計算論的感覚運動制御とリハビリテーションの統合 Integration of computational sensorimotor control and rehabilitation
S2-3-2-1 From Motor Learning to Motor Recovery ○John W. Krakauer1 Dept. of Neurology and Neuroscience, Johns Hopkins University1 Although experimental and computational studies of motor control and motor learning in humans have long been pursued, little translation has been achieved from those controlled studies to clinical rehabilitation therapies. This talk will focus on a few of our recent studies including the prediction of motor recovery after stroke and the mechanisms of spontaneous motor recovery after stroke in humans and in mouse models. I will extend these studies to a proposal of new neuro-rehabilitation approaches for patients in the first 3 months after stroke.	S2-3-2-2 Is dystonia a disorder of stochastic feedback control? ○Terence D. Sanger1 Dept. of Biomedical Engineering, University of Southern California1 Children and adults with dystonia have severely impaired movement despite normal strength and muscle function. Therefore dystonia appears to be a disorder of control systems. But the particular abnormality does not correspond to the type of instability seen in classical control, which would be predicted to lead to oscillation or exponential increase in state variables. Rather, in dystonia there is velocity-dependent variability, co-contraction, and reduction in movement speed. I propose that in order to understand dystonia, we need a model for normal and abnormal biological feedback control that considerably extends current computational models. I propose that a new theory of feedback control using stochastic operators permits modeling of important aspects of normal behavior, and that failure of stochastic operator control permits modeling of many aspects of dystonic movement. This control model suggests that the fundamental signals used for feedback are not estimates of state, but estimates of the probability distribution of state. The fundamental signals used for control are not desired reference states, but cost functions that assign a value to every potential state. In this model, dystonic movement arises in part from increased state uncertainty and increased motor variability. Compensation for uncertainty and variability leads naturally to reduced speed of movement and increased stiffness. Therefore many of the features of dystonia arise directly from failure modes of stochastic operator control that are not present in classical control, and we can consider dystonia to be a disorder of stochastic feedback control.
S2-3-2-3 ブレイン・コンピュータ・インターフェースによる脳機能可塑性の誘導　ー脳卒中後の運動回復を目指してー Promoting brain plasticity by brain-computer interface for stroke motor recovery ○牛場潤一1, 小野峻史2, 向野雅彦3 ○Junichi Ushiba1, Takashi Ono2, Masahiko Mukaino3 慶應義塾大学理工学部　生命情報学科1, 慶應義塾大学大学院理工学研究科2, 旭川医科大学リハビリテーション科3 Faculty of Science and Technology, Keio University1, School of Science and Technology, Graduate School of Keio University, Kanagawa, Japan2, Department of Rehabilitation, Asahikawa Medical Hospital, Hokkaido, Japan3 Brain-Computer Interface (BCI) can bypass motor output neural pathways by directly translating motor-related brain signals into commands for control of neuromuscular electrical stimulation. Since extrinsic feedback is expected to promote motor learning, approaches using BCI might facilitate neural plasticity and restore lost function after stroke. Here the present study using a single case ABAB design tested the importance of closed-loop system in BCI. A participant with hemiplegia due to subcortical stroke was recruited. In Period A for open-loop condition, the participant was asked to practice finger opening repeatedly. Neuromuscular electrical stimulation was simultaneously applied to the paretic finger extensor. In Period B for actual BCI condition, the sensorimotor rhythm (SMR) in electroencephalogram was recorded over the affected sensorimotor cortex. Paretic finger extensor was electrically stimulated only if sustained decrease of SMR was observed. In both periods, one-hour daily training was given for two weeks. Event-related desycnrhonization of SMR by motor intention became larger from 12% to 40% throughout the experiment, and its increment was larger in Period B. BOLD MRI initially identified activations in bilateral sensorimotor and supplementary motor cortices. The ratio of the signal intensity in the affected hemisphere to the intact side was increased in Period B, and decreased in Period A. Voluntary EMG in the paretic finger muscle was appeared in Period B. These results indicate that closed-loop system of BCI facilitates functional reorganization, at least in part, in the affected corticospinal tract. This implies that (1) SMR could be a real-time marker of corticospinal excitability, (2) extrinsic feedback of the corticospinal excitability may facilitate motor relearning, and also (3) neuromuscular electrical stimulation following the increase of corticospinal excitability may form Hebbian-like manner to enhance further excitability.	S2-3-2-4 文脈に依存した運動記憶の形成と想起　ー臨床応用を見据えてー Context dependent formation and retrieval of motor memory: A clinical application perspective ○野崎大地1 ○Daichi Nozaki1 東京大学大学院教育学研究科1 Graduate School of Education, The University of Tokyo1 Human learns to control movements or manipulate various tools under a wide variety of environments. However, it still remains unclear if and how distinct motor memories can be formed and retrieved in the brain depending on different contexts. In my presentation, I will talk about several examples that humans can adapt an identical movement to a limb to different dynamical or visuomotor environments by switching among the motor memories. Specifically, distinct motor memories can be formed and retrieved depending on whether the movements are rhythmic or discrete (Ikegami et al., JNS 2010), whether the opposite arm is moving or stationary (Nozaki et al., Nat Neurosci 2006), the kinematics of the opposite arm (Yokoi et al., JNS 2011), or the movement planned in a visual space (Hirashima & Nozaki, Curr Biol 2012). Then, I will show that the formation and retrieval of distinct motor memories can be partly explained by distinct neural representations of the movements. Finally, I will try to discuss how this knowledge can be applied as a rehabilitation technique.
S2-3-2-5 リハビリテーションによる回復プロセスの行動シミュレーションと計算モデル Behavioral simulation and computational model of stroke rehabilitation and recovery process ○大須理英子1 ○Rieko Osu1 ATR脳情報通信総合研究所1 ATR Brain Information Communication Research Laboratory Group1 An ideal of arm rehabilitation post-stroke is to recover function and increase the use of the paretic arm. To begin with, how do humans choose one arm of the other to reach single targets in front of the body? Our experimental results showed that the right arm was used most for targets in the first and third quadrants, for which the arm's inertia at the hand is the smallest. Furthermore, the greater accuracy of the dominant hand induces a bias in overall choice, such that for movements where the two arm's inertia is equal, the right arm is preferred. That is, when making the decision to use one arm or another to reach targets, subjects take into account both the mechanical property of the arm and the expected end-point accuracy of the movement. Stroke patients, because choosing paretic arm is more costly than healthy arm, often stop using paretic arm even though functionally available. Increased use of paretic arm is essential for recovery. However, there is little understanding of the interactions between arm function and use in humans post-stroke. Clinical longitudinal data is expensive and difficult to collect and the number of measurements per participant highly limited. Therefore, we set up an experimental paradigm in which we applied a non-isotropic visuo-motor rotation to one hand to simulate uni-lateral impairment, hand choice, and motor training after stroke. With healthy subjects, we were able to simulate several characteristics of hand use after stroke, and notably study the time course of the interactions between training and arm use. We observed rapid introduction of learned non-use after virtual impairment in healthy subjects. Arm non-use, defined as the difference between what the he can do when constrained to use the paretic arm and what the he does do when given a free choice to use either arm, has not yet been quantified in humans post-stroke. Therefore, we developed the Bilateral Arm Reaching Test to quantify arm use and non-use post-stroke.	S2-3-2-6 機能回復に向けた運動野における運動表現の理解 Understanding movement representations in the motor cortex for functional recovery ○田中宏和1 ○Hirokazu Tanaka1, Terrence J Sejnowski2 北陸先端科学技術大学院大学1, ソーク研究所2 Japan Advanced Institute of Science and Technology1, The Salk Institute for Biological Studies, La Jolla, U.S.A.2 How neurons in the primary motor cortex control arm movements is not yet fully understood, and understanding the cortical mechanism is indispensable for developing an effective method for functional recovery after stroke. Here I show that the equations of motion governing reaching simplify when expressed in spatial coordinates. In this fixed reference frame, joint torques are the sums of vector cross products between the spatial positions of limb segments and their spatial accelerations and velocities. The consequences that follow from this model explain many properties of neurons in the motor cortex, including directional broad, cosine-like tuning, non-uniformly distributed preferred directions dependent on the workspace, and the rotation of the population vector during arm movements. Remarkably, the torques can be directly computed as a linearly weighted sum of responses from cortical motor neurons, and the muscle tensions can be obtained as rectified linear sums of the joint torques. This allows the required muscle tensions to be computed rapidly from a trajectory in space with a feedforward network model. I demonstrate, in addition, that the direct mapping from kinematics to dynamics using the cross-product representation can be implemented with an inexpensive depth sensor such as Kinect.

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