Neural representation in M1 and S1 cortex of bilateral hand actions during prehension.

Bimanual movements that require coordinated actions of the two hands may be coordinated by synchronous bilateral activation of somatosensory and motor cortical areas in both hemispheres, by enhanced activation of individual neurons specialized for bimanual actions, or by both mechanisms. To investigate cortical neural mechanisms that mediate unimanual and bimanual prehension, we compared actions of the left and right hands in a reach to grasp-and-pull instructed-delay task. Spike trains were recorded with multiple electrode arrays placed in the hand area of primary motor (M1) and somatosensory (S1) cortex of the right hemisphere in macaques, allowing us to measure and compare the relative timing, amplitude, and synchronization of cortical activity in these areas as animals grasped and manipulated objects that differed in shape and location. We report that neurons in the right hemisphere show common task-related firing patterns for the two hands but actions of the ipsilateral hand elicited weaker and shorter-duration responses than those of the contralateral hand. We report significant bimanual activation of neurons in M1 but not in S1 cortex when animals have free choice of hand use in prehension tasks. Population ensemble responses in M1 thereby provide an accurate depiction of hand actions during skilled manual tasks. These studies also demonstrate that somatosensory cortical areas serve important cognitive and motor functions in skilled hand actions. Bilateral representation of hand actions may serve an important role in "motor equivalence" when the same movements are performed by either hand and in transfer of skill learning between the hands.NEW & NOTEWORTHY Humans can manipulate small objects with the right or left hand but typically select the dominant hand to handle them. We trained monkeys to grasp and manipulate objects with either hand, while recording neural activity in primary motor (M1) and somatosensory (S1) cortex. Actions of both hands activate M1 neurons, but S1 neurons respond only to the contralateral hand. Bilateral sensitivity in M1 may aid skill transfer between hands after stroke or head injury.

Tactile perception of the roughness of 3D-printed textures.

Surface roughness is one of the most important qualities in haptic perception. Roughness is a major identifier for judgments of material composition, comfort, and friction and is tied closely to manual dexterity. Some attention has been given to the study of roughness perception in the past, but it has typically focused on noncontrollable natural materials or on a narrow range of artificial materials. The advent of high-resolution three-dimensional (3D) printing technology provides the ability to fabricate arbitrary 3D textures with precise surface geometry to be used in tactile studies. We used parametric modeling and 3D printing to manufacture a set of textured plates with defined element spacing, shape, and arrangement. Using active touch and two-alternative forced-choice protocols, we investigated the contributions of these surface parameters to roughness perception in human subjects. Results indicate that large spatial periods produce higher estimations of roughness (with Weber fraction = 0.19), small texture elements are perceived as rougher than large texture elements of the same wavelength, perceptual differences exist between textures with the same spacing but different arrangements, and roughness equivalencies exist between textures differing along different parameters. We posit that papillary ridges serve as tactile processing units, and neural ensembles encode the spatial profiles of the texture contact area to produce roughness estimates. The stimuli and the manufacturing process may be used in further studies of tactile roughness perception and in related neurophysiological applications. NEW & NOTEWORTHY Surface roughness is an integral quality of texture perception. We manufactured textures using high-resolution 3D printing, which allows precise specification of the surface spatial topography. In human psychophysical experiments we investigated the contributions of specific surface parameters to roughness perception. We found that textures with large spatial periods, small texture elements, and irregular, isotropic arrangements elicit the highest estimations of roughness. We propose that roughness correlates inversely with the total contacted surface area.

Effect of blocking tactile information from the fingertips on adaptation and execution of grip forces to friction at the grasping surface.

Adaptation of fingertip forces to friction at the grasping surface is necessary to prevent use of inadequate or excessive grip forces. In the current study we investigated the effect of blocking tactile information from the fingertips noninvasively on the adaptation and efficiency of grip forces to surface friction during precision grasp. Ten neurologically intact subjects grasped and lifted an instrumented grip device with 18 different frictional surfaces under three conditions: with bare hands or with a thin layer of plastic (Tegaderm) or an additional layer of foam affixed to the fingertips. The coefficient of friction at the finger-object interface of each surface was obtained for each subject with bare hands and Tegaderm by measuring the slip ratio (grip force/load force) at the moment of slip. We found that the foam layer reduced sensibility for two-point discrimination and pressure sensitivity at the fingertips, but Tegaderm did not. However, Tegaderm reduced static, but not dynamic, tactile discrimination. Adaptation of fingertip grip forces to surface friction measured by the rate of change of peak grip force, and grip force efficiency measured by the grip-load force ratio at lift, showed a proportional relationship with bare hands but were impaired with Tegaderm and foam. Activation of muscles engaged in precision grip also varied with the frictional surface with bare hands but not with Tegaderm and foam. The results suggest that sensitivity for static tactile discrimination is necessary for feedforward and feedback control of grip forces and for adaptive modulation of muscle activity during precision grasp.

Neural representation of hand kinematics during prehension in posterior parietal cortex of the macaque monkey.

Studies of hand manipulation neurons in posterior parietal cortex of monkeys suggest that their spike trains represent objects by the hand postures needed for grasping or by the underlying patterns of muscle activation. To analyze the role of hand kinematics and object properties in a trained prehension task, we correlated the firing rates of neurons in anterior area 5 with hand behaviors as monkeys grasped and lifted knobs of different shapes and locations in the workspace. Trials were divided into four classes depending on the approach trajectory: forward, lateral, and local approaches, and regrasps. The task factors controlled by the animal-how and when he used the hand-appeared to play the principal roles in modulating firing rates of area 5 neurons. In all, 77% of neurons studied (58/75) showed significant effects of approach style on firing rates; 80% of the population responded at higher rates and for longer durations on forward or lateral approaches that included reaching, wrist rotation, and hand preshaping prior to contact, but only 13% distinguished the direction of reach. The higher firing rates in reach trials reflected not only the arm movements needed to direct the hand to the target before contact, but persisted through the contact, grasp, and lift stages. Moreover, the approach style exerted a stronger effect on firing rates than object features, such as shape and location, which were distinguished by half of the population. Forty-three percent of the neurons signaled both the object properties and the hand actions used to acquire them. However, the spread in firing rates evoked by each knob on reach and no-reach trials was greater than distinctions between different objects grasped with the same approach style. Our data provide clear evidence for synergies between reaching and grasping that may facilitate smooth, coordinated actions of the arm and hand.

Spike train analysis toolkit: enabling wider application of information-theoretic techniques to neurophysiology.

Conventional methods widely available for the analysis of spike trains and related neural data include various time- and frequency-domain analyses, such as peri-event and interspike interval histograms, spectral measures, and probability distributions. Information theoretic methods are increasingly recognized as significant tools for the analysis of spike train data. However, developing robust implementations of these methods can be time-consuming, and determining applicability to neural recordings can require expertise. In order to facilitate more widespread adoption of these informative methods by the neuroscience community, we have developed the Spike Train Analysis Toolkit. STAToolkit is a software package which implements, documents, and guides application of several information-theoretic spike train analysis techniques, thus minimizing the effort needed to adopt and use them. This implementation behaves like a typical Matlab toolbox, but the underlying computations are coded in C for portability, optimized for efficiency, and interfaced with Matlab via the MEX framework. STAToolkit runs on any of three major platforms: Windows, Mac OS, and Linux. The toolkit reads input from files with an easy-to-generate text-based, platform-independent format. STAToolkit, including full documentation and test cases, is freely available open source via http://neuroanalysis.org , maintained as a resource for the computational neuroscience and neuroinformatics communities. Use cases drawn from somatosensory and gustatory neurophysiology, and community use of STAToolkit, demonstrate its utility and scope.

Terminology for neuroscience data discovery: multi-tree syntax and investigator-derived semantics.

The Neuroscience Information Framework (NIF), developed for the NIH Blueprint for Neuroscience Research and available at http://nif.nih.gov and http://neurogateway.org , is built upon a set of coordinated terminology components enabling data and web-resource description and selection. Core NIF terminologies use a straightforward syntax designed for ease of use and for navigation by familiar web interfaces, and readily exportable to aid development of relational-model databases for neuroscience data sharing. Datasets, data analysis tools, web resources, and other entities are characterized by multiple descriptors, each addressing core concepts, including data type, acquisition technique, neuroanatomy, and cell class. Terms for each concept are organized in a tree structure, providing is-a and has-a relations. Broad general terms near each root span the category or concept and spawn more detailed entries for specificity. Related but distinct concepts (e.g., brain area and depth) are specified by separate trees, for easier navigation than would be required by graph representation. Semantics enabling NIF data discovery were selected at one or more workshops by investigators expert in particular systems (vision, olfaction, behavioral neuroscience, neurodevelopment), brain areas (cerebellum, thalamus, hippocampus), preparations (molluscs, fly), diseases (neurodegenerative disease), or techniques (microscopy, computation and modeling, neurogenetics). Workshop-derived integrated term lists are available Open Source at http://brainml.org ; a complete list of participants is at http://brainml.org/workshops.

Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex.

Neuroscience produces a vast amount of data from an enormous diversity of neurons. A neuronal classification system is essential to organize such data and the knowledge that is derived from them. Classification depends on the unequivocal identification of the features that distinguish one type of neuron from another. The problems inherent in this are particularly acute when studying cortical interneurons. To tackle this, we convened a representative group of researchers to agree on a set of terms to describe the anatomical, physiological and molecular features of GABAergic interneurons of the cerebral cortex. The resulting terminology might provide a stepping stone towards a future classification of these complex and heterogeneous cells. Consistent adoption will be important for the success of such an initiative, and we also encourage the active involvement of the broader scientific community in the dynamic evolution of this project.

Neurophysiology of prehension. III. Representation of object features in posterior parietal cortex of the macaque monkey.

Neurons in posterior parietal cortex (PPC) may serve both proprioceptive and exteroceptive functions during prehension, signaling hand actions and object properties. To assess these roles, we used digital video recordings to analyze responses of 83 hand-manipulation neurons in area 5 as monkeys grasped and lifted objects that differed in shape (round and rectangular), size (large and small spheres), and location (identical rectangular blocks placed lateral and medial to the shoulder). The task contained seven stages -- approach, contact, grasp, lift, hold, lower, relax -- plus a pretrial interval. The four test objects evoked similar spike trains and mean rate profiles that rose significantly above baseline from approach through lift, with peak activity at contact. Although representation by the spike train of specific hand actions was stronger than distinctions between grasped objects, 34% of these neurons showed statistically significant effects of object properties or hand postures on firing rates. Somatosensory input from the hand played an important role as firing rates diverged most prominently on contact as grasp was secured. The small sphere -- grasped with the most flexed hand posture -- evoked the highest firing rates in 43% of the population. Twenty-one percent distinguished spheres that differed in size and weight, and 14% discriminated spheres from rectangular blocks. Location in the workspace modulated response amplitude as objects placed across the midline evoked higher firing rates than positions lateral to the shoulder. We conclude that area 5 neurons, like those in area AIP, integrate object features, hand actions, and grasp postures during prehension.

Neurophysiology of prehension. I. Posterior parietal cortex and object-oriented hand behaviors.

Hand manipulation neurons in areas 5 and 7b/anterior intraparietal area (AIP) of posterior parietal cortex were analyzed in three macaque monkeys during a trained prehension task. Digital video recordings of hand kinematics synchronized to neuronal spike trains were used to correlate firing rates of 128 neurons with hand actions as the animals grasped and lifted rectangular and round objects. We distinguished seven task stages: approach, contact, grasp, lift, hold, lower, and relax. Posterior parietal cortex (PPC) firing rates were highest during object acquisition; 88% of task-related area 5 neurons and 77% in AIP/7b fired maximally during stages 1, 2, or 3. Firing rates rose 200-500 ms before contact, peaked at contact, and declined after grasp was secured. 83% of area 5 neurons and 72% in AIP/7b showed significant increases in mean rates during approach as the fingers were preshaped for grasp. Somatosensory signals at contact provided feedback concerning the accuracy of reach and helped guide the hand to grasp sites. In error trials, tactile information was used to abort grasp, or to initiate corrective actions to achieve task goals. Firing rates declined as lift began. 41% of area 5 neurons and 38% in AIP/7b were inhibited during holding, and returned to baseline when grasp was relaxed. Anatomical connections suggest that area 5 provides somesthetic information to circuits linking AIP/7b to frontal motor areas involved in grasping. Area 5 may also participate in sensorimotor transformations coordinating reach and grasp behaviors and provide on-line feedback needed for goal-directed hand movements.

Neurophysiology of prehension. II. Response diversity in primary somatosensory (S-I) and motor (M-I) cortices.

Prehension responses of 76 neurons in primary somatosensory (S-I) and motor (M-I) cortices were analyzed in three macaques during performance of a grasp and lift task. Digital video recordings of hand kinematics synchronized to neuronal spike trains were compared with responses in posterior parietal areas 5 and AIP/7b (PPC) of the same monkeys during seven task stages: 1) approach, 2) contact, 3) grasp, 4) lift, 5) hold, 6) lower, and 7) relax. S-I and M-I firing patterns signaled particular hand actions, rather than overall task goals. S-I responses were more diverse than those in PPC, occurred later in time, and focused primarily on grasping. Sixty-three percent of S-I neurons fired at peak rates during contact and/or grasping. Lift, hold, and lowering excited fewer S-I cells. Only 8% of S-I cells fired at peak rates before contact, compared with 27% in PPC. M-I responses were also diverse, forming functional groups for hand preshaping, object acquisition, and grip force application. M-I activity began < or =500 ms before contact, coinciding with the earliest activity in PPC. Activation of specific muscle groups in the hand was paralleled by matching patterns of somatosensory feedback from S-I needed for efficient performance. These findings support hypotheses that predictive and planning components of prehension are represented in PPC and premotor cortex, whereas performance and feedback circuits dominate activity in M-I and S-I. Somatosensory feedback from the hand to S-I enables real-time adjustments of grasping by connections to M-I and updates future prehension plans through projections to PPC.

Sensory monitoring of prehension in the parietal lobe: a study using digital video.

Digital video provides technological tools for monitoring hand kinematics during prehension, and for correlating motor behavior with the simultaneously recorded firing patterns of neurons in parietal cortex of monkeys. The constancy of the hand action in the task allowed us to derive population responses of neurons in both S-I and posterior parietal cortex (PPC) from serial single unit recordings. Activity of PPC neurons preceded that in S-I, and was often shape-selective for particular objects, suggesting that they play an important role in motor planning of prehension. Detailed sensory monitoring of hand-object interactions occurred in S-I, where distinct groups of neurons responded to specific behaviors such as grasping, lifting, holding or releasing objects.