Explore the vast range of titles available.

Human skin relies on cutaneous receptors that output digital signals for tactile sensing in which the intensity of stimulation is converted to a series of voltage pulses. We present a power-efficient skin-inspired mechanoreceptor with a flexible organic transistor circuit that transduces pressure into digital frequency signals directly. The output frequency ranges between 0 and 200 hertz, with a sublinear response to increasing force stimuli that mimics slow-adapting skin mechanoreceptors. The output of the sensors was further used to stimulate optogenetically engineered mouse somatosensory neurons of mouse cortex in vitro, achieving stimulated pulses in accordance with pressure levels. This work represents a step toward the design and use of large-area organic electronic skins with neural-integrated touch feedback for replacement limbs.

We studied the reactivity of EEG rhythms (mu rhythms) in association with the imagination of right hand, left hand, foot, and tongue movement with 60 EEG electrodes in nine able-bodied subjects. During hand motor imagery, the hand mu rhythm blocked or desynchronized in all subjects, whereas an enhancement of the hand area mu rhythm was observed during foot or tongue motor imagery in the majority of the subjects. The frequency of the most reactive components was 11.7 Hz ± 0.4 (mean ± SD). While the desynchronized components were broad banded and centered at 10.9 Hz ± 0.9, the synchronized components were narrow banded and displayed higher frequencies at 12.0 Hz ± 1.0. The discrimination between the four motor imagery tasks based on classification of single EEG trials improved when, in addition to event-related desynchronization (ERD), event-related synchronization (ERS) patterns were induced in at least one or two tasks. This implies that such EEG phenomena may be utilized in a multi-class brain–computer interface (BCI) operated simply by motor imagery.

Using a combination of behavioral measures and transcranial magnetic stimulation (TMS), this study finds that elite basketball players are better at predicting whether a free basketball throw will land in the basket or out and that they also have higher TMS-evoked motor potentials for when the ball misses its mark. We combined psychophysical and transcranial magnetic stimulation studies to investigate the dynamics of action anticipation and its underlying neural correlates in professional basketball players. Athletes predicted the success of free shots at a basket earlier and more accurately than did individuals with comparable visual experience (coaches or sports journalists) and novices. Moreover, performance between athletes and the other groups differed before the ball was seen to leave the model's hands, suggesting that athletes predicted the basket shot's fate by reading the body kinematics. Both visuo-motor and visual experts showed a selective increase of motor-evoked potentials during observation of basket shots. However, only athletes showed a time-specific motor activation during observation of erroneous basket throws. Results suggest that achieving excellence in sports may be related to the fine-tuning of specific anticipatory 'resonance' mechanisms that endow elite athletes' brains with the ability to predict others' actions ahead of their realization.

Performing online complementary motor adjustments is quintessential to joint actions since it allows interacting people to coordinate efficiently and achieve a common goal. We sought to determine whether, during dyadic interactions, signaling strategies and simulative processes are differentially implemented on the basis of the interactional role played by each partner. To this aim, we recorded the kinematics of the right hand of pairs of individuals who were asked to grasp as synchronously as possible a bottle-shaped object according to an imitative or complementary action schedule. Task requirements implied an asymmetric role assignment so that participants performed the task acting either as (1) Leader (i.e., receiving auditory information regarding the goal of the task with indications about where to grasp the object) or (2) Follower (i.e., receiving instructions to coordinate their movements with their partner’s by performing imitative or complementary actions). Results showed that, when acting as Leader, participants used signaling strategies to enhance the predictability of their movements. In particular, they selectively emphasized kinematic parameters and reduced movement variability to provide the partner with implicit cues regarding the action to be jointly performed. Thus, Leaders make their movements more “communicative” even when not explicitly instructed to do so. Moreover, only when acting in the role of Follower did participants tend to imitate the Leader, even in complementary actions where imitation is detrimental to joint performance. Our results show that mimicking and signaling are implemented in joint actions according to the interactional role of the agent, which in turn is reflected in the kinematics of each partner.

Corticomotoneuronal (CM) cells in the primary motor cortex (M1) have monosynaptic connections with motoneurons. They are one of the few sources of descending commands that directly influence motor output. We examined the contribution of CM cells to the generation of activity in their target muscles. The preferred direction of many CM cells differed from that of their target muscles. Some CM cells were selectively active when a muscle was used as an agonist. Others were selectively active when the same muscle was used as a synergist, fixator, or antagonist. These observations suggest that the different functional uses of a muscle are generated by separate populations of CM cells. We propose that muscle function is one of the dimensions represented in the output of M1.

The purpose of this review is to describe the unique anatomical and physiological features of the hands and feet that support heat conservation and dissipation, and in so doing, highlight the importance of these appendages in human thermoregulation. For instance, the surface area to mass ratio of each hand is 4–5 times greater than that of the body, whilst for each foot, it is ~3 times larger. This characteristic is supported by vascular responses that permit a theoretical maximal mass flow of thermal energy of 6.0 W (136 W m 2 ) to each hand for a 1 °C thermal gradient. For each foot, this is 8.5 W (119 W m 2 ). In an air temperature of 27 °C, the hands and feet of resting individuals can each dissipate 150–220 W m 2 (male–female) of heat through radiation and convection. During hypothermia, the extremities are physiologically isolated, restricting heat flow to <0.1 W. When the core temperature increases ~0.5 °C above thermoneutral (rest), each hand and foot can sweat at 22–33 mL h −1 , with complete evaporation dissipating 15–22 W (respectively). During heated exercise, sweat flows increase (one hand: 99 mL h −1 ; one foot: 68 mL h −1 ), with evaporative heat losses of 67–46 W (respectively). It is concluded that these attributes allow the hands and feet to behave as excellent radiators, insulators and evaporators.

Mountcastle and colleagues proposed that the posterior parietal cortex contains a “command apparatus” for the operation of the hand in immediate extrapersonal space [Mountcastle et al. (1975) J Neurophysiol 38(4):871–908]. Here we provide three lines of converging evidence that a lateral region within area 5 has corticospinal neurons that are directly linked to the control of hand movements. First, electrical stimulation in a lateral region of area 5 evokes finger and wrist movements. Second, corticospinal neurons in the same region of area 5 terminate at spinal locations that contain last-order interneurons that innervate hand motoneurons. Third, this lateral region of area 5 contains many neurons that make disynaptic connections with hand motoneurons. The disynaptic input to motoneurons from this portion of area 5 is as direct and prominent as that from any of the premotor areas in the frontal lobe. Thus, our results establish that a region within area 5 contains a motor area with corticospinal neurons that could function as a command apparatus for operation of the hand.

Inhibitory extracellular matrices form around mature neurons as perineuronal nets containing chondroitin sulfate proteoglycans that limit axonal sprouting after CNS injury. The enzyme chondroitinase (Chase) degrades inhibitory chondroitin sulfate proteoglycans and improves axonal sprouting and functional recovery after spinal cord injury in rodents. We evaluated the effects of Chase in rhesus monkeys that had undergone C7 spinal cord hemisection. Four weeks after hemisection, we administered multiple intraparenchymal Chase injections below the lesion, targeting spinal cord circuits that control hand function. Hand function improved significantly in Chase-treated monkeys relative to vehicle-injected controls. Moreover, Chase significantly increased corticospinal axon growth and the number of synapses formed by corticospinal terminals in gray matter caudal to the lesion. No detrimental effects were detected. This approach appears to merit clinical translation in spinal cord injury.

Somatosensory feedback of the hand is essential for object identification. Without somatosensory feedback, individuals cannot reliably determine the size or compliance of an object. Electrical nerve stimulation can restore localized tactile and proprioceptive feedback with intensity discrimination capability similar to natural sensation. We hypothesized that adding artificial somatosensation improves object recognition accuracy when using a prosthesis. To test this hypothesis, we provided different forms of sensory feedback-tactile, proprioceptive, or both-to two subjects with upper limb loss. The subjects were asked to identify the size or mechanical compliance of different foam blocks placed in the prosthetic hand while visually and audibly blinded. During trials, we did not inform the subjects of their performance, but did ask them about their confidence in correctly identifying objects. Finally, we recorded applied pressures during object interaction. Subjects were free to use any strategy they chose to examine the objects. Object identification was most accurate with both tactile and proprioceptive feedback. The relative importance of each type of feedback, however, depended on object characteristics and task. Sensory feedback increased subject confidence and was directly correlated with accuracy. Subjects applied less pressure to the objects when they had tactile pressure feedback. Artificial somatosensory feedback improves object recognition and the relative importance of tactile versus proprioceptive feedback depends on the test set. We believe this test battery provides an effective means to assess the impact of sensory restoration and the relative contribution of different forms of feedback (tactile vs. kinesthetic) within the neurorehabilitation field.

Brain–computer interfaces (BCIs) allow a user to control a computer application by brain activity as measured, e.g., by electroencephalography (EEG). After about 30years of BCI research, the success of control that is achieved by means of a BCI system still greatly varies between subjects. For about 20% of potential users the obtained accuracy does not reach the level criterion, meaning that BCI control is not accurate enough to control an application. The determination of factors that may serve to predict BCI performance, and the development of methods to quantify a predictor value from psychological and/or physiological data serve two purposes: a better understanding of the ‘BCI-illiteracy phenomenon’, and avoidance of a costly and eventually frustrating training procedure for participants who might not obtain BCI control. Furthermore, such predictors may lead to approaches to antagonize BCI illiteracy. Here, we propose a neurophysiological predictor of BCI performance which can be determined from a two minute recording of a ‘relax with eyes open’ condition using two Laplacian EEG channels. A correlation of r=0.53 between the proposed predictor and BCI feedback performance was obtained on a large data base with N=80 BCI-naive participants in their first session with the Berlin brain–computer interface (BBCI) system which operates on modulations of sensory motor rhythms (SMRs).