Responses of identified cells in postcentral cortex of awake monkeys during comparable active and passive joint movements

Somatosensory Mapping Using a Novel Sensory Discrimination Task: Technical Note

BACKGROUND AND OBJECTIVES

Although diffuse gliomas in the primary somatosensory cortex (S1) are often considered resectable, gliomas in the primary motor cortex require motor mapping to preserve motor function. Recent evidence indicates that some somatosensory cortex neurons may trigger motor responses, necessitating refined somatosensory mapping techniques.

METHODS

Using piezoelectric tactile stimulators on patients' faces and hands, we delivered 25 Hz vibrations and prompted patients to discriminate between dermatomes. Testing included areas contralateral to tumor-infiltrated and to non-tumor-infiltrated cortical regions. Sensory thresholds were determined by reducing stimulus intensity based on performance. Intraoperatively, electrocorticography electrode arrays were used to map sensory responses, and postoperative assessments evaluated sensory outcomes.

RESULTS

The high-grade glioma case involved a 61-year-old man with right-sided weakness and numbness with a left parietal mass on MRI. Preoperative testing showed that the average vibratory detection threshold of the hand contralateral to the suspected tumor site was significantly higher than that of the hand contralateral to healthy cortex ( P < .001). Intraoperative mapping confirmed the absence of functional involvement in cortical structures overlying the tumor. Postoperative imaging confirmed gross total resection, and sensory vibratory thresholds were normalized ( P = .51). The low-grade glioma case included a 54-year-old man with a left parietal nonenhancing mass on MRI. No baseline sensory impairments were found on preoperative testing. Intraoperative mapping identified motor and sensory cortices, guiding tumor resection while preserving motor function. Postoperative MRI confirmed near-total resection, but new sensory impairments were noted in the hand and face contralateral to the resection site ( P < .001). These deficits resolved by postoperative day 11, with no evidence of tumor progression on follow-up imaging.

CONCLUSION

The sensory discrimination task provides a quantifiable method for assessing sensory changes and functional outcomes related to glioma. This technique enhances our understanding of how glioma infiltration remodels sensory systems and affects clinical outcomes in patients.

Simultaneous monitoring of the human brain, spinal cord, and cauda equina activity for movement control: An fNIRS approach

Brain‒spinal cord‒cauda equina interactions are essential for controlling lower body movement. However, current monitoring approaches for spinal and caudal activity are limited to use without body movement and to processing via batches of data. Here, we present a novel optical method based on functional near-infrared spectroscopy that enables simultaneous tracking of human brain-spinal cord-cauda equina hemodynamics during body movement. We first developed a support frame for positioning optical emitters and receivers along the spinal canal to maximize spatial resolution and identify the optimal distance between them. We tested the methodology at this optimal emitter-detector distance by assessing the spatiotemporal activation of the motor clusters associated with human ankle extension-flexion movement in the brain, spinal cord, and cauda equina. These brain and spinal clusters are shown to be functionally connected and comparable to those identified by invasive methods during surgical operations. These findings suggest that hemodynamic responses reflect synchronous neural activity in the human brain-spinal cord-cauda equina system for hindlimb movement control.

Does pre-speech auditory modulation reflect processes related to feedback monitoring or speech movement planning?

Previous studies have revealed that auditory processing is modulated during the planning phase immediately prior to speech onset. To date, the functional relevance of this pre-speech auditory modulation (PSAM) remains unknown. Here, we investigated whether PSAM reflects neuronal processes that are associated with preparing auditory cortex for optimized feedback monitoring as reflected in online speech corrections. Combining electroencephalographic PSAM data from a previous data set with new acoustic measures of the same participants' speech, we asked whether individual speakers' extent of PSAM is correlated with the implementation of within-vowel articulatory adjustments during /b/-vowel-/d/ word productions. Online articulatory adjustments were quantified as the extent of change in inter-trial formant variability from vowel onset to vowel midpoint (a phenomenon known as centering). This approach allowed us to also consider inter-trial variability in formant production, and its possible relation to PSAM, at vowel onset and midpoint separately. Results showed that inter-trial formant variability was significantly smaller at vowel midpoint than at vowel onset. PSAM was not significantly correlated with this amount of change in variability as an index of within-vowel adjustments. Surprisingly, PSAM was negatively correlated with inter-trial formant variability not only in the middle but also at the very onset of the vowels. Thus, speakers with more PSAM produced formants that were already less variable at vowel onset. Findings suggest that PSAM may reflect processes that influence speech acoustics as early as vowel onset and, thus, that are directly involved in motor command preparation (feedforward control) rather than output monitoring (feedback control).

Does pre-speech auditory modulation reflect processes related to feedback monitoring or speech movement planning?

Previous studies have revealed that auditory processing is modulated during the planning phase immediately prior to speech onset. To date, the functional relevance of this pre-speech auditory modulation (PSAM) remains unknown. Here, we investigated whether PSAM reflects neuronal processes that are associated with preparing auditory cortex for optimized feedback monitoring as reflected in online speech corrections. Combining electroencephalographic PSAM data from a previous data set with new acoustic measures of the same participants' speech, we asked whether individual speakers' extent of PSAM is correlated with the implementation of within-vowel articulatory adjustments during /b/-vowel-/d/ word productions. Online articulatory adjustments were quantified as the extent of change in inter-trial formant variability from vowel onset to vowel midpoint (a phenomenon known as centering). This approach allowed us to also consider inter-trial variability in formant production and its possible relation to PSAM at vowel onset and midpoint separately. Results showed that inter-trial formant variability was significantly smaller at vowel midpoint than at vowel onset. PSAM was not significantly correlated with this amount of change in variability as an index of within-vowel adjustments. Surprisingly, PSAM was negatively correlated with inter-trial formant variability not only in the middle but also at the very onset of the vowels. Thus, speakers with more PSAM produced formants that were already less variable at vowel onset. Findings suggest that PSAM may reflect processes that influence speech acoustics as early as vowel onset and, thus, that are directly involved in motor command preparation (feedforward control) rather than output monitoring (feedback control).

Secondary somatosensory and posterior insular cortices: a somatomotor hub for object prehension and manipulation movements

The secondary somatosensory cortex (SII) and posterior insular cortex (pIC) are recognized for processing touch and movement information during hand manipulation in humans and non-human primates. However, their involvement in three-dimensional (3D) object manipulation remains unclear. To investigate neural activity related to hand manipulation in the SII/pIC, we trained two macaque monkeys to grasp three objects (a cone, a plate, and a ring) and engage in visual fixation on the object. Our results revealed that 19.4% (n = 50/257) of the task-related neurons in SII/pIC were active during hand manipulations, but did not respond to passive somatosensory stimuli. Among these neurons, 44% fired before hand-object contact (reaching to grasping neurons), 30% maintained tonic activity after contact (holding neurons), and 26% showed continuous discharge before and after contact (non-selective neurons). Object grasping-selectivity varied and was weak among these neurons, with only 24% responding to fixation of a 3D object (visuo-motor neurons). Even neurons unresponsive to passive visual stimuli showed responses to set-related activity before the onset of movement (42%, n = 21/50). Our findings suggest that somatomotor integration within SII/pIC is probably integral to all prehension sequences, including reaching, grasping, and object manipulation movements. Moreover, the existence of a set-related activity within SII/pIC may play a role in directing somatomotor attention during object prehension-manipulation in the absence of vision. Overall, SII/pIC may play a role as a somatomotor hub within the lateral grasping network that supports the generation of intentional hand actions based on haptic information.

The human somatosensory cortex contributes to the encoding of newly learned movements

Recent studies have indicated somatosensory cortex involvement in motor learning and retention. However, the nature of its contribution is unknown. One possibility is that the somatosensory cortex is transiently engaged during movement. Alternatively, there may be durable learning-related changes which would indicate sensory participation in the encoding of learned movements. These possibilities are dissociated by disrupting the somatosensory cortex following learning, thus targeting learning-related changes which may have occurred. If changes to the somatosensory cortex contribute to retention, which, in effect, means aspects of newly learned movements are encoded there, disruption of this area once learning is complete should lead to an impairment. Participants were trained to make movements while receiving rotated visual feedback. The primary motor cortex (M1) and the primary somatosensory cortex (S1) were targeted for continuous theta-burst stimulation, while stimulation over the occipital cortex served as a control. Retention was assessed using active movement reproduction, or recognition testing, which involved passive movements produced by a robot. Disruption of the somatosensory cortex resulted in impaired motor memory in both tests. Suppression of the motor cortex had no impact on retention as indicated by comparable retention levels in control and motor cortex conditions. The effects were learning specific. When stimulation was applied to S1 following training with unrotated feedback, movement direction, the main dependent variable, was unaltered. Thus, the somatosensory cortex is part of a circuit that contributes to retention, consistent with the idea that aspects of newly learned movements, possibly learning-updated sensory states (new sensory targets) which serve to guide movement, may be encoded there.

Disruption of somatosensory cortex impairs motor learning and retention

This study tests for a function of the somatosensory cortex, that, in addition to its role in processing somatic afferent information, somatosensory cortex contributes both to motor learning and the stabilization of motor memory. Continuous theta-burst magnetic stimulation (cTBS) was applied, before force-field training to disrupt activity in either the primary somatosensory cortex, primary motor cortex, or a control zone over the occipital lobe. Tests for retention and relearning were conducted after a 24 h delay. Analysis of movement kinematic measures and force-channel trials found that cTBS to somatosensory cortex disrupted both learning and subsequent retention, whereas cTBS to motor cortex had little effect on learning but possibly impaired retention. Basic movement variables are unaffected by cTBS suggesting that the stimulation does not interfere with movement but instead disrupts changes in the cortex that are necessary for learning. In all experimental conditions, relearning in an abruptly introduced force field, which followed retention testing, showed extensive savings, which is consistent with previous work suggesting that more cognitive aspects of learning and retention are not dependent on either of the cortical zones under test. Taken together, the findings are consistent with the idea that motor learning is dependent on learning-related activity in the somatosensory cortex.NEW & NOTEWORTHY This study uses noninvasive transcranial magnetic stimulation to test the contribution of somatosensory and motor cortex to human motor learning and retention. Continuous theta-burst stimulation is applied before learning; participants return 24 h later to assess retention. Disruption of the somatosensory cortex is found to impair both learning and retention, whereas disruption of the motor cortex has no effect on learning. The findings are consistent with the idea that motor learning is dependent upon learning-related plasticity in somatosensory cortex.

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-like neural dynamics in two parietal areas during arm reaching

The classical view on motor control makes a clear distinction between the role of motor cortex in controlling muscles and parietal cortex in processing movement plans and goals. However, the strong parieto-frontal connections argue against such clear-cut separation of function. Modern dynamical approaches revealed that population activity in motor cortex can be captured by a limited number of patterns, called neural states that are preserved across diverse motor behaviors. Whether such dynamics are also present in parietal cortex is unclear. Here, we studied neural dynamics in the primate parietal cortex during arm movements and found three main states temporally coupled to the planning, execution and target holding epochs. Strikingly, as reported recently in motor cortex, execution was subdivided into distinct, arm acceleration- and deceleration-related, states. These results suggest that dynamics across parieto-frontal areas are highly consistent and hint that parietal population activity largely reflects timing constraints while motor actions unfold.

Local field potentials in the parietal reach region reveal mechanisms of bimanual coordination

Primates use their arms in complex ways that frequently require coordination between the two arms. Yet the planning of bimanual movements has not been well-studied. We recorded spikes and local field potentials (LFP) from the parietal reach region (PRR) in both hemispheres simultaneously while monkeys planned and executed unimanual and bimanual reaches. From analyses of interhemispheric LFP-LFP and spike-LFP coherence, we found that task-specific information is shared across hemispheres in a frequency-specific manner. This shared information could arise from common input or from direct communication. The population average unit activity in PRR, representing PRR output, encodes only planned contralateral arm movements while beta-band LFP power, a putative PRR input, reflects the pattern of planned bimanual movement. A parsimonious interpretation of these data is that PRR integrates information about the movement of the left and right limbs, perhaps in service of bimanual coordination.

Neural encoding of actual and imagined touch within human posterior parietal cortex

In the human posterior parietal cortex (PPC), single units encode high-dimensional information with partially mixed representations that enable small populations of neurons to encode many variables relevant to movement planning, execution, cognition, and perception. Here, we test whether a PPC neuronal population previously demonstrated to encode visual and motor information is similarly engaged in the somatosensory domain. We recorded neurons within the PPC of a human clinical trial participant during actual touch presentation and during a tactile imagery task. Neurons encoded actual touch at short latency with bilateral receptive fields, organized by body part, and covered all tested regions. The tactile imagery task evoked body part-specific responses that shared a neural substrate with actual touch. Our results are the first neuron-level evidence of touch encoding in human PPC and its cognitive engagement during a tactile imagery task, which may reflect semantic processing, attention, sensory anticipation, or imagined touch.

The human primary somatosensory cortex encodes imagined movement in the absence of sensory information

Classical systems neuroscience positions primary sensory areas as early feed-forward processing stations for refining incoming sensory information. This view may oversimplify their role given extensive bi-directional connectivity with multimodal cortical and subcortical regions. Here we show that single units in human primary somatosensory cortex encode imagined reaches in a cognitive motor task, but not other sensory–motor variables such as movement plans or imagined arm position. A population reference-frame analysis demonstrates coding relative to the cued starting hand location suggesting that imagined reaching movements are encoded relative to imagined limb position. These results imply a potential role for primary somatosensory cortex in cognitive imagery, engagement during motor production in the absence of sensation or expected sensation, and suggest that somatosensory cortex can provide control signals for future neural prosthetic systems.

Matiar Jafari, Tyson Aflalo et al. show that the human primary somatosensory cortex is activated when subjects imagine reaches in a cognitive motor task, but not when they plan movement or imagine a static limb position. These results highlight a role for this region in cognitive imagery and motor control in the absence of sensory information.

Decoding hand kinematics from population responses in sensorimotor cortex during grasping

OBJECTIVE

The hand-a complex effector comprising dozens of degrees of freedom of movement-endows us with the ability to flexibly, precisely, and effortlessly interact with objects. The neural signals associated with dexterous hand movements in primary motor cortex (M1) and somatosensory cortex (SC) have received comparatively less attention than have those associated with proximal upper limb control.

APPROACH

To fill this gap, we trained two monkeys to grasp objects varying in size and shape while tracking their hand postures and recording single-unit activity from M1 and SC. We then decoded their hand kinematics across tens of joints from population activity in these areas.

MAIN RESULTS

We found that we could accurately decode kinematics with a small number of neural signals and that different cortical fields carry different amounts of information about hand kinematics. In particular, neural signals in rostral M1 led to better performance than did signals in caudal M1, whereas Brodmann's area 3a outperformed areas 1 and 2 in SC. Moreover, decoding performance was higher for joint angles than joint angular velocities, in contrast to what has been found with proximal limb decoders.

SIGNIFICANCE

We conclude that cortical signals can be used for dexterous hand control in brain machine interface applications and that postural representations in SC may be exploited via intracortical stimulation to close the sensorimotor loop.

Differential effects of the mode of touch, active and passive, on experience-driven plasticity in the S1 cutaneous digit representation of adult macaque monkeys

This study compared the receptive field (RF) properties and firing rates of neurons in the cutaneous hand representation of primary somatosensory cortex (areas 3b, 1, and 2) of 9 awake, adult macaques that were intensively trained in a texture discrimination task using active touch (fingertips scanned over the surfaces using a single voluntary movement), passive touch (surfaces displaced under the immobile fingertips), or both active and passive touch. Two control monkeys received passive exposure to the same textures in the context of a visual discrimination task. Training and recording extended over 1–2 yr per animal. All neurons had a cutaneous receptive field (RF) that included the tips of the stimulated digits (D3 and/or D4). In area 3b, RFs were largest in monkeys trained with active touch, smallest in those trained with passive touch, and intermediate in those trained with both; i.e., the mode of touch differentially modified the cortical representation of the stimulated fingers. The same trends were seen in areas 1 and 2, but the changes were not significant, possibly because a second experience-driven influence was seen in areas 1 and 2, but not in area 3b: smaller RFs with passive exposure to irrelevant tactile inputs compared with recordings from one naive hemisphere. We suggest that added feedback during active touch and higher cortical firing rates were responsible for the larger RFs with behavioral training; this influence was tempered by periods of more restricted sensory feedback during passive touch training in the active + passive monkeys.

NEW & NOTEWORTHY We studied experience-dependent sensory cortical plasticity in relation to tactile discrimination of texture using active and/or passive touch. We showed that neuronal receptive fields in primary somatosensory cortex, especially area 3b, are largest in monkeys trained with active touch, smallest in those trained with passive touch, and intermediate in those trained using both modes of touch. Prolonged, irrelevant tactile input had the opposite influence in areas 1 and 2, favoring smaller receptive fields.

Short reaction times in response to multi-electrode intracortical microstimulation may provide a basis for rapid movement-related feedback

OBJECTIVE

Tetraplegic patients using brain-machine interfaces can make visually guided reaches with robotic arms. However, restoring proprioceptive feedback to these patients will be critical, as evidenced by the movement deficit in patients with proprioceptive loss. Proprioception is critical in large part because it provides faster feedback than vision. Intracortical microstimulation (ICMS) is a promising approach, but the ICMS-evoked reaction time (RT) is typically slower than that to natural proprioceptive and often even visual cues, implying that ICMS feedback may not be fast enough to guide movement.

APPROACH

For most sensory modalities, RT decreases with increased stimulus intensity. Thus, it may be that stimulation intensities beyond what has previously been used will result in faster RTs. To test this, we compared the RT to ICMS applied through multi-electrode arrays in area 2 of somatosensory cortex to that of mechanical and visual cues.

MAIN RESULTS

We found that the RT to single-electrode ICMS decreased with increased current, frequency, and train length. For 100 µA, 330 Hz stimulation, the highest single-electrode intensity we tested routinely, most electrodes resulted in RTs slower than the mechanical cue but slightly faster than the visual cue. While increasing the current beyond 100 µA resulted in faster RTs, sustained stimulation at this level may damage tissue. Alternatively, by stimulating through multiple electrodes (mICMS), a large amount of current can be injected while keeping that through each electrode at a safe level. We found that stimulation with at least 480 µA equally distributed over 16 electrodes could produce RTs as much as 20 ms faster than the mechanical cue, roughly the conduction delay to cortex from the periphery.

SIGNIFICANCE

These results suggest that mICMS may provide a means to supply rapid, movement-related feedback. Future neuroprosthetics may need spatiotemporally patterned mICMS to convey useful somatosensory information. Novelty & Significance Intracortical microstimulation (ICMS) is a promising approach for providing artificial somatosensation to patients with spinal cord injury or limb amputation, but in prior experiments, subjects have been unable to respond as quickly to it as to natural cues. We have investigated the use of multi-electrode stimulation (mICMS) and discovered that it can produce reaction times as fast or faster even than natural mechanical cues. Although our stimulus trains were not modulated in time, this result opens the door to more complex spatiotemporal patterns of mICMS that might be used to rapidly write in complex somatosensory information to the CNS.

Neural Evidence of the Cerebellum as a State Predictor

We here provide neural evidence that the cerebellar circuit can predict future inputs from present outputs, a hallmark of an internal forward model. Recent computational studies hypothesize that the cerebellum performs state prediction known as a forward model. To test the forward-model hypothesis, we analyzed activities of 94 mossy fibers (inputs to the cerebellar cortex), 83 Purkinje cells (output from the cerebellar cortex to dentate nucleus), and 73 dentate nucleus cells (cerebellar output) in the cerebro-cerebellum, all recorded from a monkey performing step-tracking movements of the right wrist. We found that the firing rates of one population could be reconstructed as a weighted linear sum of those of preceding populations. We then went on to investigate if the current outputs of the cerebellum (dentate cells) could predict the future inputs of the cerebellum (mossy fibers). The firing rates of mossy fibers at time t + t₁ could be well reconstructed from as a weighted sum of firing rates of dentate cells at time t, thereby proving that the dentate activities contained predictive information about the future inputs. The average goodness-of-fit (R²) decreased moderately from 0.89 to 0.86 when t₁ was increased from 20 to 100 ms, hence indicating that the prediction is able to compensate the latency of sensory feedback. The linear equations derived from the firing rates resembled those of a predictor known as Kalman filter composed of prediction and filtering steps. In summary, our analysis of cerebellar activities supports the forward-model hypothesis of the cerebellum.

Bypassing stroke-damaged neural pathways via a neural interface induces targeted cortical adaptation

Regaining the function of an impaired limb is highly desirable in paralyzed individuals. One possible avenue to achieve this goal is to bridge the interrupted pathway between preserved neural structures and muscles using a brain–computer interface. Here, we demonstrate that monkeys with subcortical stroke were able to learn to use an artificial cortico-muscular connection (ACMC), which transforms cortical activity into electrical stimulation to the hand muscles, to regain volitional control of a paralysed hand. The ACMC induced an adaptive change of cortical activities throughout an extensive cortical area. In a targeted manner, modulating high-gamma activity became localized around an arbitrarily-selected cortical site controlling stimulation to the muscles. This adaptive change could be reset and localized rapidly to a new cortical site. Thus, the ACMC imparts new function for muscle control to connected cortical sites and triggers cortical adaptation to regain impaired motor function after stroke.

Monkeys were trained to use an artificial cortico-muscular connection (ACMC) to regain control over a paralyzed hand following subcortical stroke. Control over the paralyzed hand was accompanied by the appearance of localized high-gamma modulation in the cortex, which could be rapidly reset and relocalized to a different cortical site to reactivate motor control.

Somatosensory cortex participates in the consolidation of human motor memory

Newly learned motor skills are initially labile and then consolidated to permit retention. The circuits that enable the consolidation of motor memories remain uncertain. Most work to date has focused on primary motor cortex, and although there is ample evidence of learning-related plasticity in motor cortex, direct evidence for its involvement in memory consolidation is limited. Learning-related plasticity is also observed in somatosensory cortex, and accordingly, it may also be involved in memory consolidation. Here, by using transcranial magnetic stimulation (TMS) to block consolidation, we report the first direct evidence that plasticity in somatosensory cortex participates in the consolidation of motor memory. Participants made movements to targets while a robot applied forces to the hand to alter somatosensory feedback. Immediately following adaptation, continuous theta-burst transcranial magnetic stimulation (cTBS) was delivered to block retention; then, following a 24-hour delay, which would normally permit consolidation, we assessed whether there was an impairment. It was found that when mechanical loads were introduced gradually to engage implicit learning processes, suppression of somatosensory cortex following training almost entirely eliminated retention. In contrast, cTBS to motor cortex following learning had little effect on retention at all; retention following cTBS to motor cortex was not different than following sham TMS stimulation. We confirmed that cTBS to somatosensory cortex interfered with normal sensory function and that it blocked motor memory consolidation and not the ability to retrieve a consolidated motor memory. In conclusion, the findings are consistent with the hypothesis that in adaptation learning, somatosensory cortex rather than motor cortex is involved in the consolidation of motor memory.

Somatosensory cortical excitability changes precede those in motor cortex during human motor learning

Motor learning is associated with plasticity in both motor and somatosensory cortex. It is known from animal studies that tetanic stimulation to each of these areas individually induces long-term potentiation in its counterpart. In this context it is possible that changes in motor cortex contribute to somatosensory change and that changes in somatosensory cortex are involved in changes in motor areas of the brain. It is also possible that learning-related plasticity occurs in these areas independently. To better understand the relative contribution to human motor learning of motor cortical and somatosensory plasticity, we assessed the time course of changes in primary somatosensory and motor cortex excitability during motor skill learning. Learning was assessed using a force production task in which a target force profile varied from one trial to the next. The excitability of primary somatosensory cortex was measured using somatosensory evoked potentials in response to median nerve stimulation. The excitability of primary motor cortex was measured using motor evoked potentials elicited by single-pulse transcranial magnetic stimulation. These two measures were interleaved with blocks of motor learning trials. We found that the earliest changes in cortical excitability during learning occurred in somatosensory cortical responses, and these changes preceded changes in motor cortical excitability. Changes in somatosensory evoked potentials were correlated with behavioral measures of learning. Changes in motor evoked potentials were not. These findings indicate that plasticity in somatosensory cortex occurs as a part of the earliest stages of motor learning, before changes in motor cortex are observed.NEW & NOTEWORTHY We tracked somatosensory and motor cortical excitability during motor skill acquisition. Changes in both motor cortical and somatosensory excitability were observed during learning; however, the earliest changes were in somatosensory cortex, not motor cortex. Moreover, the earliest changes in somatosensory cortical excitability predict the extent of subsequent learning; those in motor cortex do not. This is consistent with the idea that plasticity in somatosensory cortex coincides with the earliest stages of human motor learning.

The somatosensory cortex receives information about motor output

During voluntary movement, the somatosensory system not only passively receives signals from the external world but also actively processes them via interactions with the motor system. However, it is still unclear how and what information the somatosensory system receives during movement. Using simultaneous recordings of activities of the primary somatosensory cortex (S1), the motor cortex (MCx), and an ensemble of afferent neurons in behaving monkeys combined with a decoding algorithm, we reveal the temporal profiles of signal integration in S1. While S1 activity before movement initiation is accounted for by MCx activity alone, activity during movement is accounted for by both MCx and afferent activities. Furthermore, premovement S1 activity encodes information about imminent activity of forelimb muscles slightly after MCx does. Thus, S1 receives information about motor output before the arrival of sensory feedback signals, suggesting that S1 executes online processing of somatosensory signals via interactions with the anticipatory information.

Spatial-Temporal Dynamics of the Sensorimotor Cortex: Sustained and Transient Activity

How the sensorimotor cortex is organized with respect to controlling different features of movement is unclear. One unresolved question concerns the relation between the duration of an action and the duration of the associated neuronal activity change in the sensorimotor cortex. Using subdural electrocorticography electrodes, we investigated in five subjects, whether high frequency band (HFB; 75-135 Hz) power changes have a transient or sustained relation to speech duration, during pronunciation of the Dutch /i/ vowel with different durations. We showed that the neuronal activity patterns recorded from the sensorimotor cortex can be directly related to action duration in some locations, whereas in other locations, during the same action, neuronal activity is transient, with a peak in HFB activity at movement onset and/or offset. This data sheds light on the neural underpinnings of motor actions and we discuss the possible mechanisms underlying these different response types.

Decoding Native Cortical Representations for Flexion and Extension at Upper Limb Joints Using Electrocorticography

Brain-machine interface (BMI) researchers have traditionally focused on modeling endpoint reaching tasks to provide the control of neurally driven prosthetic arms. Most previous research has focused on achieving an endpoint control through a Cartesian-coordinate-centered approach. However, a joint-centered approach could potentially be used to intuitively control a wide range of limb movements. We systematically investigated the feasibility of discriminating between flexion and extension of different upper limb joints using electrocorticography(ECoG) recordings from sensorimotor cortex. Four subjects implanted with macro-ECoG (10-mm spacing), high-density ECoG (5-mm spacing), and/or micro-ECoG arrays (0.9-mm spacing and 4 mm × 4 mm coverage), performed randomly cued flexions or extensions of the fingers, wrist, or elbow contralateral to the implanted hemisphere. We trained a linear model to classify six movements using averaged high-gamma power (70-110 Hz) modulations at different latencies with respect to movement onset, and within a time interval restricted to flexion or extension at each joint. Offline decoding models for each subject classified these movements with accuracies of 62%-83%. Our results suggest that the widespread ECoG coverage of sensorimotor cortex could allow a whole limb BMI to sample native cortical representations in order to control flexion and extension at multiple joints.

Repeated Vowel Production Affects Features of Neural Activity in Sensorimotor Cortex

The sensorimotor cortex is responsible for the generation of movements and interest in the ability to use this area for decoding speech by brain–computer interfaces has increased recently. Speech decoding is challenging however, since the relationship between neural activity and motor actions is not completely understood. Non-linearity between neural activity and movement has been found for instance for simple finger movements. Despite equal motor output, neural activity amplitudes are affected by preceding movements and the time between movements. It is unknown if neural activity is also affected by preceding motor actions during speech. We addressed this issue, using electrocorticographic high frequency band (HFB; 75–135 Hz) power changes in the sensorimotor cortex during discrete vowel generation. Three subjects with temporarily implanted electrode grids produced the /i/ vowel at repetition rates of 1, 1.33 and 1.66 Hz. For every repetition, the HFB power amplitude was determined. During the first utterance, most electrodes showed a large HFB power peak, which decreased for subsequent utterances. This result could not be explained by differences in performance. With increasing duration between utterances, more electrodes showed an equal response to all repetitions, suggesting that the duration between vowel productions influences the effect of previous productions on sensorimotor cortex activity. Our findings correspond with previous studies for finger movements and bear relevance for the development of brain-computer interfaces that employ speech decoding based on brain signals, in that past utterances will need to be taken into account for these systems to work accurately.

Neural Basis of Touch and Proprioception in Primate Cortex

The sense of proprioception allows us to keep track of our limb posture and movements and the sense of touch provides us with information about objects with which we come into contact. In both senses, mechanoreceptors convert the deformation of tissues-skin, muscles, tendons, ligaments, or joints-into neural signals. Tactile and proprioceptive signals are then relayed by the peripheral nerves to the central nervous system, where they are processed to give rise to percepts of objects and of the state of our body. In this review, we first examine briefly the receptors that mediate touch and proprioception, their associated nerve fibers, and pathways they follow to the cerebral cortex. We then provide an overview of the different cortical areas that process tactile and proprioceptive information. Next, we discuss how various features of objects-their shape, motion, and texture, for example-are encoded in the various cortical fields, and the susceptibility of these neural codes to attention and other forms of higher-order modulation. Finally, we summarize recent efforts to restore the senses of touch and proprioception by electrically stimulating somatosensory cortex. © 2018 American Physiological Society. Compr Physiol 8:1575-1602, 2018.

Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation

Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from neurophysiology, computer science, and engineering in an effort to establish real-time bidirectional links between living brains and artificial actuators. Although theoretical propositions and some proof of concept experiments on directly linking the brains with machines date back to the early 1960s, BMI research only took off in earnest at the end of the 1990s, when this approach became intimately linked to new neurophysiological methods for sampling large-scale brain activity. The classic goals of BMIs are 1) to unveil and utilize principles of operation and plastic properties of the distributed and dynamic circuits of the brain and 2) to create new therapies to restore mobility and sensations to severely disabled patients. Over the past decade, a wide range of BMI applications have emerged, which considerably expanded these original goals. BMI studies have shown neural control over the movements of robotic and virtual actuators that enact both upper and lower limb functions. Furthermore, BMIs have also incorporated ways to deliver sensory feedback, generated from external actuators, back to the brain. BMI research has been at the forefront of many neurophysiological discoveries, including the demonstration that, through continuous use, artificial tools can be assimilated by the primate brain's body schema. Work on BMIs has also led to the introduction of novel neurorehabilitation strategies. As a result of these efforts, long-term continuous BMI use has been recently implicated with the induction of partial neurological recovery in spinal cord injury patients.

Bayesian vector autoregressive model for multi-subject effective connectivity inference using multi-modal neuroimaging data

In this article a multi-subject vector autoregressive (VAR) modeling approach was proposed for inference on effective connectivity based on resting-state functional MRI data. Their framework uses a Bayesian variable selection approach to allow for simultaneous inference on effective connectivity at both the subject- and group-level. Furthermore, it accounts for multi-modal data by integrating structural imaging information into the prior model, encouraging effective connectivity between structurally connected regions. They demonstrated through simulation studies that their approach resulted in improved inference on effective connectivity at both the subject- and group-level, compared with currently used methods. It was concluded by illustrating the method on temporal lobe epilepsy data, where resting-state functional MRI and structural MRI were used. Hum Brain Mapp 38:1311-1332, 2017. © 2016 Wiley Periodicals, Inc.

Toward a Proprioceptive Neural Interface that Mimics Natural Cortical Activity

The dramatic advances in efferent neural interfaces over the past decade are remarkable, with cortical signals used to allow paralyzed patients to control the movement of a prosthetic limb or even their own hand. However, this success has thrown into relief, the relative lack of progress in our ability to restore somatosensation to these same patients. Somatosensation, including proprioception, the sense of limb position and movement, plays a crucial role in even basic motor tasks like reaching and walking. Its loss results in crippling deficits. Historical work dating back decades and even centuries has demonstrated that modality-specific sensations can be elicited by activating the central nervous system electrically. Recent work has focused on the challenge of refining these sensations by stimulating the somatosensory cortex (S1) directly. Animals are able to detect particular patterns of stimulation and even associate those patterns with particular sensory cues. Most of this work has involved areas of the somatosensory cortex that mediate the sense of touch. Very little corresponding work has been done for proprioception. Here we describe the effort to develop afferent neural interfaces through spatiotemporally precise intracortical microstimulation (ICMS). We review what is known of the cortical representation of proprioception, and describe recent work in our lab that demonstrates for the first time, that sensations like those of natural proprioception may be evoked by ICMS in S1. These preliminary findings are an important first step to the development of an afferent cortical interface to restore proprioception.

Gating of tactile information through gamma band during passive arm movement in awake primates

To make precise and prompt action in a dynamic environment, the sensorimotor system needs to integrate all related information. The inflow of somatosensory information to the cerebral cortex is regulated and mostly suppressed by movement, which is commonly referred to as sensory gating or gating. Sensory gating plays an important role in preventing redundant information from reaching the cortex, which should be considered when designing somatosensory neuroprosthetics. Gating can occur at several levels within the sensorimotor pathway, while the underlying mechanism is not yet fully understood. The average sensory evoked potential is commonly used to assess sensory information processing, however the assumption of a stereotyped response to each stimulus is still an open question. Event related spectral perturbation (ERSP), which is the power spectrum after time-frequency decomposition on single trial evoked potentials (total power), could overcome this limitation of averaging and provide additional information for understanding the underlying mechanism. To this aim, neural activities in primary somatosensory cortex (S1), primary motor cortex (M1), and ventral posterolateral (VPL) nucleus of thalamus were recorded simultaneously in two areas (S1 and M1 or S1 and VPL) during passive arm movement and rest in awake monkeys. Our results showed that neural activity at different recording areas demonstrated specific and unique response frequency characteristics. Tactile input induced early high frequency responses followed by low frequency oscillations within sensorimotor circuits, and passive movement suppressed these oscillations either in a phase-locked or non-phase-locked manner. Sensory gating by movement was non-phase-locked in M1, and complex in sensory areas. VPL showed gating of non-phase-locked at gamma band and mix of phase-locked and non-phase-locked at low frequency, while S1 showed gating of phase-locked and non-phase-locked at gamma band and an early phase-locked elevation followed by non-phase-locked gating at low frequency. Granger causality (GC) analysis showed bidirectional coupling between VPL and S1, while GC between M1 and S1 was not responsive to tactile input. Thus, these results suggest that tactile input is dominantly transmitted along the ascending direction from VPL to S1, and the sensory input is suppressed during movement through a bottom-up strategy within the gamma-band during passive movement.

Functional and Dysfunctional Sensorimotor Anatomy and Imaging

The sensorimotor system of the human brain and body is fundamental only in its central role in our daily lives. On further examination, it is a system with intricate and complex anatomical, physiological, and functional relationships. Sensorimotor areas including primary sensorimotor, premotor, supplementary motor, and higher order somatosensory cortices are critical for function and can be localized at routine neuroimaging with a familiarity of sulcal and gyral landmarks. Likewise, a thorough understanding of the functions and dysfunctions of these areas can empower the neuroradiologist and lead to superior imaging search patterns, diagnostic considerations, and patient care recommendations in daily clinical practice. Presurgical functional brain mapping of the sensorimotor system may be necessary in scenarios with distortion of anatomical landmarks, multiplanar localization, homunculus localization, congenital brain anomalies, informing diffusion tensor imaging interpretations, and localizing nonvisible targets.

Activity of somatosensory-responsive neurons in high subdivisions of SI cortex during locomotion

Responses of neurons in the primary somatosensory cortex during movements are poorly understood, even during such simple tasks as walking on a flat surface. In this study, we analyzed spike discharges of neurons in the rostral bank of the ansate sulcus (areas 1-2) in 2 cats while the cats walked on a flat surface or on a horizontal ladder, a complex task requiring accurate stepping. All neurons (n = 82) that had receptive fields (RFs) on the contralateral forelimb exhibited frequency modulation of their activity that was phase locked to the stride cycle during simple locomotion. Neurons with proximal RFs (upper arm/shoulder) and pyramidal tract-projecting neurons (PTNs) with fast-conducting axons tended to fire at peak rates in the middle of the swing phase, whereas neurons with RFs on the distal limb (wrist/paw) and slow-conducting PTNs typically showed peak firing at the transition between swing and stance phases. Eleven of 12 neurons with tactile RFs on the volar forepaw began firing toward the end of swing, with peak activity occurring at the moment of foot contact with floor, thereby preceding the evoked sensory volley from touch receptors. Requirement to step accurately on the ladder affected 91% of the neurons, suggesting their involvement in control of accuracy of stepping. During both tasks, neurons exhibited a wide variety of spike distributions within the stride cycle, suggesting that, during either simple or ladder locomotion, they represent the cycling somatosensory events in their activity both predictively before and reflectively after these events take place.

Sequential activation of premotor, primary somatosensory and primary motor areas in humans during cued finger movements

OBJECTIVE

Human voluntary movements are a final product of complex interactions between multiple sensory, cognitive and motor areas of central nervous system. The objective was to investigate temporal sequence of activation of premotor (PM), primary motor (M1) and somatosensory (S1) areas during cued finger movements.

METHODS

Electrocorticography (ECoG) was used to measure activation timing in human PM, S1, and M1 neurons in preparation for finger movements in 5 subjects with subdural grids for seizure localization. Cortical activation was determined by the onset of high gamma (HG) oscillation (70-150Hz). The three cortical regions were mapped anatomically using a common brain atlas and confirmed independently with direct electrical cortical stimulation, somatosensory evoked potentials and detection of HG response to tactile stimulation. Subjects were given visual cues to flex each finger or pinch the thumb and index finger. Movements were captured with a dataglove and time-locked with ECoG. A windowed covariance metric was used to identify the rising slope of HG power between two electrodes and compute time lag. Statistical constraints were applied to the time estimates to combat the noise. Rank sum testing was used to verify the sequential activation of cortical regions across 5 subjects.

RESULTS

In all 5 subjects, HG activation in PM preceded S1 by an average of 53±13ms (P=0.03), PM preceded M1 by 180±40ms (P=0.001) and S1 activation preceded M1 by 136±40ms (P=0.04).

CONCLUSIONS

Sequential HG activation of PM, S1 and M1 regions in preparation for movements is reported. Activity in S1 prior to any overt body movements supports the notion that these neurons may encode sensory information in anticipation of movements, i.e., an efference copy. Our analysis suggests that S1 modulation likely originates from PM.

SIGNIFICANCE

First electrophysiological evidence of efference copy in humans.

The role of differential delays in integrating transient visual and proprioceptive information

Many actions involve limb movements toward a target. Visual and proprioceptive estimates are available online, and by optimally combining (Ernst and Banks, 2002) both modalities during the movement, the system can increase the precision of the hand estimate. The notion that both sensory modalities are integrated is also motivated by the intuition that we do not consciously perceive any discrepancy between the felt and seen hand's positions. This coherence as a result of integration does not necessarily imply realignment between the two modalities (Smeets et al., 2006). For example, the two estimates (visual and proprioceptive) might be different without either of them (e.g., proprioception) ever being adjusted after recovering the other (e.g., vision). The implication that the felt and seen positions might be different has a temporal analog. Because the actual feedback from the hand at a given instantaneous position reaches brain areas at different times for proprioception and vision (shorter for proprioception), the corresponding instantaneous unisensory position estimates will be different, with the proprioceptive one being ahead of the visual one. Based on the assumption that the system integrates optimally and online the available evidence from both senses, we introduce a temporal mechanism that explains the reported overestimation of hand positions when vision is occluded for active and passive movements (Gritsenko et al., 2007) without the need to resort to initial feedforward estimates (Wolpert et al., 1995). We set up hypotheses to test the validity of the model, and we contrast simulation-based predictions with empirical data.

Tactile information processing in primate hand somatosensory cortex (S1) during passive arm movement

Motor output mostly depends on sensory input, which also can be affected by action. To further our understanding of how tactile information is processed in the primary somatosensory cortex (S1) in dynamic environments, we recorded neural responses to tactile stimulation of the hand in three awake monkeys under arm/hand passive movement and rest. We found that neurons generally responded to tactile stimulation under both conditions and were modulated by movement: with a higher baseline firing rate, a suppressed peak rate, and a smaller dynamic range during passive movement than during rest, while the area under the response curve was stable across these two states. By using an information theory-based method, the mutual information between tactile stimulation and neural responses was quantified with rate and spatial coding models under the two conditions. The two potential encoding models showed different contributions depending on behavioral contexts. Tactile information encoded with rate coding from individual units was lower than spatial coding of unit pairs, especially during movement; however, spatial coding had redundant information between unit pairs. Passive movement regulated the mutual information, and such regulation might play different roles depending on the encoding strategies used. The underlying mechanisms of our observation most likely come from a bottom-up strategy, where neurons in S1 were regulated through the activation of the peripheral tactile/proprioceptive receptors and the interactions between these different types of information.

Somato-motor haptic processing in posterior inner perisylvian region (SII/pIC) of the macaque monkey

The posterior inner perisylvian region including the secondary somatosensory cortex (area SII) and the adjacent region of posterior insular cortex (pIC) has been implicated in haptic processing by integrating somato-motor information during hand-manipulation, both in humans and in non-human primates. However, motor-related properties during hand-manipulation are still largely unknown. To investigate a motor-related activity in the hand region of SII/pIC, two macaque monkeys were trained to perform a hand-manipulation task, requiring 3 different grip types (precision grip, finger exploration, side grip) both in light and in dark conditions. Our results showed that 70% (n = 33/48) of task related neurons within SII/pIC were only activated during monkeys’ active hand-manipulation. Of those 33 neurons, 15 (45%) began to discharge before hand-target contact, while the remaining neurons were tonically active after contact. Thirty-percent (n = 15/48) of studied neurons responded to both passive somatosensory stimulation and to the motor task. A consistent percentage of task-related neurons in SII/pIC was selectively activated during finger exploration (FE) and precision grasping (PG) execution, suggesting they play a pivotal role in control skilled finger movements. Furthermore, hand-manipulation-related neurons also responded when visual feedback was absent in the dark. Altogether, our results suggest that somato-motor neurons in SII/pIC likely contribute to haptic processing from the initial to the final phase of grasping and object manipulation. Such motor-related activity could also provide the somato-motor binding principle enabling the translation of diachronic somatosensory inputs into a coherent image of the explored object.

The representation of egocentric space in the posterior parietal cortex

The posterior parietal cortex (PPC) is the most likely site where egocentric spatial relationships are represented in the brain. PPC cells receive visual, auditory, somaesthetic, and vestibular sensory inputs; oculomotor, head, limb, and body motor signals; and strong motivational projections from the limbic system. Their discharge increases not only when an animal moves towards a sensory target, but also when it directs its attention to it. PPC lesions have the opposite effect: sensory inattention and neglect. The PPC does not seem to contain a "map" of the location of objects in space but a distributed neural network for transforming one set of sensory vectors into other sensory reference frames or into various motor coordinate systems. Which set of transformation rules is used probably depends on attention, which selectively enhances the synapses needed for making a particular sensory comparison or aiming a particular movement.

Directional information from neuronal ensembles in the primate orofacial sensorimotor cortex

Neurons in the arm and orofacial regions of the sensorimotor cortex in behaving monkeys display directional tuning of their activity during arm reaching and tongue protrusion, respectively. While studies on population activity abound for the arm motor cortex, how populations of neurons from the orofacial sensorimotor cortex represent direction has never been described. We therefore examined and compared the directional information contained in the spiking activity of populations of single neurons recorded simultaneously from chronically implanted microelectrode arrays in the orofacial primary motor (MIo, N = 345) and somatosensory (SIo, N = 336) cortices of monkeys (Macaca mulatta) as they protruded their tongue in different directions. Differential modulation to the direction of tongue protrusion was found in >60% of task-modulated neurons in MIo and SIo and was stronger in SIo (P < 0.05). Moreover, mutual information between direction and spiking was significantly higher in SIo compared with MIo at force onset and force offset (P < 0.01). Finally, the direction of tongue protrusion was accurately predicted on a trial-by-trial basis from the spiking activity of populations of MIo or SIo neurons by using a discrete decoder (P < 0.01). The highly reliable decoding was comparable between MIo and SIo neurons. However, the temporal evolution of the decoding performance differed between these two areas: MIo showed late-onset, fast-rising, and phasic performance, whereas SIo exhibited early-onset, slow-rising, and sustained performance. Overall, the results suggest that both MIo and SIo are highly involved in representing the direction of tongue protrusion but they differ in the amplitude and temporal processing of the directional information distributed across populations of neurons.

Responses of somatosensory area 2 neurons to actively and passively generated limb movements

Control of reaching movements requires an accurate estimate of the state of the limb, yet sensory signals are inherently noisy, because of both noise at the receptors themselves and the stochastic nature of the information representation by neural discharge. One way to derive an accurate representation from noisy sensor data is to combine it with the output of a forward model that considers both the previous state estimate and the noisy input. We recorded from primary somatosensory cortex (S1) in macaques (Macaca mulatta) during both active and passive movements to investigate how the proprioceptive representation of movement in S1 may be modified by the motor command (through efference copy). We found neurons in S1 that respond to one or both movement types covering a broad distribution from active movement only, to both, to passive movement only. Those neurons that responded to both active and passive movements responded with similar directional tuning. Confirming earlier results, some, but not all, neurons responded before the onset of volitional movements, possibly as a result of efference copy. Consequently, many of the features necessary to combine the forward model with proprioceptive feedback appear to be present in S1. These features would not be expected from combinations of afferent receptor responses alone.

Dissociation between neuronal activity in sensorimotor cortex and hand movement revealed as a function of movement rate

It is often assumed that similar behavior is generated by the same brain activity. However, this does not take into account the brain state or recent behavioral history and movement initiation or continuation may not be similarly generated in the brain. To study whether similar movements are generated by the same brain activity, we measured neuronal population activity during repeated movements. Three human subjects performed a motor repetition task in which they moved their hand at four different rates (0.3, 0.5, 1, and 2 Hz). From high-resolution electrocorticography arrays implanted on motor and sensory cortex, high-frequency power (65-95 Hz) was extracted as a measure of neuronal population activity. During the two faster movement rates, high-frequency power was significantly suppressed, whereas movement parameters remained highly similar. This suppression was nonlinear: after the initial movement, neuronal population activity was reduced most strongly, and the data fit a model in which a fast decline rapidly converged to saturation. The amplitude of the beta-band suppression did not change with different rates. However, at the faster rates, beta power did not return to baseline between movements but remained suppressed. We take these findings to indicate that the extended beta suppression at the faster rates, which may suggest a release of inhibition in motor cortex, facilitates movement initiation. These results show that the relationship between behavior and neuronal activity is not consistent: recent movement influences the state of motor cortex and facilitates next movements by reducing the required level of neuronal activity.

Reprogramming movements: extraction of motor intentions from cortical ensemble activity when movement goals change

The ability to inhibit unwanted movements and change motor plans is essential for behaviors of advanced organisms. The neural mechanisms by which the primate motor system rejects undesired actions have received much attention during the last decade, but it is not well understood how this neural function could be utilized to improve the efficiency of brain-machine interfaces (BMIs). Here we employed linear discriminant analysis (LDA) and a Wiener filter to extract motor plan transitions from the activity of ensembles of sensorimotor cortex neurons. Two rhesus monkeys, chronically implanted with multielectrode arrays in primary motor (M1) and primary sensory (S1) cortices, were overtrained to produce reaching movements with a joystick toward visual targets upon their presentation. Then, the behavioral task was modified to include a distracting target that flashed for 50, 150, or 250 ms (25% of trials each) followed by the true target that appeared at a different screen location. In the remaining 25% of trials, the initial target stayed on the screen and was the target to be approached. M1 and S1 neuronal activity represented both the true and distracting targets, even for the shortest duration of the distracting event. This dual representation persisted both when the monkey initiated movements toward the distracting target and then made corrections and when they moved directly toward the second, true target. The Wiener filter effectively decoded the location of the true target, whereas the LDA classifier extracted the location of both targets from ensembles of 50–250 neurons. Based on these results, we suggest developing real-time BMIs that inhibit unwanted movements represented by brain activity while enacting the desired motor outcome concomitantly.

Increased sensory feedback in Tourette syndrome

Tourette syndrome (TS) is a neuro-psychiatric disorder being characterized by motor and phonic tics typically preceded by sensory urges. Given the latter the role of the sensory system and sensorimotor interaction in TS has recently gained increased attention. 12 TS patients and 12 matched control subjects performed two tasks, requiring simple finger movements: a Go/NoGo task and a self paced movement task. Neurophysiological data was recorded using magnetoencephalography (MEG). Event related responses around movement onset, i.e. motor field (MF) occurring directly prior to the movement and movement evoked field (MEF) immediately after movement onset were analyzed using dipole modeling. MF peak amplitudes did not differ between groups in either task. In contrast, in both tasks MEF peak amplitudes were increased in TS patients. Moreover, larger MEF amplitudes during self paced movements were inversely correlated with motor tic frequency and severity. Enlarged MEF amplitudes as a marker of early sensory feedback of one's own movements probably represent enlarged sensory input from the periphery resulting from altered subcortical gating. We conclude that TS patients exhibit altered sensory-motor processing involved in voluntary movement control, which might also be successful in tic control.

Cortical correlates of fitts' law

Fitts’ law describes the fundamental trade-off between movement accuracy and speed: it states that the duration of reaching movements is a function of target size (TS) and distance. While Fitts’ law has been extensively studied in ergonomics and has guided the design of human–computer interfaces, there have been few studies on its neuronal correlates. To elucidate sensorimotor cortical activity underlying Fitts’ law, we implanted two monkeys with multielectrode arrays in the primary motor (M1) and primary somatosensory (S1) cortices. The monkeys performed reaches with a joystick-controlled cursor toward targets of different size. The reaction time (RT), movement time, and movement velocity changed with TS, and M1 and S1 activity reflected these changes. Moreover, modifications of cortical activity could not be explained by changes of movement parameters alone, but required TS as an additional parameter. Neuronal representation of TS was especially prominent during the early RT period where it influenced the slope of the firing rate rise preceding movement initiation. During the movement period, cortical activity was correlated with movement velocity. Neural decoders were applied to simultaneously decode TS and motor parameters from cortical modulations. We suggest that sensorimotor cortex activity reflects the characteristics of both the movement and the target. Classifiers that extract these parameters from cortical ensembles could improve neuroprosthetic control.

Limb-state information encoded by peripheral and central somatosensory neurons: implications for an afferent interface

A major issue to be addressed in the development of neural interfaces for prosthetic control is the need for somatosensory feedback. Here, we investigate two possible strategies: electrical stimulation of either dorsal root ganglia (DRG) or primary somatosensory cortex (S1). In each approach, we must determine a model that reflects the representation of limb state in terms of neural discharge. This model can then be used to design stimuli that artificially activate the nervous system to convey information about limb state to the subject. Electrically activating DRG neurons using naturalistic stimulus patterns, modeled on recordings made during passive limb movement, evoked activity in S1 that was similar to that of the original movement. We also found that S1 neural populations could accurately discriminate different patterns of DRG stimulation across a wide range of stimulus pulse-rates. In studying the neural coding in S1, we also decoded the kinematics of active limb movement using multi-electrode recordings in the monkey. Neurons having both proprioceptive and cutaneous receptive fields contributed equally to this decoding. Some neurons were most informative of limb state in the recent past, but many others appeared to signal upcoming movements suggesting that they also were modulated by an efference copy signal. Finally, we show that a monkey was able to detect stimulation through a large percentage of electrodes implanted in area 2. We discuss the design of appropriate stimulus paradigms for conveying time-varying limb state information, and the relative merits and limitations of central and peripheral approaches.

Does the nervous system depend on kinesthetic information to control natural limb movements?

This target article draws together two groups of experimental studies on the control of human movement through peripheral feedback and centrally generated signals of motor commands. First, during natural movement, feedback from muscle, joint, and cutaneous afferents changes; in human subjects these changes have reflex and kinesthetic consequences. Recent psychophysical and microneurographic evidence suggests that joint and even cutaneous afferents may have a proprioceptive role. Second, the role of centrally generated motor commands in the control of normal movements and movements following acute and chronic deafferentation is reviewed. There is increasing evidence that subjects can perceive their motor commands under various conditions, but that this is inadequate for normal movement; deficits in motor performance arise when the reliance on proprioceptive feedback is abolished either experimentally or because of pathology. During natural movement, the CNS appears to have access to functionally useful input from a range of peripheral receptors as well as from internally generated command signals. The unanswered questions that remain suggest a number of avenues for further research.

Implications of neural networks for how we think about brain function

Engineers use neural networks to control systems too complex for conventional engineering solutions. To examine the behavior of individual hidden units would defeat the purpose of this approach because it would be largely uninterpretable. Yet neurophysiologists spend their careers doing just that! Hidden units contain bits and scraps of signals that yield only arcane hints about network function and no information about how its individual units process signals. Most literature on single-unit recordings attests to this grim fact. On the other hand, knowing a system's function and describing it with elegant mathematics tell one very little about what to expect of interneuronal behavior. Examples of simple networks based on neurophysiology are taken from the oculomotor literature to suggest how single-unit interpretability might decrease with increasing task complexity. It is argued that trying to explain how any real neural network works on a cell-by-cell, reductionist basis is futile and we may have to be content with trying to understand the brain at higher levels of organization.