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1.  Into the Square and out of the Box: The effects of Quadrato Motor Training on Creativity and Alpha Coherence 
PLoS ONE  2013;8(1):e55023.
The objective of the present study was to investigate the body-cognitive relationship through behavioral and electrophysiological measures in an attempt to uncover the underlying mediating neuronal mechanism for movement-induced cognitive change. To this end we examined the effects of Quadrato Motor Training (QMT), a new whole-body training paradigm on cognitive performance, including creativity and reaction time tasks, and electrophysiological change, using a within-subject pre-post design. Creativity was studied by means of the Alternate Uses Task, measuring ideational fluency and ideational flexibility. Electrophysiological effects were measured in terms of alpha power and coherence. In order to determine whether training-induced changes were driven by the cognitive or the motor aspects of the training, we used two control groups: Verbal Training (VT, identical cognitive training with verbal response) and Simple Motor Training (SMT, similar motor training with reduced choice requirements). Twenty-seven participants were randomly assigned to one of the groups. Following QMT, we found enhanced inter-hemispheric and intra-hemispheric alpha coherence, and increased ideational flexibility, which was not the case for either the SMT or VT groups. These findings indicate that it is the combination of the motor and cognitive aspects embedded in the QMT which is important for increasing ideational flexibility and alpha coherence.
doi:10.1371/journal.pone.0055023
PMCID: PMC3559385  PMID: 23383043
2.  Correlations in state space can cause sub-optimal adaptation of optimal feedback control models 
Control of our movements is apparently facilitated by an adaptive internal model in the cerebellum. It was long thought that this internal model implemented an adaptive inverse model and generated motor commands, but recently many reject that idea in favor of a forward model hypothesis. In theory, the forward model predicts upcoming state during reaching movements so the motor cortex can generate appropriate motor commands. Recent computational models of this process rely on the optimal feedback control (OFC) framework of control theory. OFC is a powerful tool for describing motor control, it does not describe adaptation. Some assume that adaptation of the forward model alone could explain motor adaptation, but this is widely understood to be overly simplistic. However, an adaptive optimal controller is difficult to implement. A reasonable alternative is to allow forward model adaptation to ‘re-tune’ the controller. Our simulations show that, as expected, forward model adaptation alone does not produce optimal trajectories during reaching movements perturbed by force fields. However, they also show that re-optimizing the controller from the forward model can be sub-optimal. This is because, in a system with state correlations or redundancies, accurate prediction requires different information than optimal control. We find that adding noise to the movements that matches noise found in human data is enough to overcome this problem. However, since the state space for control of real movements is far more complex than in our simple simulations, the effects of correlations on re-adaptation of the controller from the forward model cannot be overlooked.
Electronic supplementary material
The online version of this article (doi:10.1007/s10827-011-0350-z) contains supplementary material, which is available to authorized users.
doi:10.1007/s10827-011-0350-z
PMCID: PMC3304072  PMID: 21792671
Optimal control; Motor adaptation; Forward model; Reaching movements
3.  Acquisition of internal models of motor tasks in children with autism 
Brain  2008;131(11):2894-2903.
Children with autism exhibit a host of motor disorders including poor coordination, poor tool use and delayed learning of complex motor skills like riding a tricycle. Theory suggests that one of the crucial steps in motor learning is the ability to form internal models: to predict the sensory consequences of motor commands and learn from errors to improve performance on the next attempt. The cerebellum appears to be an important site for acquisition of internal models, and indeed the development of the cerebellum is abnormal in autism. Here, we examined autistic children on a range of tasks that required a change in the motor output in response to a change in the environment. We first considered a prism adaptation task in which the visual map of the environment was shifted. The children were asked to throw balls to visual targets with and without the prism goggles. We next considered a reaching task that required moving the handle of a novel tool (a robotic arm). The tool either imposed forces on the hand or displaced the cursor associated with the handle position. In all tasks, the children with autism adapted their motor output by forming a predictive internal model, as exhibited through after-effects. Surprisingly, the rates of acquisition and washout were indistinguishable from normally developing children. Therefore, the mechanisms of acquisition and adaptation of internal models in self-generated movements appeared normal in autism. Sparing of adaptation suggests that alternative mechanisms contribute to impaired motor skill development in autism. Furthermore, the findings may have therapeutic implications, highlighting a reliable mechanism by which children with autism can most effectively alter their behaviour.
doi:10.1093/brain/awn226
PMCID: PMC2577807  PMID: 18819989
reach adaptation; prism adaptation; motor control; autism
4.  Motor adaptation as a process of reoptimization 
Adaptation is sometimes viewed as a process where the nervous system learns to predict and cancel effects of a novel environment, returning movements to near baseline (unperturbed) conditions. An alternate view is that cancellation is not the goal of adaptation. Rather, the goal is to maximize performance in that environment. If performance criteria are well defined, theory allows one to predict the re-optimized trajectory. For example, if velocity dependent forces perturb the hand perpendicular to the direction of a reaching movement, the best reach plan is not a straight line but a curved path that appears to over-compensate for the forces. If this environment is stochastic (changing from trial to trial), the re-optimized plan should take into account this uncertainty, removing the over-compensation. If the stochastic environment is zero-mean, peak velocities should increase to allow for more time to approach the target. Finally, if one is reaching through a via-point, the optimum plan in a zero-mean deterministic environment is a smooth movement, but in a zero-mean stochastic environment is a segmented movement. We observed all of these tendencies in how people adapt to novel environments. Therefore, motor control in a novel environment is not a process of perturbation cancellation. Rather, the process resembles re-optimization: through practice in the novel environment, we learn internal models that predict sensory consequences of motor commands. Through reward based optimization, we use the internal model to search for a better movement plan to minimize implicit motor costs and maximize rewards.
doi:10.1523/JNEUROSCI.5359-07.2008
PMCID: PMC2752329  PMID: 18337419
motor learning; motor adaptation; cerebellar damage; ataxia; optimal control; internal model
5.  Pausing Purkinje Cells in the Cerebellum of the Awake Cat 
A recent controversy has emerged concerning the existence of long pauses, presumably reflecting bistability of membrane potential, in the cerebellar Purkinje cells (PC) of awake animals. It is generally agreed that in the anesthetized animals and in vitro, these cells switch between two stable membrane potential states: a depolarized state (the ‘up-state’) characterized by continuous firing of simple spikes (SS) and a hyperpolarized state (the ‘down-state’) characterized by long pauses in the SS activity. To address the existence of long pauses in the neural activity of cerebellar PCs in the awake and behaving animal we used extracellular recordings in cats and find that approximately half of the recorded PCs exhibit such long pauses in the SS activity and transition between activity – periods with uninterrupted SS lasting an average of 1300 ms – and pauses up to several seconds. We called these cells pausing Purkinje cells (PPC) and they can easily be distinguished from continuous firing Purkinje cells. In most PPCs, state transitions in both directions were often associated (25% of state transitions) with complex spikes (CSs). This is consistent with intracellular findings of CS-driven state transitions. In sum, we present proof for the existence of long pauses in the PC SS activity that probably reflect underlying bistability, provide the first in-depth analysis of these pauses and show for the first time that transitions in and out of these pauses are related to CS firing in the awake and behaving animal.
doi:10.3389/neuro.06.002.2009
PMCID: PMC2671936  PMID: 19390639
Purkinje cell; cerebellum; pauses; bistability; simple spike; complex spike
6.  Forward Models and State Estimation in Compensatory Eye Movements 
The compensatory eye movement (CEM) system maintains a stable retinal image, integrating information from different sensory modalities to compensate for head movements. Inspired by recent models of the physiology of limb movements, we suggest that CEM can be modeled as a control system with three essential building blocks: a forward model that predicts the effects of motor commands; a state estimator that integrates sensory feedback into this prediction; and, a feedback controller that translates a state estimate into motor commands. We propose a specific mapping of nuclei within the CEM system onto these control functions. Specifically, we suggest that the Flocculus is responsible for generating the forward model prediction and that the Vestibular Nuclei integrate sensory feedback to generate an estimate of current state. Finally, the brainstem motor nuclei – in the case of horizontal compensation this means the Abducens Nucleus and the Nucleus Prepositus Hypoglossi – implement a feedback controller, translating state into motor commands. While these efforts to understand the physiological control system as a feedback control system are in their infancy, there is the intriguing possibility that CEM and targeted voluntary movements use the same cerebellar circuitry in fundamentally different ways.
doi:10.3389/neuro.03.013.2009
PMCID: PMC2786296  PMID: 19956563
cerebellum; model; control systems; vor; okr; vestibular nucleus; eye movements; forward model
7.  Acquisition of internal models of motor tasks in children with autism 
Brain : a journal of neurology  2008;131(Pt 11):2894-2903.
Children with autism exhibit a host of motor disorders including poor coordination, poor tool use, and delayed learning of complex motor skills like riding a tricycle. Theory suggests that one of the crucial steps in motor learning is the ability to form internal models: to predict the sensory consequences of motor commands and learn from errors to improve performance on the next attempt. The cerebellum appears to be an important site for acquisition of internal models, and indeed the development of the cerebellum is abnormal in autism. Here, we examined autistic children on a range of tasks that required a change in the motor output in response to a change in the environment. We first considered a prism adaptation task in which the visual map of the environment was shifted. The children were asked to throw balls to visual targets with and without the prism goggles. We next considered a reaching task that required moving the handle of a novel tool (a robotic arm). The tool either imposed forces on the hand or displaced the cursor associated with the handle position. In all tasks, the children with autism adapted their motor output by forming a predictive internal model, as exhibited through after-effects. Surprisingly, the rates of acquisition and washout were indistinguishable from normally developing children. Therefore, the mechanisms of acquisition and adaptation of internal models in self-generated movements appeared normal in autism. Sparing of adaptation suggests that alternative mechanisms contribute to impaired motor skill development in autism. Furthermore, the findings may have therapeutic implications, highlighting a reliable mechanism by which children with autism can most effectively alter their behavior.
doi:10.1093/brain/awn226
PMCID: PMC2577807  PMID: 18819989
reach adaptation; prism adaptation; motor control; autism
8.  A Gain-Field Encoding of Limb Position and Velocity in the Internal Model of Arm Dynamics 
PLoS Biology  2003;1(2):e25.
Adaptability of reaching movements depends on a computation in the brain that transforms sensory cues, such as those that indicate the position and velocity of the arm, into motor commands. Theoretical consideration shows that the encoding properties of neural elements implementing this transformation dictate how errors should generalize from one limb position and velocity to another. To estimate how sensory cues are encoded by these neural elements, we designed experiments that quantified spatial generalization in environments where forces depended on both position and velocity of the limb. The patterns of error generalization suggest that the neural elements that compute the transformation encode limb position and velocity in intrinsic coordinates via a gain-field; i.e., the elements have directionally dependent tuning that is modulated monotonically with limb position. The gain-field encoding makes the counterintuitive prediction of hypergeneralization: there should be growing extrapolation beyond the trained workspace. Furthermore, nonmonotonic force patterns should be more difficult to learn than monotonic ones. We confirmed these predictions experimentally.
A computational model offers a unifying explanation of seemingly disparate findings from human reaching experiments
doi:10.1371/journal.pbio.0000025
PMCID: PMC261873  PMID: 14624237

Results 1-8 (8)