Learned dynamics of reaching movements generalize from dominant to nondominant arm
Learned dynamics of reaching movements generalize from dominant to nondominant arm
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DOI：
10.1152/jn.00622.2002
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发表时间：
2003-01-01
影响因子：
2.5
通讯作者：
Shadmehr, R
中科院分区：
文献类型：
--
作者：
Criscimagna-Hemminger, SE;Donchin, O;Shadmehr, R
准确的伸展动作取决于适应性神经回路，它学会预测力量并补偿肢体动力。在早期的实验中，我们量化了从一个手臂位置到另一个位置的训练泛化。概括模式表明，学习预测力的神经元在一个内在的、类似肌肉的坐标系中编码肢体的状态。在这里，我们通过量化臂间泛化来测试这些元素对另一臂的敏感性。我们考虑了两种可能的坐标系：一个内在的（联合）表示应概括与镜像对称反映联合的对称性和一个外在的表示应保持任务的结构在外在坐标。这两个坐标系统的泛化进行了比较，一个天真的控制组。我们测试了右手受试者从优势臂到非优势臂（D-->ND）的迁移，反之亦然（ND-->D）。这导致了2 × 3的实验设计矩阵：通过坐标系（非本征、本征、控制）的转移方向（D-->ND/ND-->D）。泛化只发生在显性臂到非显性臂，并且只发生在外在坐标中。为了评估概括对胼胝体半球间通信的依赖性，我们测试了连合切开术患者JW。JW表现出泛化从显性到非显性臂的外在坐标。结果表明，当使用优势右臂学习动力学时，信息可以在左半球与右臂和左臂相协调的神经元一起呈现;相反，使用非优势手臂学习似乎依赖于仅与该手臂运动相协调的非优势半球的神经元。
Accurate performance of reaching movements depends on adaptable neural circuitry that learns to predict forces and compensate for limb dynamics. In earlier experiments, we quantified generalization from training at one arm position to another position. The generalization patterns suggested that neural elements learning to predict forces coded a limb's state in an intrinsic, muscle-like coordinate system. Here, we test the sensitivity of these elements to the other arm by quantifying inter-arm generalization. We considered two possible coordinate systems: an intrinsic (joint) representation should generalize with mirror symmetry reflecting the joint's symmetry and an extrinsic representation should preserve the task's structure in extrinsic coordinates. Both coordinate systems of generalization were compared with a naive control group. We tested transfer in right-handed subjects both from dominant to nondominant arm (D-->ND) and vice versa (ND-->D). This led to a 2 x 3 experimental design matrix: transfer direction (D-->ND/ND-->D) by coordinate system (extrinsic, intrinsic, control). Generalization occurred only from dominant to nondominant arm and only in extrinsic coordinates. To assess the dependence of generalization on callosal inter-hemispheric communication, we tested commissurotomy patient JW. JW showed generalization from dominant to nondominant arm in extrinsic coordinates. The results suggest that when the dominant right arm is used in learning dynamics, the information could be represented in the left hemisphere with neural elements tuned to both the right arm and the left arm. In contrast, learning with the nondominant arm seems to rely on the elements in the nondominant hemisphere tuned only to movements of that arm.