Localized Tuning Fields for 3D Hand Position in the Primary Motor Cortex and Premotor Cortex of Macaques

Abstract

A central question in motor neuroscience is how the brain represents the state of the limbs to guide volitional movements. While the primate motor cortex is known to encode movement kinematics, such as velocity and direction, whether it also maintains a direct and explicit representation of hand position in 3D space remains debated. To address this, we recorded the activity of single neurons in the primary motor cortex (M1) and dorsal premotor cortex (PMd) of two male rhesus macaques performing a naturalistic, self-paced 3D reach-and-grasp task. We found significant populations of neurons in both M1 (36.2%) and PMd (21.3%) that are robustly tuned to the instantaneous 3D position of the hand. In these neurons, the tuning for hand position—characterized by localized, elongated fields—coexists with tunings for other kinematic variables, reflecting the principle of mixed selectivity. Critically, the spatial organization of these representations differs between the two areas: M1 fields are systematically oriented along cardinal axes and exhibit multi-scale spatial clustering, whereas PMd fields are more randomly organized. Furthermore, a small subset of these hand position-tuned cells is sufficient to decode the hand’s 3D trajectory with high fidelity. Our findings demonstrate that an explicit and functionally organized representation of 3D hand position is a fundamental component of primate motor cortex, complementing dynamic motor signals to support high-fidelity motor control. Significance Statement To guide skilled actions, the brain must track the hand's location. While the motor cortex is known for controlling movement commands, we reveal it also creates an explicit, highly organized 3D map of hand position. This neural representation is systematically structured, differing between primary and premotor areas. This discovery reshapes our understanding of motor control, showing the brain merges spatial information (“where”) with motor commands (“how”) of the hand in the same areas. These insights are crucial for creating more effective brain-computer interfaces for individuals with paralysis.