Spatiotemporal neural interactions underlying continuous drawing movements as revealed by magnetoencephalography.
ABSTRACT Continuous and sequential movements are controlled by widely distributed brain regions. A series of studies have contributed to understanding the functional role of these regions in a variety of visuomotor tasks. However, little is known about the neural interactions underpinning continuous movements. In the current study, we examine the spatiotemporal neural interactions underlying continuous drawing movements and the association of them with behavioral components. We conducted an experiment in which subjects copied a pentagon continuously for ~45 s using an XY joystick, while neuromagnetic fluxes were recorded from their head using a 248-sensor whole-head magnetoencephalography (MEG) device. Each sensor time series was rendered stationary and non-autocorrelated by applying an autoregressive integrated moving average model and taking the residuals. We used the directional variability of the movement as a behavioral measure of the controls generated. The main objective of this study was to assess the relation between neural interactions and the variability of movement direction. That is, we divided the continuous recordings into consecutive periods (i.e., time-bins) of 51 steps duration and computed the pairwise cross-correlations between the prewhitened time series in each time-bin. The circular standard deviation of the movement direction within each time-bin provides an estimate of the directional variability of the 51-ms trajectory segment. We looked at the association between neural interactions and variability of movement direction, separately for each pair of sensors, by running a cross-correlation analysis between the strength of the MEG pairwise cross-correlations and the circular standard deviations. We identified two types of neuronal networks: in one, the neural interactions are correlated with the directional variability of the movement at negative time-lags (feedforward), and in the other, the neural interactions are correlated with the directional variability of the movement at positive time-lags (feedback). Sensors associated mostly with feedforward processes are distributed in the left hemisphere and the right occipital-temporal junction, whereas sensors related to feedback processes are distributed in the right hemisphere and the left cerebellar hemisphere. These results are in line with findings from a series of previous studies showing that specific brain regions are involved in feedforward and feedback control processes to plan, perform, and correct movements. Additionally, we looked at whether changes in movement direction modulate the neural interactions. Interestingly, we found a preponderance of sensors associated with changes in movement direction over the right hemisphere-ipsilateral to the moving hand. These sensors exhibit stronger coupling with the rest of the sensors for trajectory segments with high rather than low directional movement variability. We interpret these results as evidence that ipsilateral cortical regions are recruited for continuous movements when the curvature of the trajectory increases. To the best of our knowledge, this is the first study that shows how neural interactions are associated with a behavioral control parameter in continuous and sequential movements.
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ABSTRACT: The motor cortical substrate associated with reaching was studied as monkeys moved their hands from a central position to one of eight targets spaced around a circle. Single-cell activity patterns were recorded in the proximal arm area of motor cortex during the task. In addition to the well-studied average directional selectivity ("preferred direction") of single-cell activity, we also found the time-varying speed of movement to be represented in the cortical activity. A single equation relating motor cortical discharge rate to these two parameters was developed. This equation, which has both independent (speed only) and interactive (speed and direction) components, described a large portion of the time-varying motor cortical activity during the task. Electromyographic activity from a number of upper arm muscles was recorded during this task. Muscle activity was also found to be directionally tuned; however, the distributions of preferred directions were found to be significantly different from cortical activity. In addition, the effect of speed on cortical and muscle activity was also found to be significantly different.Journal of Neurophysiology 12/1999; 82(5):2676-92. · 3.04 Impact Factor
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ABSTRACT: Monkeys traced spirals on a planar surface as unitary activity was recorded from either premotor or primary motor cortex. Using the population vector algorithm, the hand's trajectory could be accurately visualized with the cortical activity throughout the task. The time interval between this prediction and the corresponding movement varied linearly with the instantaneous radius of curvature; the prediction interval was longer when the path of the finger was more curved (smaller radius). The intervals in the premotor cortex fell into two groups, whereas those in the primary motor cortex formed a single group. This suggests that the change in prediction interval is a property of a single population in primary motor cortex, with the possibility that this outcome is due to the different properties generated by the simultaneous action of separate subpopulations in premotor cortex. Electromyographic (EMG) activity and joint kinematics were also measured in this task. These parameters varied harmonically throughout the task with many of the same characteristics as those of single cortical cells. Neither the lags between joint-angular velocities and hand velocity nor the lags between EMG and hand velocity could explain the changes in prediction interval between cortical activity and hand velocity. The simple spatial and temporal relationship between cortical activity and finger trajectory suggests that the figural aspects of this task are major components of cortical activity.Journal of Neurophysiology 12/1999; 82(5):2693-704. · 3.04 Impact Factor
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ABSTRACT: The study of patients to infer normal brain function has a long tradition in neurology and psychology. More recently, the motor system has been subject to quantitative and computational characterization. The purpose of this review is to argue that the lesion approach and theoretical motor control can mutually inform each other. Specifically, one may identify distinct motor control processes from computational models and map them onto specific deficits in patients. Here we review some of the impairments in motor control, motor learning and higher-order motor control in patients with lesions of the corticospinal tract, the cerebellum, parietal cortex, the basal ganglia, and the medial temporal lobe. We attempt to explain some of these impairments in terms of computational ideas such as state estimation, optimization, prediction, cost, and reward. We suggest that a function of the cerebellum is system identification: to build internal models that predict sensory outcome of motor commands and correct motor commands through internal feedback. A function of the parietal cortex is state estimation: to integrate the predicted proprioceptive and visual outcomes with sensory feedback to form a belief about how the commands affected the states of the body and the environment. A function of basal ganglia is related to optimal control: learning costs and rewards associated with sensory states and estimating the "cost-to-go" during execution of a motor task. Finally, functions of the primary and the premotor cortices are related to implementing the optimal control policy by transforming beliefs about proprioceptive and visual states, respectively, into motor commands.Experimental Brain Research 04/2008; 185(3):359-81. DOI:10.1007/s00221-008-1280-5 · 2.17 Impact Factor