Temporal encoding of movement kinematics in the discharge of primate primary motor and premotor neurons

Preparatory activity and the expansive null-space

The study of the cortical control of movement experienced a conceptual shift over recent decades, as the basic currency of understanding shifted from single-neuron tuning towards population-level factors and their dynamics. This transition was informed by a maturing understanding of recurrent networks, where mechanism is often characterized in terms of population-level factors. By estimating factors from data, experimenters could test network-inspired hypotheses. Central to such hypotheses are 'output-null' factors that do not directly drive motor outputs yet are essential to the overall computation. In this Review, we highlight how the hypothesis of output-null factors was motivated by the venerable observation that motor-cortex neurons are active during movement preparation, well before movement begins. We discuss how output-null factors then became similarly central to understanding neural activity during movement. We discuss how this conceptual framework provided key analysis tools, making it possible for experimenters to address long-standing questions regarding motor control. We highlight an intriguing trend: as experimental and theoretical discoveries accumulate, the range of computational roles hypothesized to be subserved by output-null factors continues to expand.

Initial and corrective submovement encoding differences within primary motor cortex during precision reaching

Precision reaching tasks often require corrective submovements for successful completion. Most studies of reaching have focused on single initial movements, and the cortical encoding model was implied to be the same for all submovements. However, corrective submovements may show different encoding patterns from the initial submovement with distinct patterns of activation across the population. Two rhesus macaques performed a precision center-out-task with small targets. Neural activity from single units in primary motor cortex and associated behavioral data were recorded to evaluate movement characteristics. Neural population data and individual neuronal firing rates identified with a peak finding algorithm to identify peaks in hand speed were examined for encoding differences between initial and corrective submovements. Individual neurons were fitted with a regression model that included the reach vector, position, and speed to predict firing rate. For both initial and corrective submovements, the largest effect remained movement direction. We observed a large subset changed their preferred direction greater than 45° between initial and corrective submovements. Neuronal depth of modulation also showed considerable variation when adjusted for movement speed. By utilizing principal component analysis, neural trajectories of initial and corrective submovements progressed through different neural subspaces. These findings all suggest that different neural encoding patterns exist for initial and corrective submovements within the cortex. We hypothesize that this variation in how neurons change to encode small, corrective submovements might allow for a larger portion of the neural space being used to encode a greater range of movements with varying amplitudes and levels of precision.

New and Noteworthy

Neuronal recordings matched with kinematic behavior were collected in a precision center-out task that often required corrective movements. We reveal large differences in preferred direction and depth of modulation between initial and corrective submovements across the neural population. We then present a model of the neural population describing how these shifts in tuning create different subspaces for signaling initial and corrective movements likely to improve motor precision.

Transcranial Direct Current Stimulation (tDCS) modulates motor execution in a limb reaching task

The majority of human activities show a trade-off between movement speed and accuracy. Here we tested 16 participants in a quick pointing action after 20 minutes (2mA) of transcranial Direct Current Stimulation (tDCS) delivered at the Supplementary Motor Area (SMA) in a single-blind crossover design study for testing the feedforward components in the control of action. tDCS stimuli were delivered in three randomized sessions of stimulations as anodal, cathodal and sham as a control. The task performed Pre and Post tDCS stimulation, was to point as fast and as precise as possible with the big toe to targets having different sizes (2 and 8 cm; Width) and positioned at different Distances (20 and 60 cm; Distance). An optoelectronic motion capture system was used to collect the kinematics of movement. Result indicates that individuals after receiving anodal stimulation decreased their movement time and increased their movement speed while the opposite happened after receiving a cathodal stimulation. The scarcity of studies in this area invites us to plan a research that aims at the trade-off especially in the clinical settings.

The science and engineering behind sensitized brain-controlled bionic hands

Advances in our understanding of brain function, along with the development of neural interfaces that allow for the monitoring and activation of neurons, have paved the way for brain-machine interfaces (BMIs), which harness neural signals to reanimate the limbs via electrical activation of the muscles or to control extracorporeal devices, thereby bypassing the muscles and senses altogether. BMIs consist of reading out motor intent from the neuronal responses monitored in motor regions of the brain and executing intended movements with bionic limbs, reanimated limbs, or exoskeletons. BMIs also allow for the restoration of the sense of touch by electrically activating neurons in somatosensory regions of the brain, thereby evoking vivid tactile sensations and conveying feedback about object interactions. In this review, we discuss the neural mechanisms of motor control and somatosensation in able-bodied individuals and describe approaches to use neuronal responses as control signals for movement restoration and to activate residual sensory pathways to restore touch. Although the focus of the review is on intracortical approaches, we also describe alternative signal sources for control and noninvasive strategies for sensory restoration.

Lower visual field preference for the visuomotor control of limb movements in the human dorsomedial parietal cortex

Visual cues coming from the lower visual field (VF) play an important role in the visual guidance of upper and lower limb movements. A recently described region situated in the dorsomedial parietal cortex, area hPEc (Pitzalis et al. in NeuroImage 202:116092, 2019), might have a role in integrating visually derived information with somatomotor signals to guide limb interaction with the environment. In macaque, it has been demonstrated that PEc receives visual information mostly from the lower visual field but, to date, there has been no systematic investigation of VF preference in the newly defined human homologue of macaque area PEc (hPEc). Here we examined the VF preferences of hPEc while participants performed a visuomotor task implying spatially directed delayed eye-, hand- and foot-movements towards different spatial locations within the VF. By analyzing data as a function of the different target locations towards which upcoming movements were planned (and then executed), we observed the presence of asymmetry in the vertical dimension of VF in area hPEc, being this area more strongly activated by limb movements directed towards visual targets located in the lower compared to the upper VF. This result confirms the view, first advanced in macaque monkey, that PEc is involved in processing visual information to guide body interaction with the external environment, including locomotion. We also observed a contralateral dominance for the lower VF preference in the foot selective somatomotor cortex anterior to hPEc. This result might reflect the role of this cortex (which includes areas PE and S-I) in providing highly topographically organized signals, likely useful to achieve an appropriate foot posture during locomotion.

Activity in Primary Motor Cortex Related to Visual Feedback

Neural modulation in primate motor cortex exhibits complex patterns. We found that modulation during reaching could be separated into discrete temporal epochs. To determine if these epochs are driven by behavioral events, monkeys performed variations of a center-out reaching task. Monkeys viewed a computer cursor matched to hand position and a radial target at 1 of 16 locations. In some trials, they performed a visuomotor rotation (the cursor moved at an angle to the hand). After adaptation, encoding changes for single units are temporally structured: adaptation could affect one temporal component of a unit's response but not another. In half the normal and perturbed trials, we removed visual feedback before movement. Adaptation-sensitive firing components toward the end of movement are often weak or absent during reaches without feedback. These results show that temporal structure in motor cortical activity is driven by behavior, with a discrete component related to visual feedback.

Multidimensional Tuning in Motor Cortical Neurons during Active Behavior

A region within songbird cortex, dorsal intermediate arcopallium (AId), is functionally analogous to motor cortex in mammals and has been implicated in song learning during development. Non-vocal factors such as visual and social cues are known to mediate song learning and performance, yet previous chronic-recording studies of regions important for song behavior have focused exclusively on neural activity in relation to song production. Thus, we have little understanding of the range of non-vocal information that single neurons may encode. We made chronic recordings in AId of freely behaving juvenile zebra finches and evaluated neural activity during diverse motor behaviors throughout entire recording sessions, including song production as well as hopping, pecking, preening, fluff-ups, beak interactions, scratching, and stretching. These movements are part of natural behavioral repertoires and are important components of both song learning and courtship behavior. A large population of AId neurons showed significant modulation of activity during singing. In addition, single neurons demonstrated heterogeneous response patterns during multiple movements (including excitation during one movement type and suppression during another), and some neurons showed differential activity depending on the context in which movements occurred. Moreover, we found evidence of neurons that did not respond during discrete movements but were nonetheless modulated during active behavioral states compared with quiescence. Our results suggest that AId neurons process both vocal and non-vocal information, highlighting the importance of considering the variety of multimodal factors that can contribute to vocal motor learning during development.

Spatially and Temporally Distinct Encoding of Muscle and Kinematic Information in Rostral and Caudal Primary Motor Cortex

The organizing principle of human motor cortex does not follow an anatomical body map, but rather a distributed representational structure in which motor primitives are combined to produce motor outputs. Electrophysiological recordings in primates and human imaging data suggest that M1 encodes kinematic features of movements, such as joint position and velocity. However, M1 exhibits well-documented sensory responses to cutaneous and proprioceptive stimuli, raising questions regarding the origins of kinematic motor representations: are they relevant in top-down motor control, or are they an epiphenomenon of bottom-up sensory feedback during movement? Here we provide evidence for spatially and temporally distinct encoding of kinematic and muscle information in human M1 during the production of a wide variety of naturalistic hand movements. Using a powerful combination of high-field functional magnetic resonance imaging and magnetoencephalography, a spatial and temporal multivariate representational similarity analysis revealed encoding of kinematic information in more caudal regions of M1, over 200 ms before movement onset. In contrast, patterns of muscle activity were encoded in more rostral motor regions much later after movements began. We provide compelling evidence that top-down control of dexterous movement engages kinematic representations in caudal regions of M1 prior to movement production.

How does the CNS control arm reaching movements? Introducing a hierarchical nonlinear predictive control organization based on the idea of muscle synergies

In this study, we introduce a hierarchical and modular computational model to explain how the CNS (Central Nervous System) controls arm reaching movement (ARM) in the frontal plane and under different conditions. The proposed hierarchical organization was established at three levels: 1) motor planning, 2) command production, and 3) motor execution. Since in this work we are not discussing motion learning, no learning procedure was considered in the model. Previous models mainly assume that the motor planning level produces the desired trajectories of the joints and feeds it to the next level to be tracked. In the proposed model, the motion control is described based on a regulatory control policy, that is, the output of the motor planning level is a step function defining the initial and final desired position of the hand. For the command production level, a nonlinear predictive model was developed to explain how the time-invariant muscle synergies (MSs) are recruited. We used the same computational model to explain the arm reaching motion for a combined ARM task. The combined ARM is defined as two successive ARM such that it starts from point A and reaches to point C via point B. To develop the model, kinematic and kinetic data from six subjects were recorded and analyzed during ARM task performance. The subjects used a robotic manipulator while moving their hand in the frontal plane. The EMG data of 15 muscles were also recorded. The MSs used in the model were extracted from the recorded EMG data. The proposed model explains two aspects of the motor control system by a novel computational approach: 1) the CNS reduces the dimension of the control space using the notion of MSs and thereby, avoids immense computational loads; 2) at the level of motor planning, the CNS generates the desired position of the hand at the starting, via and the final points, and this amounts to a regulatory and non-tracking structure.

Bilateral Transcranial Direct Stimulation Over the Primary Motor Cortex Alters Motor Modularity of Multiple Muscles

Transcranial direct current stimulation (tDCS) over the primary motor cortex (M1) has been demonstrated to modulate the motor performance of both healthy individuals and patients with neuromuscular disorders. However, the effect of tDCS on motor control of multiple muscles, which is a prerequisite to change in motor performance, is currently unknown. Using dimensionality reduction analysis, we investigated whether bilateral tDCS over M1 modulates the coordinated activity of 12 muscles. Fifteen healthy men participated in this randomized, double-blind crossover study. Each participant received a 20-min sham and 2-mA stimulation bilaterally over M1 (anode on the right M1 and cathode on the left M1), with a minimum washout period of 4 days. Muscle activation and end-point kinematics were evaluated during a task where participants reached out to a marked target with non-dominant hand as fast as possible, before and immediately after tDCS application. We found decreased similarity in motor modularity and significant changes in muscle activation in a specific motor module, particularly when reaching out to a target placed within arm's length and improved smoothness index of movement only following 2-mA stimulation. These findings indicate that clinicians and researchers need to consider the simultaneous effect of bilateral tDCS over M1 on multiple muscles when they establish tDCS protocol to change in motor performance of patients with neuromuscular deficits.

Condition-Dependent Neural Dimensions Progressively Shift during Reach to Grasp

Neural population space analysis was performed to assess the dimensionality and dynamics of the neural population in the primary motor cortex (M1) during a reach-grasp-manipulation task in which both the reach location and the object being grasped were varied. We partitioned neural activity into three components: (1) general task-related activity independent of location and object, (2) location- and/or object-related activity, and (3) noise. Neural modulation related to location and/or object was only one-third the size of either general task modulation or noise. The neural dimensions of location and/or object-related activity overlapped with both the general task and noise dimensions. Rather than large amplitude modulation in a fixed set of dimensions, the active dimensions of location and/or object modulation shifted progressively over the time course of a trial.

Rouse and Schieber show that during reach-grasp-manipulate movements, M1 activity related to location and object occurs not in a fixed set but rather in a shifting set of neural dimensions that overlap with those of general task and noise activity.

Latent Factors and Dynamics in Motor Cortex and Their Application to Brain-Machine Interfaces

In the 1960s, Evarts first recorded the activity of single neurons in motor cortex of behaving monkeys (Evarts, 1968). In the 50 years since, great effort has been devoted to understanding how single neuron activity relates to movement. Yet these single neurons exist within a vast network, the nature of which has been largely inaccessible. With advances in recording technologies, algorithms, and computational power, the ability to study these networks is increasing exponentially. Recent experimental results suggest that the dynamical properties of these networks are critical to movement planning and execution. Here we discuss this dynamical systems perspective and how it is reshaping our understanding of the motor cortices. Following an overview of key studies in motor cortex, we discuss techniques to uncover the "latent factors" underlying observed neural population activity. Finally, we discuss efforts to use these factors to improve the performance of brain-machine interfaces, promising to make these findings broadly relevant to neuroengineering as well as systems neuroscience.

Encoding of kinetic and kinematic movement parameters in the sensorimotor cortex: A Brain-Computer Interface perspective

For severely paralyzed people, Brain-Computer Interfaces (BCIs) can potentially replace lost motor output and provide a brain-based control signal for augmentative and alternative communication devices or neuroprosthetics. Many BCIs focus on neuronal signals acquired from the hand area of the sensorimotor cortex, employing changes in the patterns of neuronal firing or spectral power associated with one or more types of hand movement. Hand and finger movement can be described by two groups of movement features, namely kinematics (spatial and motion aspects) and kinetics (muscles and forces). Despite extensive primate and human research, it is not fully understood how these features are represented in the SMC and how they lead to the appropriate movement. Yet, the available information may provide insight into which features are most suitable for BCI control. To that purpose, the current paper provides an in-depth review on the movement features encoded in the SMC. Even though there is no consensus on how exactly the SMC generates movement, we conclude that some parameters are well represented in the SMC and can be accurately used for BCI control with discrete as well as continuous feedback. However, the vast evidence also suggests that movement should be interpreted as a combination of multiple parameters rather than isolated ones, pleading for further exploration of sensorimotor control models for accurate BCI control.

Parieto-frontal mechanisms underlying observation of complex hand-object manipulation

The observation of actions performed by others is believed to activate the Action Observation Network (AON). Previous evidence suggests that subjects with a specific motor skill show increased activation of the AON during observation of the same skill. The question arises regarding which modulation of the AON occurs during observation of novel complex manipulative actions that are beyond the personal motor repertoire. To address this issue, we carried out a functional MRI study in which healthy volunteers without specific hand motor skills observed videos displaying hand-object manipulation executed by an expert with high manual dexterity, by an actor with intermediate ability or by a naïve subject. The results showed that the observation of actions performed by a naïve model produced stronger activation in a dorso-medial parieto-premotor circuit including the superior parietal lobule and dorsal premotor cortex, compared to observation of an expert actor. Functional connectivity analysis comparing the observation of the naïve model with that of the expert model, revealed increased connectivity between dorsal areas of the AON. This suggests a possible distinction between ventral and dorsal brain circuits involved in the processing of different aspects of action perception, such as kinematics and final action goal.

Indirect, referent control of motor actions underlies directional tuning of neurons

Many neurons of the primary motor cortex (M1) are maximally sensitive to "preferred" hand movement directions and generate progressively less activity with movements away from these directions. M1 activity also correlates with other biomechanical variables. These findings are predominantly interpreted in a framework in which the brain preprograms and directly specifies the desired motor outcome. This approach is inconsistent with the empirically derived equilibrium-point hypothesis, in which the brain can control motor actions only indirectly, by changing neurophysiological parameters that may influence, but remain independent of, biomechanical variables. The controversy is resolved on the basis of experimental findings and theoretical analysis of how sensory and central influences are integrated in the presence of the fundamental nonlinearity of neurons: electrical thresholds. In the presence of sensory inputs, electrical thresholds are converted into spatial thresholds that predetermine the position of the body segments at which muscles begin to be activated. Such thresholds may be considered as referent points of respective spatial frames of reference (FRs) in which neurons, including motoneurons, are centrally predetermined to work. By shifting the referent points of respective FRs, the brain elicits intentional actions. Pure involuntary reactions to perturbations are accomplished in motionless FRs. Neurons are primarily sensitive to shifts in referent directions, i.e., shifts in spatial FRs, whereas emergent neural activity may or may not correlate with different biomechanical variables depending on the motor task and external conditions. Indirect, referent control of posture and movement symbolizes a departure from conventional views based on direct preprogramming of the motor outcome.

An automated behavioral apparatus to combine parameterized reaching and grasping movements in 3D space

BACKGROUND

The neural principles underlying reaching and grasping movements have been studied extensively in primates for decades. However, few experimental apparatuses have been developed to enable a flexible combination of reaching and grasping in one task in three-dimensional (3D) space.

NEW METHOD

By combining a custom turning table with a 3D translational device, we have developed a highly flexible apparatus that enables the subject to reach multiple positions in 3D space, and grasp differently shaped objects with multiple grip types in each position. Meanwhile, hand trajectory and grip types can be recorded via optical motion tracking cameras and touch sensors, respectively.

RESULTS

We have used the apparatus to successfully train a macaque monkey to accomplish a visually-guided reach-to-grasp task, in which, six objects, fixed on the turning table, were grasped appropriately when they were transported to multiple positions in 3D space. A preliminary analysis of neural signals recorded in primary motor cortex, shows that plenty of neurons exhibit significant tuning to both target position and grip type.

COMPARISON WITH EXISTING METHOD(S)

Our apparatus realizes an arbitrary combination of parameterized reaching and grasping movements in a single task, which were usually separated or fixed in other systems. Meanwhile, the apparatus has high expansibility in terms of dynamic range, object shapes and applicable subjects.

CONCLUSIONS

The apparatus provides a valuable platform to study upper limb functions in behavioral and neurophysiological studies, and may facilitate simultaneous reconstruction of reaching and grasping movements in brain-machine interfaces (BMIs).

Cortical population activity within a preserved neural manifold underlies multiple motor behaviors

Populations of cortical neurons flexibly perform different functions; for the primary motor cortex (M1) this means a rich repertoire of motor behaviors. We investigate the flexibility of M1 movement control by analyzing neural population activity during a variety of skilled wrist and reach-to-grasp tasks. We compare across tasks the neural modes that capture dominant neural covariance patterns during each task. While each task requires different patterns of muscle and single unit activity, we find unexpected similarities at the neural population level: the structure and activity of the neural modes is largely preserved across tasks. Furthermore, we find two sets of neural modes with task-independent activity that capture, respectively, generic temporal features of the set of tasks and a task-independent mapping onto muscle activity. This system of flexibly combined, well-preserved neural modes may underlie the ability of M1 to learn and generate a wide-ranging behavioral repertoire.

Added value of money on motor performance feedback: Increased left central beta-band power for rewards and fronto-central theta-band power for punishments

Monetary rewards and punishments have been shown to respectively enhance retention of motor memories and short-term motor performance, but their underlying neural bases in the context of motor control tasks remain unclear. Using electroencephalography (EEG), the present study tested the hypothesis that monetary rewards and punishments are respectively reflected in post-feedback beta-band (20-30 Hz) and theta-band (3-8 Hz) oscillatory power. While participants performed upper limb reaching movements toward visual targets using their right hand, the delivery of monetary rewards and punishments was manipulated as well as their probability (i.e., by changing target size). Compared to unrewarded and unpunished trials, monetary rewards and the successful avoidance of punishments both entailed greater beta-band power at left central electrodes overlaying contralateral motor areas. In contrast, monetary punishments and reward omissions both entailed increased theta-band power at fronto-central scalp sites. Additional analyses revealed that beta-band power was further increased when rewards were lowly probable. In light of previous work demonstrating similar beta-band modulations in basal ganglia during reward processing, the present results may reflect functional communication of reward-related information between the basal ganglia and motor cortical regions. In turn, the increase in fronto-central theta-band power after monetary punishments may reflect an emphasized cognitive need for behavioral adjustments. Globally, the present work identifies possible neural substrates for the growing behavioral evidence showing beneficial effects of monetary feedback on motor learning and performance.

The ipsilateral corticospinal responses to cross-education are dependent upon the motor-training intervention

This study aimed to identify the ipsilateral corticospinal responses of the contralateral limb following different types of unilateral motor-training. Three groups performing unilateral slow-paced strength training (SPST), non-paced strength training (NPST) or visuomotor skill training (VT) were compared to a control group. It was hypothesised that 4 weeks of unilateral SPST and VT, but not NPST, would increase ipsilateral corticospinal excitability (CSE) and reduce short-interval cortical inhibition (SICI), resulting in greater performance gains of the untrained limb. Tracking error of the untrained limb reduced by 29 and 41% following 2 and 4 weeks of VT. Strength of the untrained limb increased by 8 and 16% following 2 and 4 weeks of SPST and by 6 and 13% following NPST. There was no difference in cross-education of strength or tracking error. For the trained limb, SPST and NPST increased strength (28 and 26%), and VT improved by 47 and 58%. SPST and VT increased ipsilateral CSE by 89 and 71% at 2 weeks. Ipsilateral CSE increased 105 and 81% at 4 weeks following SPST and VT. The NPST group and control group showed no changes at 2 and 4 weeks. SPST and VT reduced ipsilateral SICI by 45 and 47% at 2 weeks; at 4 weeks, SPST and VT reduced SICI by 48 and 38%. The ipsilateral corticospinal responses are determined by the type of motor-training. There were no differences in motor performance between SPST, NPST and VT. The data suggests that the corticospinal responses to cross-education are different and determined by the type of motor-training.

Modeling task-specific neuronal ensembles improves decoding of grasp

OBJECTIVE

Dexterous movement involves the activation and coordination of networks of neuronal populations across multiple cortical regions. Attempts to model firing of individual neurons commonly treat the firing rate as directly modulating with motor behavior. However, motor behavior may additionally be associated with modulations in the activity and functional connectivity of neurons in a broader ensemble. Accounting for variations in neural ensemble connectivity may provide additional information about the behavior being performed.

APPROACH

In this study, we examined neural ensemble activity in primary motor cortex (M1) and premotor cortex (PM) of two male rhesus monkeys during performance of a center-out reach, grasp and manipulate task. We constructed point process encoding models of neuronal firing that incorporated task-specific variations in the baseline firing rate as well as variations in functional connectivity with the neural ensemble. Models were evaluated both in terms of their encoding capabilities and their ability to properly classify the grasp being performed.

MAIN RESULTS

Task-specific ensemble models correctly predicted the performed grasp with over 95% accuracy and were shown to outperform models of neuronal activity that assume only a variable baseline firing rate. Task-specific ensemble models exhibited superior decoding performance in 82% of units in both monkeys (p  <  0.01). Inclusion of ensemble activity also broadly improved the ability of models to describe observed spiking. Encoding performance of task-specific ensemble models, measured by spike timing predictability, improved upon baseline models in 62% of units.

SIGNIFICANCE

These results suggest that additional discriminative information about motor behavior found in the variations in functional connectivity of neuronal ensembles located in motor-related cortical regions is relevant to decode complex tasks such as grasping objects, and may serve the basis for more reliable and accurate neural prosthesis.

Developing a Nonstationary Computational Framework With Application to Modeling Dynamic Modulations in Neural Spiking Responses

OBJECTIVE

This paper aims to develop a computational model that incorporates the functional effects of modulatory covariates (such as context, task, or behavior), which dynamically alter the relationship between the stimulus and the neural response.

METHODS

We develop a general computational approach along with an efficient estimation procedure in the widely used generalized linear model (GLM) framework to characterize such nonstationary dynamics in spiking response and spatiotemporal characteristics of a neuron at the level of individual trials. The model employs a set of modulatory components, which nonlinearly interact with other stimulus-related signals to reproduce such nonstationary effects.

RESULTS

The model is tested for its ability to predict the responses of neurons in the middle temporal cortex of macaque monkeys during an eye movement task. The fitted model proves successful in capturing the fast temporal modulations in the response, reproducing the spike response temporal statistics, and accurately accounting for the neurons' dynamic spatiotemporal sensitivities, during eye movements.

CONCLUSION

The nonstationary GLM framework developed in this study can be used in cases where a time-varying behavioral or cognitive component makes GLM-based models insufficient to describe the dependencies of neural responses on the stimulus-related covariates.

SIGNIFICANCE

In addition to being quite powerful in encoding time-varying response modulations, this general framework also enables a readout of the neural code while dissociating the influence of other nonstimulus covariates. This framework will advance our ability to understand sensory processing in higher brain areas when modulated by several behavioral or cognitive variables.

Motor cortical activity changes during neuroprosthetic-controlled object interaction

Brain-computer interface (BCI) controlled prosthetic arms are being developed to restore function to people with upper-limb paralysis. This work provides an opportunity to analyze human cortical activity during complex tasks. Previously we observed that BCI control became more difficult during interactions with objects, although we did not quantify the neural origins of this phenomena. Here, we investigated how motor cortical activity changed in the presence of an object independently of the kinematics that were being generated using intracortical recordings from two people with tetraplegia. After identifying a population-wide increase in neural firing rates that corresponded with the hand being near an object, we developed an online scaling feature in the BCI system that operated without knowledge of the task. Online scaling increased the ability of two subjects to control the robotic arm when reaching to grasp and transport objects. This work suggests that neural representations of the environment, in this case the presence of an object, are strongly and consistently represented in motor cortex but can be accounted for to improve BCI performance.

Preferential encoding of movement amplitude and speed in the primary motor cortex and cerebellum

Voluntary movements require control of multiple kinematic parameters, a task carried out by a distributed brain architecture. However, it remains unclear whether regions along the motor system encode single, or rather a mixture of, kinematic parameters during action execution. Here, rapid event-related functional magnetic resonance imaging was used to differentiate brain activity along the motor system during the encoding of movement amplitude, duration, and speed. We present cumulative evidence supporting preferential encoding of kinematic parameters along the motor system, based on blood-oxygenation-level dependent signal recorded in a well-controlled single-joint wrist-flexion task. Whereas activity in the left primary motor cortex (M1) showed preferential encoding of movement amplitude, the anterior lobe of the right cerebellum (primarily lobule V) showed preferential encoding of movement speed. Conversely, activity in the left supplementary motor area (SMA), basal ganglia (putamen), and anterior intraparietal sulcus was not preferentially modulated by any specific parameter. We found no preference in peak activation for duration encoding in any of the tested regions. Electromyographic data was mainly modulated by movement amplitude, restricting the distinction between amplitude and muscle force encoding. Together, these results suggest that during single-joint movements, distinct kinematic parameters are controlled by largely distinct brain-regions that work together to produce and control precise movements. Hum Brain Mapp 38:5970-5986, 2017. © 2017 Wiley Periodicals, Inc.

Perspectives on classical controversies about the motor cortex

Primary motor cortex has been studied for more than a century, yet a consensus on its functional contribution to movement control is still out of reach. In particular, there remains controversy as to the level of control produced by motor cortex ("low-level" movement dynamics vs. "high-level" movement kinematics) and the role of sensory feedback. In this review, we present different perspectives on the two following questions: What does activity in motor cortex reflect? and How do planned motor commands interact with incoming sensory feedback during movement? The four authors each present their independent views on how they think the primary motor cortex (M1) controls movement. At the end, we present a dialogue in which the authors synthesize their views and suggest possibilities for moving the field forward. While there is not yet a consensus on the role of M1 or sensory feedback in the control of upper limb movements, such dialogues are essential to take us closer to one.

Characterization of neurons in the primate medial intraparietal area reveals a joint representation of intended reach direction and amplitude

To support accurate memory-guided reaching, the brain must represent both the direction and amplitude of reaches in a movement plan. Several cortical areas have been shown to represent the direction of a planned reaching movement, but the neuronal representation of reach amplitude is still unclear, especially in sensory-motor integration areas. To investigate this, we recorded from neurons in the medial intraparietal area (MIP) of monkeys performing a variable amplitude memory reach task. In one monkey, we additionally recorded from the dorsal premotor cortex (PMd) for direct cross-area comparisons. In both areas, we found modest but significant proportions of neurons with movement-planning activity sensitive to reach amplitude. However, reach amplitude was under-represented relative to direction in the neuronal population, with approximately one third as many selective neurons. We observed an interaction between neuronal selectivity for amplitude and direction; neurons in both areas exhibited significant modulation of neuronal activity by reach amplitude in some but not all directions. Consistent with an encoding of reach goals as a position in visual space, the response patterns of MIP/PMd neurons were best predicted by 2D Gaussian position encoding model, in contrast to a number of alternative direction and amplitude tuning models. Taken together, these results suggest that amplitude and direction jointly modulate activity in MIP, as in PMd, to form representations of intended reach position.

Spatiotemporal Distribution of Location and Object Effects in Primary Motor Cortex Neurons during Reach-to-Grasp

Reaching and grasping typically are considered to be spatially separate processes that proceed concurrently in the arm and the hand, respectively. The proximal representation in the primary motor cortex (M1) controls the arm for reaching, while the distal representation controls the hand for grasping. Many studies of M1 activity therefore have focused either on reaching to various locations without grasping different objects, or else on grasping different objects all at the same location. Here, we recorded M1 neurons in the anterior bank and lip of the central sulcus as monkeys performed more naturalistic movements, reaching toward, grasping, and manipulating four different objects in up to eight different locations. We quantified the extent to which variation in firing rates depended on location, on object, and on their interaction-all as a function of time. Activity proceeded largely in two sequential phases: the first related predominantly to the location to which the upper extremity reached, and the second related to the object about to be grasped. Both phases involved activity distributed widely throughout the sampled territory, spanning both the proximal and the distal upper extremity representation in caudal M1. Our findings indicate that naturalistic reaching and grasping, rather than being spatially segregated processes that proceed concurrently, each are spatially distributed processes controlled by caudal M1 in large part sequentially. Rather than neuromuscular processes separated in space but not time, reaching and grasping are separated more in time than in space.

SIGNIFICANCE STATEMENT

Reaching and grasping typically are viewed as processes that proceed concurrently in the arm and hand, respectively. The arm region in the primary motor cortex (M1) is assumed to control reaching, while the hand region controls grasping. During naturalistic reach-grasp-manipulate movements, we found, however, that neuron activity proceeds largely in two sequential phases, each spanning both arm and hand representations in M1. The first phase is related predominantly to the reach location, and the second is related to the object about to be grasped. Our findings indicate that reaching and grasping are successive aspects of a single movement. Initially the arm and the hand both are projected toward the object's location, and later both are shaped to grasp and manipulate.

Multiple Coordinate Systems and Motor Strategies for Reaching Movements When Eye and Hand Are Dissociated in Depth and Direction

Reaching behavior represents one of the basic aspects of human cognitive abilities important for the interaction with the environment. Reaching movements towards visual objects are controlled by mechanisms based on coordinate systems that transform the spatial information of target location into appropriate motor response. Although recent works have extensively studied the encoding of target position for reaching in three-dimensional space at behavioral level, the combined analysis of reach errors and movement variability has so far been investigated by few studies. Here we did so by testing 12 healthy participants in an experiment where reaching targets were presented at different depths and directions in foveal and peripheral viewing conditions. Each participant executed a memory-guided task in which he/she had to reach the memorized position of the target. A combination of vector and gradient analysis, novel for behavioral data, was applied to analyze patterns of reach errors for different combinations of eye/target positions. The results showed reach error patterns based on both eye- and space-centered coordinate systems: in depth more biased towards a space-centered representation and in direction mixed between space- and eye-centered representation. We calculated movement variability to describe different trajectory strategies adopted by participants while reaching to the different eye/target configurations tested. In direction, the distribution of variability between configurations that shared the same eye/target relative configuration was different, whereas in configurations that shared the same spatial position of targets, it was similar. In depth, the variability showed more similar distributions in both pairs of eye/target configurations tested. These results suggest that reaching movements executed in geometries that require hand and eye dissociations in direction and depth showed multiple coordinate systems and different trajectory strategies according to eye/target configurations and the two dimensions of space.

From the motor cortex to the movement and back again

The motor cortex controls motor behaviors by generating movement-specific signals and transmitting them through spinal cord circuits and motoneurons to the muscles. Precise and well-coordinated muscle activation patterns are necessary for accurate movement execution. Therefore, the activity of cortical neurons should correlate with movement parameters. To investigate the specifics of such correlations among activities of the motor cortex, spinal cord network and muscles, we developed a model for neural control of goal-directed reaching movements that simulates the entire pathway from the motor cortex through spinal cord circuits to the muscles controlling arm movements. In this model, the arm consists of two joints (shoulder and elbow), whose movements are actuated by six muscles (4 single-joint and 2 double-joint flexors and extensors). The muscles provide afferent feedback to the spinal cord circuits. Cortical neurons are defined as cortical "controllers" that solve an inverse problem based on a proposed straight-line trajectory to a target position and a predefined bell-shaped velocity profile. Thus, the controller generates a motor program that produces a task-specific activation of low-level spinal circuits that in turn induce the muscle activation realizing the intended reaching movement. Using the model, we describe the mechanisms of correlation between cortical and motoneuronal activities and movement direction and other movement parameters. We show that the directional modulation of neuronal activity in the motor cortex and the spinal cord may result from direction-specific dynamics of muscle lengths. Our model suggests that directional modulation first emerges at the level of muscle forces, augments at the motoneuron level, and further increases at the level of the motor cortex due to the dependence of frictional forces in the joints, contractility of the muscles and afferent feedback on muscle lengths and/or velocities.

Refinement of learned skilled movement representation in motor cortex deep output layer

The mechanisms underlying the emergence of learned motor skill representation in primary motor cortex (M1) are not well understood. Specifically, how motor representation in the deep output layer 5b (L5b) is shaped by motor learning remains virtually unknown. In rats undergoing motor skill training, we detect a subpopulation of task-recruited L5b neurons that not only become more movement-encoding, but their activities are also more structured and temporally aligned to motor execution with a timescale of refinement in tens-of-milliseconds. Field potentials evoked at L5b in vivo exhibit persistent long-term potentiation (LTP) that parallels motor performance. Intracortical dopamine denervation impairs motor learning, and disrupts the LTP profile as well as the emergent neurodynamical properties of task-recruited L5b neurons. Thus, dopamine-dependent recruitment of L5b neuronal ensembles via synaptic reorganization may allow the motor cortex to generate more temporally structured, movement-encoding output signal from M1 to downstream circuitry that drives increased uniformity and precision of movement during motor learning.

Role of human premotor dorsal region in learning a conditional visuomotor task

Conditional learning is an important component of our everyday activities (e.g., handling a phone or sorting work files) and requires identification of the arbitrary stimulus, accurate selection of the motor response, monitoring of the response, and storing in memory of the stimulus-response association for future recall. Learning this type of conditional visuomotor task appears to engage the premotor dorsal region (PMd). However, the extent to which PMd might be involved in specific or all processes of conditional learning is not well understood. Using transcranial magnetic stimulation (TMS), we demonstrate the role of human PMd in specific stages of learning of a novel conditional visuomotor task that required subjects to identify object center of mass using a color cue and to apply appropriate torque on the object at lift onset to minimize tilt. TMS over PMd, but not vertex, increased error in torque exerted on the object during the learning trials. Analyses of digit position and forces further revealed that the slowing in conditional visuomotor learning resulted from impaired monitoring of the object orientation during lift, rather than stimulus identification, thus compromising the ability to accurately reduce performance error across trials. Importantly, TMS over PMd did not alter production of torque based on the recall of learned color-torque associations. We conclude that the role of PMd for conditional learning is highly sensitive to the stage of learning visuomotor associations.

NEW & NOTEWORTHY

Conditional learning involves stimulus identification, motor response selection, response monitoring, memory encoding, and recall of the learned association. Premotor dorsal (PMd) has been implicated for conditional learning. However, the extent to which PMd might be involved in specific or all stages of conditional learning is not well understood. The novel finding of our study is that PMd appears to be involved with monitoring motor responses, a sensorimotor integration stage essential for conditional learning.

An Improved Unscented Kalman Filter Based Decoder for Cortical Brain-Machine Interfaces

Brain-machine interfaces (BMIs) seek to connect brains with machines or computers directly, for application in areas such as prosthesis control. For this application, the accuracy of the decoding of movement intentions is crucial. We aim to improve accuracy by designing a better encoding model of primary motor cortical activity during hand movements and combining this with decoder engineering refinements, resulting in a new unscented Kalman filter based decoder, UKF2, which improves upon our previous unscented Kalman filter decoder, UKF1. The new encoding model includes novel acceleration magnitude, position-velocity interaction, and target-cursor-distance features (the decoder does not require target position as input, it is decoded). We add a novel probabilistic velocity threshold to better determine the user's intent to move. We combine these improvements with several other refinements suggested by others in the field. Data from two Rhesus monkeys indicate that the UKF2 generates offline reconstructions of hand movements (mean CC 0.851) significantly more accurately than the UKF1 (0.833) and the popular position-velocity Kalman filter (0.812). The encoding model of the UKF2 could predict the instantaneous firing rate of neurons (mean CC 0.210), given kinematic variables and past spiking, better than the encoding models of these two decoders (UKF1: 0.138, p-v Kalman: 0.098). In closed-loop experiments where each monkey controlled a computer cursor with each decoder in turn, the UKF2 facilitated faster task completion (mean 1.56 s vs. 2.05 s) and higher Fitts's Law bit rate (mean 0.738 bit/s vs. 0.584 bit/s) than the UKF1. These results suggest that the modeling and decoder engineering refinements of the UKF2 improve decoding performance. We believe they can be used to enhance other decoders as well.

Tensor Analysis Reveals Distinct Population Structure that Parallels the Different Computational Roles of Areas M1 and V1

Cortical firing rates frequently display elaborate and heterogeneous temporal structure. One often wishes to compute quantitative summaries of such structure—a basic example is the frequency spectrum—and compare with model-based predictions. The advent of large-scale population recordings affords the opportunity to do so in new ways, with the hope of distinguishing between potential explanations for why responses vary with time. We introduce a method that assesses a basic but previously unexplored form of population-level structure: when data contain responses across multiple neurons, conditions, and times, they are naturally expressed as a third-order tensor. We examined tensor structure for multiple datasets from primary visual cortex (V1) and primary motor cortex (M1). All V1 datasets were ‘simplest’ (there were relatively few degrees of freedom) along the neuron mode, while all M1 datasets were simplest along the condition mode. These differences could not be inferred from surface-level response features. Formal considerations suggest why tensor structure might differ across modes. For idealized linear models, structure is simplest across the neuron mode when responses reflect external variables, and simplest across the condition mode when responses reflect population dynamics. This same pattern was present for existing models that seek to explain motor cortex responses. Critically, only dynamical models displayed tensor structure that agreed with the empirical M1 data. These results illustrate that tensor structure is a basic feature of the data. For M1 the tensor structure was compatible with only a subset of existing models.

Neuroscientists commonly measure the time-varying activity of neurons in the brain. Early studies explored how such activity directly encodes sensory stimuli. Since then neural responses have also been found to encode abstract parameters such as expected reward. Yet not all aspects of neural activity directly encode identifiable parameters: patterns of activity sometimes reflect the evolution of underlying internal computations, and may be only obliquely related to specific parameters. For example, it remains debated whether cortical activity during movement relates to parameters such as reach velocity, to parameters such as muscle activity, or to underlying computations that culminate in the production of muscle activity. To address this question we exploited an unexpected fact. When activity directly encodes a parameter it tends to be mathematically simple in a very particular way. When activity reflects the evolution of a computation being performed by the network, it tends to be mathematically simple in a different way. We found that responses in a visual area were simple in the first way, consistent with encoding of parameters. We found that responses in a motor area were simple in the second way, consistent with participation in the underlying computations that culminate in movement.

Reorganization between preparatory and movement population responses in motor cortex

Neural populations can change the computation they perform on very short timescales. Although such flexibility is common, the underlying computational strategies at the population level remain unknown. To address this gap, we examined population responses in motor cortex during reach preparation and movement. We found that there exist exclusive and orthogonal population-level subspaces dedicated to preparatory and movement computations. This orthogonality yielded a reorganization in response correlations: the set of neurons with shared response properties changed completely between preparation and movement. Thus, the same neural population acts, at different times, as two separate circuits with very different properties. This finding is not predicted by existing motor cortical models, which predict overlapping preparation-related and movement-related subspaces. Despite orthogonality, responses in the preparatory subspace were lawfully related to subsequent responses in the movement subspace. These results reveal a population-level strategy for performing separate but linked computations.

Single neuron responses are highly complex and dynamic yet they are able to flexibly represent behaviour through their collective activity. Here the authors demonstrate that population activity patterns of motor cortex neurons are orthogonal during successive task epochs that are linked through a simple linear function.

Neostriatal Neuronal Activity Correlates Better with Movement Kinematics under Certain Rewards

This study investigated how the activity of neostriatal neurons is related to the kinematics of movement when monkeys performed visually and vibratory cued wrist extensions and flexions. Single-unit recordings of 142/236 neostriatal neurons showed pre-movement activity (PMA) in a reaction time task with unpredictable reward. Monkeys were pseudo-randomly (75%) rewarded for correct performance. A regression model was used to determine whether the correlation between neostriatal neuronal activity and the kinematic variables (position, velocity, and acceleration) of wrist movement changes as a function of reward contingency, sensory cues, and movement direction. The coefficients of determination (CoD) representing the proportion of the variance in neuronal activity explained by the regression model on a trial by trial basis, together with their temporal occurrences (time of best regression/correlation, ToC) were compared across sensory modality, movement direction, and reward contingency. The best relationship (correlation) between neuronal activity and movement kinematic variables, given by the average coefficient of determination (CoD), was: (a) greater during trials in which rewards were certain, called "A" trials, as compared with those in which reward was uncertain called ("R") trials, (b) greater during flexion (Flex) trials as compared with extension (Ext) trials, and (c) greater during visual (VIS) cued trials than during vibratory (VIB) cued trials, for the same type of trial and the same movement direction. These results are consistent with the hypothesis that predictability of reward for correct performance is accompanied by faster linkage between neostriatal PMA and the vigor of wrist movement kinematics. Furthermore, the results provide valuable insights for building an upper-limb neuroprosthesis.

New Views of the Primary Motor Cortex

For 100 years, from the 1870s to the 1970s, somatotopic organization was considered the hallmark of the primary motor cortex (M1). M1 neurons were viewed as upper motor neurons, implying that their organization and function were upstream versions of the spinal motoneurons to which they project. Taken together, the notions of somatotopic organization and upper motor neurons established a view of M1 as a sheet of somatotopically arrayed neurons that controlled either the muscles or the movements of different body parts. Evidence accumulating since the 1970s, however, has generated new views of M1 at an accelerating pace. Here, I briefly review evidence leading to three new views of M1. First, whereas the gross somatotopic organization of M1—with the head represented laterally, the lower extremity medially, and the upper extremity in between—is unquestioned, we now view representation within the upper extremity region (from which the most evidence is available) as widely distributed. Second, rather than a fixed array of representation, we now view M1 as capable of considerable, and surprisingly rapid, reorganization. And third, rather than simply controlling the parameters of movement execution, we now view M1 as participating in aspects of sensorimotor transformation that include some representation of the sensory cues leading to voluntary movement. Although these new views account for a good deal of recent experimental evidence, they also open many new questions about the primary motor cortex.

Predominance of Movement Speed Over Direction in Neuronal Population Signals of Motor Cortex: Intracranial EEG Data and A Simple Explanatory Model

How neuronal activity of motor cortex is related to movement is a central topic in motor neuroscience. Motor-cortical single neurons are more closely related to hand movement velocity than speed, that is, the magnitude of the (directional) velocity vector. Recently, there is also increasing interest in the representation of movement parameters in neuronal population activity, such as reflected in the intracranial EEG (iEEG). We show that in iEEG, contrasting to what has been previously found on the single neuron level, speed predominates over velocity. The predominant speed representation was present in nearly all iEEG signal features, up to the 600–1000 Hz range. Using a model of motor-cortical signals arising from neuronal populations with realistic single neuron tuning properties, we show how this reversal can be understood as a consequence of increasing population size. Our findings demonstrate that the information profile in large population signals may systematically differ from the single neuron level, a principle that may be helpful in the interpretation of neuronal population signals in general, including, for example, EEG and functional magnetic resonance imaging. Taking advantage of the robust speed population signal may help in developing brain–machine interfaces exploiting population signals.

Motor imagery of hand actions: Decoding the content of motor imagery from brain activity in frontal and parietal motor areas

How motor maps are organized while imagining actions is an intensely debated issue. It is particularly unclear whether motor imagery relies on action‐specific representations in premotor and posterior parietal cortices. This study tackled this issue by attempting to decode the content of motor imagery from spatial patterns of Blood Oxygen Level Dependent (BOLD) signals recorded in the frontoparietal motor imagery network. During fMRI‐scanning, 20 right‐handed volunteers worked on three experimental conditions and one baseline condition. In the experimental conditions, they had to imagine three different types of right‐hand actions: an aiming movement, an extension–flexion movement, and a squeezing movement. The identity of imagined actions was decoded from the spatial patterns of BOLD signals they evoked in premotor and posterior parietal cortices using multivoxel pattern analysis. Results showed that the content of motor imagery (i.e., the action type) could be decoded significantly above chance level from the spatial patterns of BOLD signals in both frontal (PMC, M1) and parietal areas (SPL, IPL, IPS). An exploratory searchlight analysis revealed significant clusters motor‐ and motor‐associated cortices, as well as in visual cortices. Hence, the data provide evidence that patterns of activity within premotor and posterior parietal cortex vary systematically with the specific type of hand action being imagined. Hum Brain Mapp 37:81–93, 2016. © 2015 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.

Spatiotemporal distribution of location and object effects in reach-to-grasp kinematics

In reaching to grasp an object, the arm transports the hand to the intended location as the hand shapes to grasp the object. Prior studies that tracked arm endpoint and grip aperture have shown that reaching and grasping, while proceeding in parallel, are interdependent to some degree. Other studies of reaching and grasping that have examined the joint angles of all five digits as the hand shapes to grasp various objects have not tracked the joint angles of the arm as well. We, therefore, examined 22 joint angles from the shoulder to the five digits as monkeys reached, grasped, and manipulated in a task that dissociated location and object. We quantified the extent to which each angle varied depending on location, on object, and on their interaction, all as a function of time. Although joint angles varied depending on both location and object beginning early in the movement, an early phase of location effects in joint angles from the shoulder to the digits was followed by a later phase in which object effects predominated at all joint angles distal to the shoulder. Interaction effects were relatively small throughout the reach-to-grasp. Whereas reach trajectory was influenced substantially by the object, grasp shape was comparatively invariant to location. Our observations suggest that neural control of reach-to-grasp may occur largely in two sequential phases: the first determining the location to which the arm transports the hand, and the second shaping the entire upper extremity to grasp and manipulate the object.

Overlapping representations for reach depth and direction in caudal superior parietal lobule of macaques

Reaching movements in the real world have typically a direction and a depth component. Despite numerous behavioral studies, there is no consensus on whether reach coordinates are processed in separate or common visuomotor channels. Furthermore, the neural substrates of reach depth in parietal cortex have been ignored in most neurophysiological studies. In the medial posterior parietal area V6A, we recently demonstrated the strong presence of depth signals and the extensive convergence of depth and direction information on single neurons during all phases of a fixate-to-reach task in 3-dimensional (3D) space. Using the same task, in the present work we examined the processing of direction and depth information in area PEc of the caudal superior parietal lobule (SPL) in three Macaca fascicularis monkeys. Across the task, depth and direction had a similar, high incidence of modulatory effect. The effect of direction was stronger than depth during the initial fixation period. As the task progressed toward arm movement execution, depth tuning became more prominent than directional tuning and the number of cells modulated by both depth and direction increased significantly. Neurons tuned by depth showed a small bias for far peripersonal space. Cells with directional modulations were more frequently tuned toward contralateral spatial locations, but ipsilateral space was also represented. These findings, combined with results from neighboring areas V6A and PE, support a rostral-to-caudal gradient of overlapping representations for reach depth and direction in SPL. These findings also support a progressive change from visuospatial (vergence angle) to somatomotor representations of 3D space in SPL.

Advancing brain-machine interfaces: moving beyond linear state space models

Advances in recent years have dramatically improved output control by Brain-Machine Interfaces (BMIs). Such devices nevertheless remain robotic and limited in their movements compared to normal human motor performance. Most current BMIs rely on transforming recorded neural activity to a linear state space composed of a set number of fixed degrees of freedom. Here we consider a variety of ways in which BMI design might be advanced further by applying non-linear dynamics observed in normal motor behavior. We consider (i) the dynamic range and precision of natural movements, (ii) differences between cortical activity and actual body movement, (iii) kinematic and muscular synergies, and (iv) the implications of large neuronal populations. We advance the hypothesis that a given population of recorded neurons may transmit more useful information than can be captured by a single, linear model across all movement phases and contexts. We argue that incorporating these various non-linear characteristics will be an important next step in advancing BMIs to more closely match natural motor performance.

Direction and speed tuning of motor-cortex multi-unit activity and local field potentials during reaching movements

Primary motor-cortex multi-unit activity (MUA) and local-field potentials (LFPs) have both been suggested as potential control signals for brain-computer interfaces (BCIs) aimed at movement restoration. Some studies report that LFP-based decoding is comparable to spiking-based decoding, while others offer contradicting evidence. Differences in experimental paradigms, tuning models and decoding techniques make it hard to directly compare these results. Here, we use regression and mutual information analyses to study how MUA and LFP encode various kinematic parameters during reaching movements. We find that in addition to previously reported directional tuning, MUA also contains prominent speed tuning. LFP activity in low-frequency bands (15-40Hz, LFPL) is primarily speed tuned, and contains more speed information than both high-frequency LFP (100-300Hz, LFPH) and MUA. LFPH contains more directional information compared to LFPL, but less information when compared with MUA. Our results suggest that a velocity and speed encoding model is most appropriate for both MUA and LFPH, whereas a speed only encoding model is adequate for LFPL.

Single-unit activity, threshold crossings, and local field potentials in motor cortex differentially encode reach kinematics

A diversity of signals can be recorded with extracellular electrodes. It remains unclear whether different signal types convey similar or different information and whether they capture the same or different underlying neural phenomena. Some researchers focus on spiking activity, while others examine local field potentials, and still others posit that these are fundamentally the same signals. We examined the similarities and differences in the information contained in four signal types recorded simultaneously from multielectrode arrays implanted in primary motor cortex: well-isolated action potentials from putative single units, multiunit threshold crossings, and local field potentials (LFPs) at two distinct frequency bands. We quantified the tuning of these signal types to kinematic parameters of reaching movements. We found 1) threshold crossing activity is not a proxy for single-unit activity; 2) when examined on individual electrodes, threshold crossing activity more closely resembles LFP activity at frequencies between 100 and 300 Hz than it does single-unit activity; 3) when examined across multiple electrodes, threshold crossing activity and LFP integrate neural activity at different spatial scales; and 4) LFP power in the "beta band" (between 10 and 40 Hz) is a reliable indicator of movement onset but does not encode kinematic features on an instant-by-instant basis. These results show that the diverse signals recorded from extracellular electrodes provide somewhat distinct and complementary information. It may be that these signal types arise from biological phenomena that are partially distinct. These results also have practical implications for harnessing richer signals to improve brain-machine interface control.

The emergence of single neurons in clinical neurology

Single neuron actions and interactions are the sine qua non of brain function, and nearly all diseases and injuries of the CNS trace their clinical sequelae to neuronal dysfunction or failure. Remarkably, discussion of neuronal activity is largely absent in clinical neuroscience. Advances in neurotechnology and computational capabilities, accompanied by shifts in theoretical frameworks, have led to renewed interest in the information represented by single neurons. Using direct interfaces with the nervous system, millisecond-scale information will soon be extracted from single neurons in clinical environments, supporting personalized treatment of neurologic and psychiatric disease. In this Perspective, we focus on single-neuronal activity in restoring communication and motor control in patients suffering from devastating neurological injuries. We also explore the single neuron's role in epilepsy and movement disorders, surgical anesthesia, and in cognitive processes disrupted in neurodegenerative and neuropsychiatric disease. Finally, we speculate on how technological advances will revolutionize neurotherapeutics.

A small-world-based population encoding model of the primary visual cortex

A wide range of evidence has shown that information encoding performed by the visual cortex involves complex activities of neuronal populations. However, the effects of the neuronal connectivity structure on the population's encoding performance remain poorly understood. In this paper, a small-world-based population encoding model of the primary visual cortex (V1) is established on the basis of the generalized linear model (GLM) to describe the computation of the neuronal population. The model mainly consists of three sets of filters, including a spatiotemporal stimulus filter, a post-spike history filter, and a set of coupled filters with the coupling neurons organizing as a small-world network. The parameters of the model were fitted with neuronal data of the rat V1 recorded with a micro-electrode array. Compared to the traditional GLM, without considering the small-world structure of the neuronal population, the proposed model was proved to produce more accurate spiking response to grating stimuli and enhance the capability of the neuronal population to carry information. The comparison results proved the validity of the proposed model and further suggest the role of small-world structure in the encoding performance of local populations in V1, which provides new insights for understanding encoding mechanisms of a small scale population in visual system.

Dissociable contribution of the parietal and frontal cortex to coding movement direction and amplitude

To reach for an object, we must convert its spatial location into an appropriate motor command, merging movement direction and amplitude. In humans, it has been suggested that this visuo-motor transformation occurs in a dorsomedial parieto-frontal pathway, although the causal contribution of the areas constituting the “reaching circuit” remains unknown. Here we used transcranial magnetic stimulation (TMS) in healthy volunteers to disrupt the function of either the medial intraparietal area (mIPS) or dorsal premotor cortex (PMd), in each hemisphere. The task consisted in performing step-tracking movements with the right wrist towards targets located in different directions and eccentricities; targets were either visible for the whole trial (Target-ON) or flashed for 200 ms (Target-OFF). Left and right mIPS disruption led to errors in the initial direction of movements performed towards contralateral targets. These errors were corrected online in the Target-ON condition but when the target was flashed for 200 ms, mIPS TMS manifested as a larger endpoint spreading. In contrast, left PMd virtual lesions led to higher acceleration and velocity peaks—two parameters typically used to probe the planned movement amplitude—irrespective of the target position, hemifield and presentation condition; in the Target-OFF condition, left PMd TMS induced overshooting and increased the endpoint dispersion along the axis of the target direction. These results indicate that left PMd intervenes in coding amplitude during movement preparation. The critical TMS timings leading to errors in direction and amplitude were different, namely 160–100 ms before movement onset for mIPS and 100–40 ms for left PMd. TMS applied over right PMd had no significant effect. These results demonstrate that, during motor preparation, direction and amplitude of goal-directed movements are processed by different cortical areas, at distinct timings, and according to a specific hemispheric organization.

Dorsal premotor cortex: neural correlates of reach target decisions based on a color-location matching rule and conflicting sensory evidence

We recorded single-neuron activity in dorsal premotor (PMd) and primary motor cortex (M1) of two monkeys in a reach-target selection task. The monkeys chose between two color-coded potential targets by determining which target's color matched the predominant color of a multicolored checkerboard-like Decision Cue (DC). Different DCs contained differing numbers of colored squares matching each target. The DCs provided evidence about the correct target ranging from unambiguous (one color only) to very ambiguous and conflicting (nearly equal number of squares of each color). Differences in choice behavior (reach response times and success rates as a function of DC ambiguity) of the monkeys suggested that each applied a different strategy for using the target-choice evidence in the DCs. Nevertheless, the appearance of the DCs evoked a transient coactivation of PMd neurons preferring both potential targets in both monkeys. Reach response time depended both on how long it took activity to increase in neurons that preferred the chosen target and on how long it took to suppress the activity of neurons that preferred the rejected target, in both correct-choice and error-choice trials. These results indicate that PMd neurons in this task are not activated exclusively by a signal proportional to the net color bias of the DCs. They are instead initially modulated by the conflicting evidence supporting both response choices; final target selection may result from a competition between representations of the alternative choices. The results also indicate a temporal overlap between action selection and action initiation processes in PMd and M1.

Predicting object size from hand kinematics: a temporal perspective

Research on reach-to-grasp movements generally concentrates on kinematics values that are expression of maxima, in particular the maximum aperture of the hand and the peak of wrist velocity. These parameters provide a snapshot description of movement kinematics at a specific time point during reach, i.e., the maximum within a set of value, but do not allow to investigate how hand kinematics gradually conform to target properties. The present study was designed to extend the characterization of object size effects to the temporal domain. Thus, we computed the wrist velocity and the grip aperture throughout reach-to-grasp movements aimed at large versus small objects. To provide a deeper understanding of how joint movements varied over time, we also considered the time course of finger motion relative to hand motion. Results revealed that movement parameters evolved in parallel but at different rates in relation to object size. Furthermore, a classification analysis performed using a Support Vector Machine (SVM) approach showed that kinematic features taken as a group predicted the correct target size well before contact with the object. Interestingly, some kinematics features exhibited a higher ability to discriminate the target size than others did. These findings reinforce our knowledge about the relationship between kinematics and object properties and shed new light on the quantity and quality of information available in the kinematics of a reach-to-grasp movement over time. This might have important implications for our understanding of the action-perception coupling mechanism.