCE STATEMENT A simple act such as turning an oven dial requires not only that the CNS encode the initial state (starting dial orientation) of the object but also the appropriate posture to grasp it to achieve the desired end state (final dial orientation) and the motor commands to achieve that state. Using advanced temporal neuroimaging analysis techniques, we reveal how such actions unfold over time and how they differ between object manipulation (turning a dial) versus grasping alone. We find that a combination of brain areas implicated in visual processing and sensorimotor integration can distinguish between the complex and simple tasks during planning, with neural patterns that approximate those during the actual execution of the action.

Spontaneous activity patterns in human motor cortex replay evoked activity patterns for hand movements

Spontaneous brain activity, measured with resting state fMRI (R-fMRI), is correlated among regions that are co-activated by behavioral tasks. It is unclear, however, whether spatial patterns of spontaneous activity within a cortical region correspond to spatial patterns of activity evoked by specific stimuli, actions, or mental states. The current study investigated the hypothesis that spontaneous activity in motor cortex represents motor patterns commonly occurring in daily life. To test this hypothesis 15 healthy participants were scanned while performing four different hand movements. Three movements (Grip, Extend, Pinch) were ecological involving grip and grasp hand movements; one control movement involving the rotation of the wrist was not ecological and infrequent (Shake). They were also scanned at rest before and after the execution of the motor tasks (resting-state scans). Using the task data, we identified movement-specific patterns in the primary motor cortex. These task-defined patterns were compared to resting-state patterns in the same motor region. We also performed a control analysis within the primary visual cortex. We found that spontaneous activity patterns in the primary motor cortex were more like task patterns for ecological than control movements. In contrast, there was no difference between ecological and control hand movements in the primary visual area. These findings provide evidence that spontaneous activity in human motor cortex forms fine-scale, patterned representations associated with behaviors that frequently occur in daily life.

Deep learning multimodal fNIRS and EEG signals for bimanual grip force decoding

Objective.Non-invasive brain-machine interfaces (BMIs) offer an alternative, safe and accessible way to interact with the environment. To enable meaningful and stable physical interactions, BMIs need to decode forces. Although previously addressed in the unimanual case, controlling forces from both hands would enable BMI-users to perform a greater range of interactions. We here investigate the decoding of hand-specific forces.Approach.We maximise cortical information by using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) and developing a deep-learning architecture with attention and residual layers (cnnatt) to improve their fusion. Our task required participants to generate hand-specific force profiles on which we trained and tested our deep-learning and linear decoders.Main results.The use of EEG and fNIRS improved the decoding of bimanual force and the deep-learning models outperformed the linear model. In both cases, the greatest gain in performance was due to the detection of force generation. In particular, the detection of forces was hand-specific and better for the right dominant hand andcnnattwas better at fusing EEG and fNIRS. Consequently, the study ofcnnattrevealed that forces from each hand were differently encoded at the cortical level.Cnnattalso revealed traces of the cortical activity being modulated by the level of force which was not previously found using linear models.Significance.Our results can be applied to avoid hand-cross talk during hand force decoding to improve the robustness of BMI robotic devices. In particular, we improve the fusion of EEG and fNIRS signals and offer hand-specific interpretability of the encoded forces which are valuable during motor rehabilitation assessment.

Scene context shapes category representational geometry during processing of tools

Tools are ubiquitous in human environments and to think about them we use concepts. Increasingly, conceptual representation is thought to be dynamic and sensitive to the goals of the observer. Indeed, observer goals can reshape representational geometry within cortical networks supporting concepts. In the present study, we investigated the novel hypothesis that task-irrelevant scene context may implicitly alter the representational geometry of regions within the tool network. Participants performed conceptual judgments on images of tools embedded in scenes that either suggested their use (i.e., a kitchen timer sitting on a kitchen counter with vegetables in a frying pan) or that they would simply be moved (i.e., a kitchen timer sitting in an open drawer with other miscellaneous kitchen items around). We investigated whether representations in the tool network reflect category, grip, and shape information using a representational similarity analysis (RSA). We show that a) a number of regions of the tool network reflect category information about tools and b) category information predicts patterns in supramarginal gyrus more strongly in use contexts than in move contexts. Together, these results show that information about tool category is distributed across different regions of the tool network and that scene context helps shape the representational geometry of the tool network.

Decoding stimuli (tool-hand) and viewpoint invariant grasp-type information

When we see a manipulable object (henceforth tool) or a hand performing a grasping movement, our brain is automatically tuned to how that tool can be grasped (i.e., its affordance) or what kind of grasp that hand is performing (e.g., a power or precision grasp). However, it remains unclear where visual information related to tools or hands are transformed into abstract grasp representations. We therefore investigated where different levels of abstractness in grasp information are processed: grasp information that is invariant to the kind of stimuli that elicits it (tool-hand invariance); and grasp information that is hand-specific but viewpoint-invariant (viewpoint invariance). We focused on brain areas activated when viewing both tools and hands, i.e., the posterior parietal cortices (PPC), ventral premotor cortices (PMv), and lateral occipitotemporal cortex/posterior middle temporal cortex (LOTC/pMTG). To test for invariant grasp representations, we presented participants with tool images and grasp videos (from first or third person perspective; 1pp or 3pp) inside an MRI scanner, and cross-decoded power versus precision grasps across (i) grasp perspectives (viewpoint invariance), (ii) tool images and grasp 1pp videos (tool-hand 1pp invariance), and (iii) tool images and grasp 3pp videos (tool-hand 3pp invariance). Tool-hand 1pp, but not tool-hand 3pp, invariant grasp information was found in left PPC, whereas viewpoint-invariant information was found bilaterally in PPC, left PMv, and left LOTC/pMTG. These findings suggest different levels of abstractness-where visual information is transformed into stimuli-invariant grasp representations/tool affordances in left PPC, and viewpoint invariant but hand-specific grasp representations in the hand network.

Language and action in Broca's area: Computational differentiation and cortical segregation

Actions have been proposed to follow hierarchical principles similar to those hypothesized for language syntax. These structural similarities are claimed to be reflected in the common involvement of certain neural populations of Broca's area, in the Inferior Frontal Gyrus (IFG). In this position paper, we follow an influential hypothesis in linguistic theory to introduce the syntactic operation Merge and the corresponding motor/conceptual interfaces. We argue that actions hierarchies do not follow the same principles ruling language syntax. We propose that hierarchy in the action domain lies in predictive processing mechanisms mapping sensory inputs and statistical regularities of action-goal relationships. At the cortical level, distinct Broca's subregions appear to support different types of computations across the two domains. We argue that anterior BA44 is a major hub for the implementation of the syntactic operation Merge. On the other hand, posterior BA44 is recruited in selecting premotor mental representations based on the information provided by contextual signals. This functional distinction is corroborated by a recent meta-analysis (Papitto, Friederici, & Zaccarella, 2020). We conclude by suggesting that action and language can meet only where the interfaces transfer abstract computations either to the external world or to the internal mental world.

Representational Neural Mapping of Dexterous Grasping Before Lifting in Humans

The ability of humans to reach and grasp objects in their environment has been the mainstay paradigm for characterizing the neural circuitry driving object-centric actions. Although much is known about hand shaping, a persistent question is how the brain orchestrates and integrates the grasp with lift forces of the fingers in a coordinated manner. The objective of the current study was to investigate how the brain represents grasp configuration and lift force during a dexterous object-centric action in a large sample of male and female human subjects. BOLD activity was measured as subjects used a precision-grasp to lift an object with a center of mass (CoM) on the left or right with the goal of minimizing tilting the object. The extent to which grasp configuration and lift force varied between left and right CoM conditions was manipulated by grasping the object collinearly (requiring a non-collinear force distribution) or non-collinearly (requiring more symmetrical forces). Bayesian variational representational similarity analyses on fMRI data assessed the evidence that a set of cortical and cerebellar regions were sensitive to grasp configuration or lift force differences between CoM conditions at differing time points during a grasp to lift action. In doing so, we reveal strong evidence that grasping and lift force are not represented by spatially separate functionally specialized regions, but by the same regions at differing time points. The coordinated grasp to lift effort is shown to be under dorsolateral (PMv and AIP) more than dorsomedial control, and under SPL7, somatosensory PSC, ventral LOC and cerebellar control.SIGNIFICANCE STATEMENT Clumsy disasters such as spilling, dropping, and crushing during our daily interactions with objects are a rarity rather than the norm. These disasters are avoided in part as a result of our orchestrated anticipatory efforts to integrate and coordinate grasping and lifting of object interactions, all before the lift of an object even commences. How the brain orchestrates this integration process has been largely neglected by historical approaches independently and solely focusing on reaching and grasping and the neural principles that guide them. Here, we test the extent to which grasping and lifting are represented in a spatially or temporally distinct manner and identified strong evidence for the consecutive emergence of sensitivity to grasping, then lifting within the same region.

The topographical organization of motor processing: An ALE meta-analysis on six action domains and the relevance of Broca's region

Action is a cover term used to refer to a large set of motor processes differing in domain specificities (e.g. execution or observation). Here we review neuroimaging evidence on action processing (N = 416; Subjects = 5912) using quantitative Activation Likelihood Estimation (ALE) and Meta-Analytic Connectivity Modeling (MACM) approaches to delineate the functional specificities of six domains: (1) Action Execution, (2) Action Imitation, (3) Motor Imagery, (4) Action Observation, (5) Motor Learning, (6) Motor Preparation. Our results show distinct functional patterns for the different domains with convergence in posterior BA44 (pBA44) for execution, imitation and imagery processing. The functional connectivity network seeding in the motor-based localized cluster of pBA44 differs from the connectivity network seeding in the (language-related) anterior BA44. The two networks implement distinct cognitive functions. We propose that the motor-related network encompassing pBA44 is recruited when processing movements requiring a mental representation of the action itself.

Decoding Grasping Movements from the Parieto-Frontal Reaching Circuit in the Nonhuman Primate

Prehension movements typically include a reaching phase, guiding the hand toward the object, and a grip phase, shaping the hand around it. The dominant view posits that these components rely upon largely independent parieto-frontal circuits: a dorso-medial circuit involved in reaching and a dorso-lateral circuit involved in grasping. However, mounting evidence suggests a more complex arrangement, with dorso-medial areas contributing to both reaching and grasping. To investigate the role of the dorso-medial reaching circuit in grasping, we trained monkeys to reach-and-grasp different objects in the dark and determined if hand configurations could be decoded from functional magnetic resonance imaging (MRI) responses obtained from the reaching and grasping circuits. Indicative of their established role in grasping, object-specific grasp decoding was found in anterior intraparietal (AIP) area, inferior parietal lobule area PFG and ventral premotor region F5 of the lateral grasping circuit, and primary motor cortex. Importantly, the medial reaching circuit also conveyed robust grasp-specific information, as evidenced by significant decoding in parietal reach regions (particular V6A) and dorsal premotor region F2. These data support the proposed role of dorso-medial "reach" regions in controlling aspects of grasping and demonstrate the value of complementing univariate with more sensitive multivariate analyses of functional MRI (fMRI) data in uncovering information coding in the brain.

Reduced neural representation of arm/hand actions in the medial posterior parietal cortex

Several investigations at a single-cell level demonstrated that the medial posterior parietal area V6A is involved in encoding reaching and grasping actions in different visual conditions. Here, we looked for a “low-dimensional” representation of these encoding processes by studying macaque V6A neurons tested in three different tasks with a dimensionality reduction technique, the demixed principal component analysis (dPCA), which is very suitable for neuroprosthetics readout. We compared neural activity in reaching and grasping tasks by highlighting the portions of population variance involved in the encoding of visual information, target position, wrist orientation and grip type. The weight of visual information and task parameters in the encoding process was dependent on the task. We found that the distribution of variance captured by visual information in the three tasks did not differ significantly among the tasks, whereas the variance captured by target position and grip type parameters were significantly higher with respect to that captured by wrist orientation regardless of the number of conditions considered in each task. These results suggest a different use of relevant information according to the type of planned and executed action. This study shows a simplified picture of encoding that describes how V6A processes relevant information for action planning and execution.

Just above Chance: Is It Harder to Decode Information from Prefrontal Cortex Hemodynamic Activity Patterns?

The prefrontal cortex (PFC) is central to flexible, goal-directed cognition, and understanding its representational code is an important problem in cognitive neuroscience. In humans, multivariate pattern analysis (MVPA) of fMRI blood oxygenation level-dependent (BOLD) measurements has emerged as an important approach for studying neural representations. Many previous studies have implicitly assumed that MVPA of fMRI BOLD is just as effective in decoding information encoded in PFC neural activity as it is in visual cortex. However, MVPA studies of PFC have had mixed success. Here we estimate the base rate of decoding information from PFC BOLD activity patterns from a meta-analysis of published MVPA studies. We show that PFC has a significantly lower base rate (55.4%) than visual areas in occipital (66.6%) and temporal (71.0%) cortices and one that is close to chance levels. Our results have implications for the design and interpretation of MVPA studies of PFC and raise important questions about its functional organization.

Numerical Affordance Influences Action Execution: A Kinematic Study of Finger Movement

Humans represent symbolic numbers as oriented from left to right: the mental number line (MNL). Up to now, scientific studies have mainly investigated the MNL by means of response times. However, the existing knowledge on the MNL can be advantaged by studies on motor patterns while responding to a number. Cognitive representations, in fact, cannot be fully understood without considering their impact on actions. Here we investigated whether a motor response can be influenced by number processing. Participants seated in front of a little soccer goal. On each trial they were visually presented with a numerical (2, 5, 8) or a non-numerical ($) stimulus. They were instructed to kick a small ball with their right index toward a frontal soccer goal as soon as a stimulus appeared on a screen. However, they had to refrain from kicking when number five was presented (no-go signal). Our main finding is that performing a kicking action after observation of the larger digit proved to be more efficient: the trajectory path was shorter and lower on the surface, velocity peak was anticipated. The smaller number, instead, specifically altered the temporal and spatial aspects of trajectories, leading to more prolonged left deviations. This is the first experimental demonstration that the reaching component of a movement is influenced by number magnitude. Since this paradigm does not require any verbal skill and non-symbolic stimuli (array of dots) can be used, it could be fruitfully adopted to evaluate number abilities in children and even preschoolers. Notably, this is a self-motivating and engaging task, which might help children to get involved and to reduce potential arousal connected to institutional paper-and-pencil examinations.

Decoding social intentions in human prehensile actions: Insights from a combined kinematics-fMRI study

Consistent evidence suggests that the way we reach and grasp an object is modulated not only by object properties (e.g., size, shape, texture, fragility and weight), but also by the types of intention driving the action, among which the intention to interact with another agent (i.e., social intention). Action observation studies ascribe the neural substrate of this 'intentional' component to the putative mirror neuron (pMNS) and the mentalizing (MS) systems. How social intentions are translated into executed actions, however, has yet to be addressed. We conducted a kinematic and a functional Magnetic Resonance Imaging (fMRI) study considering a reach-to-grasp movement performed towards the same object positioned at the same location but with different intentions: passing it to another person (social condition) or putting it on a concave base (individual condition). Kinematics showed that individual and social intentions are characterized by different profiles, with a slower movement at the level of both the reaching (i.e., arm movement) and the grasping (i.e., hand aperture) components. fMRI results showed that: (i) distinct voxel pattern activity for the social and the individual condition are present within the pMNS and the MS during action execution; (ii) decoding accuracies of regions belonging to the pMNS and the MS are correlated, suggesting that these two systems could interact for the generation of appropriate motor commands. Results are discussed in terms of motor simulation and inferential processes as part of a hierarchical generative model for action intention understanding and generation of appropriate motor commands.

Disentangling Representations of Object and Grasp Properties in the Human Brain

The properties of objects, such as shape, influence the way we grasp them. To quantify the role of different brain regions during grasping, it is necessary to disentangle the processing of visual dimensions related to object properties from the motor aspects related to the specific hand configuration. We orthogonally varied object properties (shape, size, and elongation) and task (passive viewing, precision grip with two or five digits, or coarse grip with five digits) and used representational similarity analysis of functional magnetic resonance imaging data to infer the representation of object properties and hand configuration in the human brain. We found that object elongation is the most strongly represented object feature during grasping and is coded preferentially in the primary visual cortex as well as the anterior and posterior superior-parieto-occipital cortex. By contrast, primary somatosensory, motor, and ventral premotor cortices coded preferentially the number of digits while ventral-stream and dorsal-stream regions coded a mix of visual and motor dimensions. The representation of object features varied with task modality, as object elongation was less relevant during passive viewing than grasping. To summarize, this study shows that elongation is a particularly relevant property of the object to grasp, which along with the number of digits used, is represented within both ventral-stream and parietal regions, suggesting that communication between the two streams about these specific visual and motor dimensions might be relevant to the execution of efficient grasping actions.

SIGNIFICANCE STATEMENT

To grasp something, the visual properties of an object guide preshaping of the hand into the appropriate configuration. Different grips can be used, and different objects require different hand configurations. However, in natural actions, grip and object type are often confounded, and the few experiments that have attempted to separate them have produced conflicting results. As such, it is unclear how visual and motor properties are represented across brain regions during grasping. Here we orthogonally manipulated object properties and grip, and revealed the visual dimension (object elongation) and the motor dimension (number of digits) that are more strongly coded in ventral and dorsal streams. These results suggest that both streams play a role in the visuomotor coding essential for grasping.

Does non-invasive brain stimulation modify hand dexterity? Protocol for a systematic review and meta-analysis

INTRODUCTION

Dexterity is described as coordinated hand and finger movement for precision tasks. It is essential for day-to-day activities like computer use, writing or buttoning a shirt. Integrity of brain motor networks is crucial to properly execute these fine hand tasks. When these networks are damaged, interventions to enhance recovery are frequently accompanied by unwanted side effects or limited in their effect. Non-invasive brain stimulation (NIBS) are postulated to target affected motor areas and improve hand motor function with few side effects. However, the results across studies vary, and the current literature does not allow us to draw clear conclusions on the use of NIBS to promote hand function recovery. Therefore, we developed a protocol for a systematic review and meta-analysis on the effects of different NIBS technologies on dexterity in diverse populations. This study will potentially help future evidence-based research and guidelines that use these NIBS technologies for recovering hand dexterity.

METHODS AND ANALYSIS

This protocol will compare the effects of active versus sham NIBS on precise hand activity. Records will be obtained by searching relevant databases. Included articles will be randomised clinical trials in adults, testing the therapeutic effects of NIBS on continuous dexterity data. Records will be studied for risk of bias. Narrative and quantitative synthesis will be done.

ETHICS AND DISSEMINATION

No private health information is included; the study is not interventional. Ethical approval is not required. The results will be reported in a peer-review journal.

REGISTRATION DETAILS

PROSPERO International prospective register of systematic reviews registration number: CRD42016043809.

Elucidating the neural circuitry underlying planning of internally-guided voluntary action

In an attempt to elucidate the neural circuitry of planning of internally guided voluntary action, Ariani et al. (2015) used a delayed-movement design and multivariate pattern analysis of functional MRI data and found areas decoding internally elicited action plans, stimulus-elicited action plans, and both types of plans. In interpreting their results in the context of a heuristic decision model of voluntary action, encompassing "what" action to perform, "when" to perform it, and "whether" to perform it at all, we highlight at least some neural dissociation of these components. More to that, we note that the exact neural circuitry of each component might vary depending on the performed action type, and finally, we underscore the importance of understanding the temporal specifics of such circuitries to further elucidate how they are involved and interact during voluntary action planning.

Probing the reaching-grasping network in humans through multivoxel pattern decoding

INTRODUCTION

The quest for a putative human homolog of the reaching-grasping network identified in monkeys has been the focus of many neuropsychological and neuroimaging studies in recent years. These studies have shown that the network underlying reaching-only and reach-to-grasp movements includes the superior parieto-occipital cortex (SPOC), the anterior part of the human intraparietal sulcus (hAIP), the ventral and the dorsal portion of the premotor cortex, and the primary motor cortex (M1). Recent evidence for a wider frontoparietal network coding for different aspects of reaching-only and reach-to-grasp actions calls for a more fine-grained assessment of the reaching-grasping network in humans by exploiting pattern decoding methods (multivoxel pattern analysis--MVPA).

METHODS

Here, we used MPVA on functional magnetic resonance imaging (fMRI) data to assess whether regions of the frontoparietal network discriminate between reaching-only and reach-to-grasp actions, natural and constrained grasping, different grasp types, and object sizes. Participants were required to perform either reaching-only movements or two reach-to-grasp types (precision or whole hand grasp) upon spherical objects of different sizes.

RESULTS

Multivoxel pattern analysis highlighted that, independently from the object size, all the selected regions of both hemispheres contribute in coding for grasp type, with the exception of SPOC and the right hAIP. Consistent with recent neurophysiological findings on monkeys, there was no evidence for a clear-cut distinction between a dorsomedial and a dorsolateral pathway that would be specialized for reaching-only and reach-to-grasp actions, respectively. Nevertheless, the comparison of decoding accuracy across brain areas highlighted their different contributions to reaching-only and grasping actions.

CONCLUSIONS

Altogether, our findings enrich the current knowledge regarding the functional role of key brain areas involved in the cortical control of reaching-only and reach-to-grasp actions in humans, by revealing novel fine-grained distinctions among action types within a wide frontoparietal network.