Department of Experimental Psychology, Justus Liebig University Giessen, 35390 Giessen, Germany.
School of Psychology, University of Southampton, Southampton SO17 1PS, United Kingdom
J Neurosci. 2023 Dec 6;43(49):8504-8514. doi: 10.1523/JNEUROSCI.0335-23.2023.
Selecting suitable grasps on three-dimensional objects is a challenging visuomotor computation, which involves combining information about an object (e.g., its shape, size, and mass) with information about the actor's body (e.g., the optimal grasp aperture and hand posture for comfortable manipulation). Here, we used functional magnetic resonance imaging to investigate brain networks associated with these distinct aspects during grasp planning and execution. Human participants of either sex viewed and then executed preselected grasps on L-shaped objects made of wood and/or brass. By leveraging a computational approach that accurately predicts human grasp locations, we selected grasp points that disentangled the role of multiple grasp-relevant factors, that is, grasp axis, grasp size, and object mass. Representational Similarity Analysis revealed that grasp axis was encoded along dorsal-stream regions during grasp planning. Grasp size was first encoded in ventral stream areas during grasp planning then in premotor regions during grasp execution. Object mass was encoded in ventral stream and (pre)motor regions only during grasp execution. Premotor regions further encoded visual predictions of grasp comfort, whereas the ventral stream encoded grasp comfort during execution, suggesting its involvement in haptic evaluation. These shifts in neural representations thus capture the sensorimotor transformations that allow humans to grasp objects. Grasping requires integrating object properties with constraints on hand and arm postures. Using a computational approach that accurately predicts human grasp locations by combining such constraints, we selected grasps on objects that disentangled the relative contributions of object mass, grasp size, and grasp axis during grasp planning and execution in a neuroimaging study. Our findings reveal a greater role of dorsal-stream visuomotor areas during grasp planning, and, surprisingly, increasing ventral stream engagement during execution. We propose that during planning, visuomotor representations initially encode grasp axis and size. Perceptual representations of object material properties become more relevant instead as the hand approaches the object and motor programs are refined with estimates of the grip forces required to successfully lift the object.
选择适合三维物体的抓握方式是一项具有挑战性的视动计算任务，它涉及到将关于物体的信息（例如物体的形状、大小和质量）与关于身体的信息（例如舒适操作的最佳抓握开口和手的姿势）结合起来。在这里，我们使用功能磁共振成像技术来研究在抓握规划和执行过程中与这些不同方面相关的大脑网络。男性和女性的人类参与者观看并执行预先选择的对木质和/或黄铜制成的 L 形物体的抓握。通过利用一种能够准确预测人类抓握位置的计算方法，我们选择了能够分离多个与抓握相关因素（即抓握轴、抓握大小和物体质量）的抓握点。代表性相似性分析表明，在抓握规划过程中，抓握轴沿背侧流区域进行编码。在抓握规划过程中，抓握大小首先在腹侧流区域进行编码，然后在抓握执行过程中在运动前区域进行编码。物体质量仅在抓握执行过程中在腹侧流和（运动前）区域进行编码。运动前区域进一步对抓握舒适性的视觉预测进行编码，而腹侧流对执行过程中的抓握舒适性进行编码，表明其参与了触觉评估。这些神经表示的转变因此捕获了允许人类抓握物体的感觉运动转换。抓握需要将物体属性与对手和手臂姿势的约束相结合。在一项神经影像学研究中，我们使用了一种计算方法，该方法通过结合这些约束条件，准确地预测了人类的抓握位置，选择了在物体上进行抓握，从而在抓握规划和执行过程中分离了物体质量、抓握大小和抓握轴的相对贡献。我们的研究结果表明，在抓握规划过程中，背侧流视动区域发挥了更大的作用，而在执行过程中，腹侧流的参与度却在增加。我们提出，在规划阶段，视动代表最初编码抓握轴和大小。随着手接近物体，物体材料属性的知觉代表变得更加相关，并且随着对成功提起物体所需的握力的估计，对运动程序进行了细化。
