A revised computational neuroanatomy for motor control

We discuss a new framework for understanding the structure of motor control. Our approach integrates existing models of motor control with the reality of hierarchical cortical processing and the parallel segregated loops that characterize cortical-subcortical connections. We also incorporate the recent claim that cortex functions via predictive representation and optimal information utilization. Our framework assumes each cortical area engaged in motor control generates a predictive model of a different aspect of motor behavior. In maintaining these predictive models, each area interacts with a different part of the cerebellum and basal ganglia. These subcortical areas are thus engaged in domain appropriate system identification and optimization. This refocuses the question of division of function among different cortical areas. What are the different aspects of motor behavior that are predictively modelled? We suggest that one fundamental division is between modelling of task and body while another is the model of state and action. Thus, we propose that the posterior parietal cortex, somatosensory cortex, premotor cortex, and motor cortex represent task state, body state, task action, and body action, respectively. In the second part of this review, we demonstrate how this division of labor can better account for many recent findings of movement encoding, especially in the premotor and posterior parietal cortices

Individual movement variability magnitudes are predicted by cortical neural variability

Humans exhibit considerable motor variability even across trivial reaching movements. This variability can be separated into specific kinematic components such as extent and direction that are thought to be governed by distinct neural processes. Here, we report that individual subjects (males and females) exhibit different magnitudes of kinematic variability, which are consistent (within individual) across movements to different targets and regardless of which arm (right or left) was used to perform the movements. Simultaneous fMRI recordings revealed that the same subjects also exhibited different magnitudes of fMRI variability across movements in a variety of motor system areas. These fMRI variability magnitudes were also consistent across movements to different targets when performed with either arm. Cortical fMRI variability in the posterior–parietal cortex of individual subjects explained their movement–extent variability. This relationship was apparent only in posterior-parietal cortex and not in other motor system areas, thereby suggesting that individuals with more variable movement preparation exhibit larger kinematic variability. We therefore propose that neural and kinematic variability are reliable and interrelated individual characteristics that may predispose individual subjects to exhibit distinct motor capabilities

Impact of transcranial direct current stimulation (tDCS) on neuronal functions

Transcranial direct current stimulation (tDCS), a non-invasive brain stimulation technique, modulates neuronal excitability by the application of a small electrical current. The low cost and ease of the technique has driven interest in potential clinical applications. However, outcomes are highly sensitive to stimulation parameters, leading to difficulty maximizing the technique's effectiveness. Although reversing the polarity of stimulation often causes opposite effects, this is not always the case. Effective clinical application will require an understanding of how tDCS works; how it modulates a neuron; how it affects the local network; and how it alters inter-network signaling. We have summarized what is known regarding the mechanisms of tDCS from sub-cellular processing to circuit level communication with a particular focus on what can be learned from the polarity specificity of the effects

Individual movement variability magnitudes are explained by cortical neural variability

Humans exhibit considerable motor variability even across trivial reaching movements. This variability can be separated into specific kinematic components such as extent and direction that are thought to be governed by distinct neural processes. Here, we report that individual subjects (males and females) exhibit different magnitudes of kinematic variability, which are consistent (within individual) across movements to different targets and regardless of which arm (right or left) was used to perform the movements. Simultaneous fMRI recordings revealed that the same subjects also exhibited different magnitudes of fMRI variability across movements in a variety of motor system areas. These fMRI variability magnitudes were also consistent across movements to different targets when performed with either arm. Cortical fMRI variability in the posterior–parietal cortex of individual subjects explained their movement–extent variability. This relationship was apparent only in posterior-parietal cortex and not in other motor system areas, thereby suggesting that individuals with more variable movement preparation exhibit larger kinematic variability. We therefore propose that neural and kinematic variability are reliable and interrelated individual characteristics that may predispose individual subjects to exhibit distinct motor capabilities

A replica free evaluation of the neuronal population information with mixed continuous and discrete stimuli: from the linear to the asymptotic regime

Recent studies have explored theoretically the ability of populations of neurons to carry information about a set of stimuli, both in the case of purely discrete or purely continuous stimuli, and in the case of multidimensional continuous angular and discrete correlates, in presence of additional quenched disorder in the distribution. An analytical expression for the mutual information has been obtained in the limit of large noise by means of the replica trick. Here we show that the same results can actually be obtained in most cases without the use of replicas, by means of a much simpler expansion of the logarithm. Fitting the theoretical model to real neuronal data, we show that the introduction of correlations in the quenched disorder improves the fit, suggesting a possible role of signal correlations-actually detected in real data- in a redundant code. We show that even in the more difficult analysis of the asymptotic regime, an explicit expression for the mutual information can be obtained without resorting to the replica trick despite the presence of quenched disorder, both with a gaussian and with a more realistic thresholded-gaussian model. When the stimuli are mixed continuous and discrete, we find that with both models the information seem to grow logarithmically to infinity with the number of neurons and with the inverse of the noise, even though the exact general dependence cannot be derived explicitly for the thresholded gaussian model. In the large noise limit lower values of information were obtained with the thresholded-gaussian model, for a fixed value of the noise and of the population size. On the contrary, in the asymptotic regime, with very low values of the noise, a lower information value is obtained with the gaussian model.Comment: 34 pages, 5 figure

Cerebellar tDCS does not enhance performance in an implicit categorization learning task

Background: Transcranial Direct Current Stimulation (tDCS) is a form of non-invasive electrical stimulation that changes neuronal excitability in a polarity and site-specific manner. In cognitive tasks related to prefrontal and cerebellar learning, cortical tDCS arguably facilitates learning, but the few studies investigating cerebellar tDCS, however, are inconsistent. Objective: We investigate the effect of cerebellar tDCS on performance of an implicit categorization learning task. Methods: Forty participants performed a computerized version of an implicit categorization learning task where squares had to be sorted into two categories, according to an unknown but fixed rule that integrated both the size and luminance of the square. Participants did one round of categorization to familiarize themselves with the task and to provide a baseline of performance. After that, 20 participants received anodal tDCS (20 min, 1.5 mA) over the right cerebellum, and 19 participants received sham stimulation and simultaneously started a second session of the categorization task using a new rule. Results: As expected, subjects performed better in the second session than in the first, baseline session, showing increased accuracy scores and reduced reaction times. Over trials, participants learned the categorization rule, improving their accuracy and reaction times. However, we observed no effect of anodal tDCS stimulation on overall performance or on learning, compared to sham stimulation. Conclusion: These results suggest that cerebellar tDCS does not modulate performance and learning on an implicit categorization task

Superposition violations in the compensatory eye movement system

PURPOSE. Compensatory eye movements (CEM) maintain a stable image on the retina by minimizing retinal slip. The optokinetic reflex (OKR) and vestibulo-ocular reflex (VOR) compensate for low and high velocity stimuli, respectively. The OKR system is known to be highly nonlinear. The VOR is generally modeled as a linear system and assumed to satisfy the superposition and homogeneity principles. To probe CEM violation of the superposition principle, we recorded eye movement responses in C57BL/6 mice to sum of sine (SoS) stimulation, a combination of multiple nonharmonic inputs. METHODS. We tested the VOR, OKR, WOR (visually enhanced VOR), and SVOR (suppressed VOR). We used stimuli containing 0.6 Hz, 0.8 Hz, 1.0 Hz, and 1.9 Hz. Power spectra of SoS stimuli did not yield distortion products. Gains and delays of SoS and single sine (SS) responses were compared to yield relative gains and delays. RESULTS. We find the superposition principle is violated primarily in the OKR, VOR, and SVOR conditions. In OKR, we observed relative gain suppression of the lower SoS stimulus frequency component irrespective of the absolute frequency. Conversely, SVOR and VOR results showed gain enhancement of the lower frequency component and overall decrease in lead. Visually enhanced VOR results showed trends for overall gain suppression and delay decrease. CONCLUSIONS. Compensatory eye movements arguably depend on predictive signals. These results may reflect better prediction for SS stimuli. Natural CEM system stimulation generally involves complex frequency spectra. Use of SoS stimuli is a step toward unravelling the signals that really drive CEM and the predictive algorithms they depend on

Effector-invariant movement encoding in the human motor system

Ipsilateral motor areas of cerebral cortex are active during arm movements and even reliably predict movement direction. Is coding similar during ipsilateral and contralateral movements? If so, is it in extrinsic (world-centered) or intrinsic (joint-configuration) coordinates? We addressed these questions by examining the similarity of multivoxel fMRI patterns in visuomotor cortical regions during unilateral reaching movements with both arms. The results of three complementary analyses revealed that fMRI response patterns were similar across right and left arm movements to identical targets (extrinsic coordinates) in visual cortices, and across movements with equivalent joint-angles (intrinsic coordinates) in motor cortices. We interpret this as evidence for the existence of distributed neural populations in multiple motor system areas that encode ipsilateral and contralateral movements in a similar manner: according to their intrinsic/joint coordinates

Cerebellar tDCS does not improve performance in probabilistic classification learning

In this study, the role of the cerebellum in a cognitive learning task using transcranial direct current stimulation (tDCS) was investigated. Using a weather prediction task, subjects had to learn the probabilistic associations between a stimulus (a combination of cards) and an outcome (sun or rain). This task is a variant of a probabilistic classification learning task, for which it has been reported that prefrontal tDCS enhances performance. Using a between-subject design, all 30 subjects learned to improve their performance with increasing accuracies and shortened response times over a series of 500 trials. Subjects also became more confident in their prediction during the experiment. However, no differences in performance and learning were observed between subjects receiving sham stimulation (n = 10) or anodal stimulation (2 mA for 20 min) over either the right cerebellum (n = 10) or the left prefrontal cortex (n = 10). This suggests that stimulating the brain with cerebellar tDCS does not readily influence probabilistic classification performances, probably due to the rather complex nature of this cognitive task

Contralateral manual compensation for velocity-dependent force perturbations

It is not yet clear how the temporal structure of a voluntary action is coded allowing coordinated bimanual responses. This study focuses on the adaptation to and compensation for a force profile presented to one stationary arm which is proportional to the velocity of the other moving arm. We hypothesised that subjects would exhibit predictive coordinative responses which would co-vary with the state of the moving arm. Our null hypothesis is that they develop a time-dependent template of forces appropriate to compensate for the imposed perturbation. Subjects were trained to make 500 ms duration reaching movements with their dominant right arm to a visual target. A force generated with a robotic arm that was proportional to the velocity of the moving arm and perpendicular to movement direction acted on their stationary left hand, either at the same time as the movement or delayed by 250 or 500 ms. Subjects rapidly learnt to minimise the final end-point error. In the delay conditions, the left hand moved in advance of the onset of the perturbing force. In test conditions with faster or slower movement of the right hand, the predictive actions of the left hand co-varied with movement speed. Compensation for movement-related forces appeared to be predictive but not based on an accurate force profile that was equal and opposite to the imposed perturbatio