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

Protocol for state-based decoding of hand movement parameters using neural signals

We present a protocol for decoding kinematic and kinetic parameters from the primary somatosensory cortex during active and passive hand movements in a center-out reaching task using state-based and conventional decoders. We describe steps for preparing data and using the state-based model to classify movement directions into states via feature extraction and predict parameters with regression models (partial least squares and multilinear regression) trained per state. This state-based approach outperforms conventional methods, enhancing accuracy for brain-computer interface applications. For complete details on the use and execution of this protocol, please refer to Mirfathollahi et al.1.

Different Sensory Information Is Used for State Estimation when Stationary or Moving

The accurate estimation of limb state is necessary for movement planning and execution. While state estimation requires both feedforward and feedback information, we focus here on the latter. Prior literature has shown that integrating visual and proprioceptive feedback improves estimates of static limb position. However, differences in visual and proprioceptive feedback delays suggest that multisensory integration could be disadvantageous when the limb is moving. We formalized this hypothesis by modeling feedback-based state estimation using the long-standing maximum likelihood estimation model of multisensory integration, which we updated to account for sensory delays. Our model predicted that the benefit of multisensory integration was largely lost when the limb was passively moving. We tested this hypothesis in a series of experiments in human subjects that compared the degree of interference created by discrepant visual or proprioceptive feedback when estimating limb position either statically at the end of the movement or dynamically at movement midpoint. In the static case, we observed significant interference: discrepant feedback in one modality systematically biased sensory estimates based on the other modality. However, no interference was seen in the dynamic case: participants could ignore sensory feedback from one modality and accurately reproduce the motion indicated by the other modality. Together, these findings suggest that the sensory feedback used to compute a state estimate differs depending on whether the limb is stationary or moving. While the former may tend toward multimodal integration, the latter is more likely to be based on feedback from a single sensory modality.

Different sensory information is used for state estimation when stationary or moving

The accurate estimation of limb state is necessary for movement planning and execution. While state estimation requires both feedforward and feedback information, we focus here on the latter. Prior literature has shown that integrating visual and proprioceptive feedback improves estimates of static limb position. However, differences in visual and proprioceptive feedback delays suggest that multisensory integration could be disadvantageous when the limb is moving. We formalized this hypothesis by modeling feedback-based state estimation using the longstanding maximum likelihood estimation model of multisensory integration, which we updated to account for sensory delays. Our model predicted that the benefit of multisensory integration was largely lost when the limb was passively moving. We tested this hypothesis in a series of experiments in human subjects that compared the degree of interference created by discrepant visual or proprioceptive feedback when estimating limb position either statically at the end of the movement or dynamically at movement midpoint. In the static case, we observed significant interference: discrepant feedback in one modality systematically biased sensory estimates based on the other modality. However, no interference was seen in the dynamic case: participants could ignore sensory feedback from one modality and accurately reproduce the motion indicated by the other modality. Together, these findings suggest that the sensory feedback used to compute a state estimate differs depending on whether the limb is stationary or moving. While the former may tend toward multimodal integration, the latter is more likely to be based on feedback from a single sensory modality.

A neural surveyor to map touch on the body

Perhaps the most recognizable “sensory map” in neuroscience is the somatosensory homunculus. Although the homunculus suggests a direct link between cortical territory and body part, the relationship is actually ambiguous without a decoder that knows this mapping. How the somatosensory system derives a spatial code from an activation in the homunculus is a longstanding mystery we aimed to solve. We propose that touch location is disambiguated using multilateration, a computation used by surveying and global positioning systems to localize objects. We develop a Bayesian formulation of multilateration, which we implement in a neural network to identify its computational signature. We then detect this signature in psychophysical experiments. Our results suggest that multilateration provides the homunculus-to-body mapping necessary for localizing touch.

Perhaps the most recognizable sensory map in all of neuroscience is the somatosensory homunculus. Although it seems straightforward, this simple representation belies the complex link between an activation in a somatotopic map and the associated touch location on the body. Any isolated activation is spatially ambiguous without a neural decoder that can read its position within the entire map, but how this is computed by neural networks is unknown. We propose that the somatosensory system implements multilateration, a common computation used by surveying and global positioning systems to localize objects. Specifically, to decode touch location on the body, multilateration estimates the relative distance between the afferent input and the boundaries of a body part (e.g., the joints of a limb). We show that a simple feedforward neural network, which captures several fundamental receptive field properties of cortical somatosensory neurons, can implement a Bayes-optimal multilateral computation. Simulations demonstrated that this decoder produced a pattern of localization variability between two boundaries that was unique to multilateration. Finally, we identify this computational signature of multilateration in actual psychophysical experiments, suggesting that it is a candidate computational mechanism underlying tactile localization.

Somatosensory inputs modulate the excitability of cerebellar-cortical interaction

OBJECTIVE

Transcranial magnetic stimulation (TMS) delivered over the cerebellum 5-7 ms prior to a stimulus over the contralateral primary motor cortex (M1) reduces the excitability of M1 output, a phenomenon termed cerebellar brain inhibition (CBI). The cerebellum receives sensory information for adaptive motor coordination and motor planning. Here, we explored through TMS whether a peripheral electrical stimulus modulates CBI.

METHODS

We studied the effect of right median nerve electrical stimulation (ES) on CBI from right cerebellum (conditioning stimulus, CS) to left M1 (test stimulus, TS) in 12 healthy subjects. The following ES-CS inter-stimulus intervals (ISIs) were tested: 25, 30 and 35 ms. CS-TS ISI was set at 5 ms.

RESULTS

We found significantly weaker CBI when the ES was delivered 25 ms (p < 0.001) and 35 ms (p < 0.001) earlier the CS over the ipsilateral cerebellum and a trend for 30 ms ES-CS ISI (p = 0.07).

CONCLUSIONS

We hypothesize that the activation of cerebellar interneurons together with intrinsic properties of Purkinje cells may be responsible of the decreased CBI when the peripheral stimulation preceded the cerebellar stimulation of 25 and 35 ms.

SIGNIFICANCE

To test the interaction between somatosensory inputs and cerebello-cortical pathway may be important in a variety of motor tasks and neuropsychiatric disorders.

Mirror neurons are modulated by grip force and reward expectation in the sensorimotor cortices (S1, M1, PMd, PMv)

Mirror Neurons (MNs) respond similarly when primates make or observe grasping movements. Recent work indicates that reward expectation influences rostral M1 (rM1) during manual, observational, and Brain Machine Interface (BMI) reaching movements. Previous work showed MNs are modulated by subjective value. Here we expand on the above work utilizing two non-human primates (NHPs), one male Macaca Radiata (NHP S) and one female Macaca Mulatta (NHP P), that were trained to perform a cued reward level isometric grip-force task, where the NHPs had to apply visually cued grip-force to move and transport a virtual object. We found a population of (S1 area 1–2, rM1, PMd, PMv) units that significantly represented grip-force during manual and observational trials. We found the neural representation of visually cued force was similar during observational trials and manual trials for the same units; however, the representation was weaker during observational trials. Comparing changes in neural time lags between manual and observational tasks indicated that a subpopulation fit the standard MN definition of observational neural activity lagging the visual information. Neural activity in (S1 areas 1–2, rM1, PMd, PMv) significantly represented force and reward expectation. In summary, we present results indicating that sensorimotor cortices have MNs for visually cued force and value.

Spinal cord direct current stimulation differentially modulates neuronal activity in the dorsal and ventral spinal cord

Spinal cord direct current stimulation (sDCS) has the potential for promoting motor function after injury through its modulatory actions on sensory processing, reflex functions, the motor cortex (M1) motor map, and motor output. Here we addressed systems-level mechanisms underlying sDCS neuromodulation of spinal circuits activated by M1 and peripheral forelimb electrical stimulation in anesthetized healthy rats. We determined the effects of cathodal and anodal sDCS (c- and a-sDCS) on local field potentials (LFP) and single-unit activity recorded at 32 sites simultaneously within the sixth cervical segment using a silicon multielectrode array. M1 stimulation produced distinctive dorsomedial and ventral LFP responses that showed polarity-dependent sDCS modulation. c-sDCS enhanced and a-sDCS depressed significantly ventral M1 responses; neither modulated dorsal responses significantly. Using evoked changes in β- and γ-oscillations to assay network function, c-sDCS enhanced and a-sDCS reduced oscillation power ventrally. c-sDCS increased and a-sDCS decreased background firing and firing synchrony of recorded pairs of single units. Peripheral stimulation produced a region-dependent response that showed polarity-dependent sDCS modulation. The dorsomedial LFP was unaffected by c-sDCS and weakly suppressed with a-sDCS. Peripheral-evoked unit responses showed limited polarity dependence. Our findings stress that ventral motor network behavior is enhanced by the neuromodulatory actions of c-sDCS. The combined actions of c-sDCS on M1-evoked neural responses and network behavior in the cervical spinal cord help explain the reported enhanced motor effects of this neuromodulation approach and inform the mechanisms of sDCS for promoting motor rehabilitation after spinal cord or brain injury.NEW & NOTEWORTHY Spinal cord direct current stimulation (sDCS) modulates spinal functions and shows potential for neural rehabilitation after motor systems injury. Using a multichannel electrode array, we found that cathodal DCS enhanced, and anodal depressed, M1-evoked local field potentials, network oscillations, neuronal activity, and neuronal synchrony, especially in the ventral horn. With this new understanding, it is hoped that sDCS can be developed into a tunable spinal neuromodulatory tool for promoting function after brain or spinal injury.

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.

Tactile representation in somatosensory thalamus (VPL) and cortex (S1) of awake primate and the plasticity induced by VPL neuroprosthetic stimulation

To further understand how tactile information is carried in somatosensory cortex (S1) and the thalamus (VPL), and how neuronal plasticity after neuroprosthetic stimulation affects sensory encoding, we chronically implanted microelectrode arrays across hand areas in both S1 and VPL, where neuronal activities were simultaneously recorded during tactile stimulation on the finger pad of awake monkeys. Tactile information encoded in the firing rate of individual units (rate coding) or in the synchrony of unit pairs (synchrony coding) was quantitatively assessed within the information theoretic-framework. We found that tactile information encoded in VPL was higher than that encoded in S1 for both rate coding and synchrony coding; rate coding carried greater information than synchrony coding for the same recording area. With the aim for neuroprosthetic stimulation, plasticity of the circuit was tested after 30 min of VPL electrical stimulation, where stimuli were delivered either randomly or contingent on the spiking of an S1 unit. We showed that neural encoding in VPL was more stable than in S1, which depends not only on the thalamic input but also on recurrent feedback. The percent change of mutual-information after stimulation was increased with closed-loop stimulation, but decreased with random stimulation. The underlying mechanisms during closed-loop stimulation might be spike-timing-dependent plasticity, while frequency-dependent synaptic plasticity might play a role in random stimulation. Our results suggest that VPL could be a promising target region for somatosensory stimulation with closed-loop brain-machine-interface applications.