Spatiotemporal neural interactions underlying continuous drawing movements as revealed by magnetoencephalography
Brain Sciences Center (11B), Veterans Affairs Medical Center, VAHCS, One Veterans Drive, Minneapolis, MN, 55417, USA. Experimental Brain Research
(Impact Factor: 2.04).
08/2012; 222(1-2):159-71. DOI: 10.1007/s00221-012-3208-3
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: Author Summary
Choosing between alternative options requires assigning and integrating values along a multitude of dimensions. For instance, when buying a car, different cars may vary for their price, quality, fuel economy and more. Solving this problem requires finding a common currency to allow integration of disparate value dimensions. In dynamic decisions, in which the environment changes continuously, this multi-dimensional integration must be updated over time. Despite many years of research, it is still unclear how the brain integrates value information and makes decisions in the presence of competing alternatives. In the current study, we propose a probabilistic theory that allows dynamically integrating value information into a common currency. It builds on successful models in motor control and decision-making. It is comprised of a series of control schemes with each of them attached to an individual goal, generating an optimal action-plan to achieve that goal starting from the current state. The key novelty is the relative desirability computation that integrates good- and action- values to a single dynamic variable that weighs the individual action-plans as a function of state and time. By dynamically integrating value information, our theory models many key results in movement decisions that have previously eluded a common explanation.
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