Matthew D. Best

Millisecond Precision Spike Timing Shapes Tactile Perception

In primates, the sense of touch has traditionally been considered to be a spatial modality, drawing an analogy to the visual system. In this view, stimuli are encoded in spatial patterns of activity over the sheet of receptors embedded in the skin. We propose that the spatial processing mode is complemented by a temporal one. Indeed, the transduction and processing of complex, high-frequency skin vibrations have been shown to play an important role in tactile texture perception, and the frequency composition of vibrations shapes the evoked percept. Mechanoreceptive afferents innervating the glabrous skin exhibit temporal patterning in their responses, but the importance and behavioral relevance of spike timing, particularly for naturalistic stimuli, remains to be elucidated. Based on neurophysiological recordings from Rhesus macaques, we show that spike timing conveys information about the frequency composition of skin vibrations, both for individual afferents and for afferent populations, and that the temporal fidelity varies across afferent class. Furthermore, the perception of skin vibrations, measured in human subjects, is better predicted when spike timing is taken into account, and the resolution that predicts perception best matches the optimal resolution of the respective afferent classes. In light of these results, the peripheral representation of complex skin vibrations draws a powerful analogy with the auditory and vibrissal systems.

Large-scale spatiotemporal spike patterning consistent with wave propagation in motor cortex

Aggregate signals in cortex are known to be spatiotemporally organized as propagating waves across the cortical surface, but it remains unclear whether the same is true for spiking activity in individual neurons. Furthermore, the functional interactions between cortical neurons are well documented but their spatial arrangement on the cortical surface has been largely ignored. Here we use a functional network analysis to demonstrate that a subset of motor cortical neurons in non-human primates spatially coordinate their spiking activity in a manner that closely matches wave propagation measured in the beta oscillatory band of the local field potential. We also demonstrate that sequential spiking of pairs of neuron contains task-relevant information that peaks when the neurons are spatially oriented along the wave axis. We hypothesize that the spatial anisotropy of spike patterning may reflect the underlying organization of motor cortex and may be a general property shared by other cortical areas.

Spatio-Temporal Patterning in Primary Motor Cortex at Movement Onset

Abstract Voluntary movement initiation involves the engagement of large populations of motor cortical neurons around movement onset. Despite knowledge of the temporal dynamics that lead to movement, the spatial structure of these dynamics across the cortical surface remains unknown. In data from 4 rhesus macaques, we show that the timing of attenuation of beta frequency local field potential oscillations, a correlate of locally activated cortex, forms a spatial gradient across primary motor cortex (MI). We show that these spatio‐temporal dynamics are recapitulated in the engagement order of ensembles of MI neurons. We demonstrate that these patterns are unique to movement onset and suggest that movement initiation requires a precise spatio‐temporal sequential activation of neurons in MI.

Encoding of both reaching and grasping kinematics in dorsal and ventral premotor cortices

Classically, it has been hypothesized that reach-to-grasp movements arise from two discrete parietofrontal cortical networks. As part of these networks, the dorsal premotor cortex (PMd) has been implicated in the control of reaching movements of the arm, whereas the ventral premotor cortex (PMv) has been associated with the control of grasping movements of the hand. Recent studies have shown that such a strict delineation of function along anatomical boundaries is unlikely, partly because reaching to different locations can alter distal hand kinematics and grasping different objects can affect kinematics of the proximal arm. Here, we used chronically implanted multielectrode arrays to record unit-spiking activity in both PMd and PMv simultaneously while rhesus macaques engaged in a reach-to-grasp task. Generalized linear models were used to predict the spiking activity of cells in both areas as a function of different kinematic parameters, as well as spike history. To account for the influence of reaching on hand kinematics and vice versa, we applied demixed principal components analysis to define kinematics synergies that maximized variance across either different object locations or grip types. We found that single cells in both PMd and PMv encode the kinematics of both reaching and grasping synergies, suggesting that this classical division of reach and grasp in PMd and PMv, respectively, does not accurately reflect the encoding preferences of cells in those areas. SIGNIFICANCE STATEMENT For reach-to-grasp movements, the dorsal premotor cortex (PMd) has been implicated in the control of reaching movements of the arm, whereas the ventral premotor cortex (PMv) has been associated with the control of grasping movements of the hand. We recorded unit-spiking activity in PMd and PMv simultaneously while macaques performed a reach-to-grasp task. We modeled the spiking activity of neurons as a function of kinematic parameters and spike history. We applied demixed principal components analysis to define kinematics synergies. We found that single units in both PMd and PMv encode the kinematics of both reaching and grasping synergies, suggesting that the division of reach and grasp in PMd and PMv, respectively, cannot be made based on their encoding properties.

Ultra-long term stability of single units using chronically implanted multielectrode arrays.

Recordings from chronically implanted multielectrode arrays have become prevalent in both neuroscience and neural engineering experiments. To date, however, the extent to which populations of single-units remain stable over long periods of time has not been well characterized. In this study, neural activity was recorded from a Utah multielectrode array implanted in the primary motor cortex of a rhesus macaque during 18 recording sessions spanning nine months. We found that 67% of the units were stable through the first 15 days, 31% of units were stable through 47 days, 21% of units were stable through 106 days, and 8% of units were stable over 9 months. Thus not only were units stable over a timescale of several months, but units stable over 2 months were more likely to remain stable in the next 2 months.

Comparing offline decoding performance in physiologically defined neuronal classes.

Objective Recently, several studies have documented the presence of a bimodal distribution of spike waveform widths in primary motor cortex. Although narrow and wide spiking neurons, corresponding to the two modes of the distribution, exhibit different response properties, it remains unknown if these differences give rise to differential decoding performance between these two classes of cells. Approach We used a Gaussian mixture model to classify neurons into narrow and wide physiological classes. Using similar-size, random samples of neurons from these two physiological classes, we trained offline decoding models to predict a variety of movement features. We compared offline decoding performance between these two physiologically defined populations of cells. Main results We found that narrow spiking neural ensembles decode motor parameters better than wide spiking neural ensembles including kinematics, kinetics, and muscle activity. Significance These findings suggest that the utility of neural ensembles in brain machine interfaces may be predicted from their spike waveform widths.

Semiautomatic marker tracking of tongue positions captured by videofluoroscopy during primate feeding

Videofluoroscopy (VF) is one of the most commonly used tools to assess oropharyngeal dysphagia as well as to visualize musculoskeletal structures of humans and animals engaged in various behaviors, including feeding. Despite its importance in clinical and scientific use, processing VF data has historically been extremely tedious because it is performed using manual frame-by-frame methods. With recent technological advances, the frame rate for scientific use has been increasing along with the use of high speed data capture systems. In the current study, we used non-human primates as a model animal to study human feeding behaviors to capture tongue movement based on markers implanted into the tongue. Here, we introduce a semi-automatic marker tracking algorithm that yields high tracking accuracy (> 90%) and dramatic speed improvements (faster than real time labeling). Furthermore, we quantify the sources of tracking errors and the tracking performance as a function of marker speeds. Our results indicate that there is more room for methodological improvements both in detection and prediction of marker positions. Moreover, correspondingly faster frame rates will be required to capture faster kinematic behaviors such as those of mice, which are extensively used to study both control and pathological conditions.

Consideration of the functional relationship between cortex and motor periphery improves offline decoding performance

Decoding neural activity to control prosthetic devices or computer interfaces is a promising avenue for rehabilitating individuals with amputation or severe spinal cord injury. In most cases, however, the local functionality of the neural tissue is not considered when designing a decoding algorithm. One way to characterize the functional specificity of a local region of motor cortex, and its output effects, is to use intracortical microstimulation. In this study, we examined how the results of an ICMS experiment relate to the performance of various offline decoders. We found evidence that units from electrodes with stimulation effects decode kinematics better than units from electrodes without stimulation effects.

Granger causality analysis of state dependent functional connectivity of neurons in orofacial motor cortex during chewing and swallowing

Primate feeding behavior is characterized by a series of jaw movement cycles of different types making it ideal for investigating the role of motor cortex in controlling transitions between different kinematic states. We recorded spiking activity in populations of neurons in the orofacial portion of primary motor cortex (MIo) of a macaque monkey and, using a Granger causality model, estimated their functional connectivity during transitions between chewing cycles and from chewing to swallowing cycles. We found that during rhythmic chewing, the network was dominated by excitatory connections and exhibited a few “out degree” hub neurons, while during transitions from rhythmic chews to swallows, the numbers of excitatory and inhibitory connections became comparable, and more temporarily varying “in degree” hub neurons emerged. Furthermore, based on shared connections between neurons between different networks, networks from same state transitions were quantitatively shown to be more similar. These results suggest that networks of functionally connected neurons in MIo change their operative states with changes in kinematically defined behavioral states.

Comparing decoding performance between functionally defined neural populations

Neurons in primary motor cortex can be divided into functional populations based on the width of their spike waveforms. These ensembles have different response properties that may subserve different roles in movement generation. Yet, how these differences impact offline decoding performance remains unknown. Here, we show that neurons exhibiting narrow spike waveforms outperform wide spiking neurons in decoding several features of movement. We further examine how decoding performance scales with respect to the number of neurons in the decoder, and show that an ensemble containing only narrow spiking units outperforms other models. These results suggest that it may be useful to consider spike waveform width when designing neural decoders.