Reference frame of human medial intraparietal cortex in visually guided movements

Distinct neural representations of hand movement direction between motor imagery and execution in the presupplementary motor area

Motor simulation theory proposes a functional equivalence between motor execution (ME) and its simulation, suggesting that motor imagery (MI) is the self-intentioned simulation of one's actions. This study used functional magnetic resonance imaging (fMRI) with multivoxel pattern analysis to test whether the direction of hand movement is represented with a similar neural code between ME and MI. In our study, participants used their right hand to move an on-screen cursor in the left-right direction with a joystick or imagined the same movement without execution. The results indicated that the left-right direction as well as their modality (ME or MI) could be decoded significantly above the chance level in the presupplementary motor area (pre-SMA) and primary visual cortex (V1). Next, we used activation patterns of ME as inputs to the decoder to predict hand move directions in MI sessions and found a significantly higher-than-chance accuracy only in V1, not in pre-SMA. Moreover, the representational similarity analysis showed similar activation patterns for the same directions between ME and MI in V1 but not in pre-SMA. This study's finding indicates distinct spatial activation patterns for movement directions between ME and MI in pre-SMA.

The Importance of Lateral Connections in the Parietal Cortex for Generating Motor Plans

Substantial evidence has highlighted the significant role of associative brain areas, such as the posterior parietal cortex (PPC) in transforming multimodal sensory information into motor plans. However, little is known about how different sensory information, which can have different delays or be absent, combines to produce a motor plan, such as executing a reaching movement. To address these issues, we constructed four biologically plausible network architectures to simulate PPC: 1) feedforward from sensory input to the PPC to a motor output area, 2) feedforward with the addition of an efference copy from the motor area, 3) feedforward with the addition of lateral or recurrent connectivity across PPC neurons, and 4) feedforward plus efference copy, and lateral connections. Using an evolutionary strategy, the connectivity of these network architectures was evolved to execute visually guided movements, where the target stimulus provided visual input for the entirety of each trial. The models were then tested on a memory guided motor task, where the visual target disappeared after a short duration. Sensory input to the neural networks had sensory delays consistent with results from monkey studies. We found that lateral connections within the PPC resulted in smoother movements and were necessary for accurate movements in the absence of visual input. The addition of lateral connections resulted in velocity profiles consistent with those observed in human and non-human primate visually guided studies of reaching, and allowed for smooth, rapid, and accurate movements under all conditions. In contrast, Feedforward or Feedback architectures were insufficient to overcome these challenges. Our results suggest that intrinsic lateral connections are critical for executing accurate, smooth motor plans.

Feature interactions enable decoding of sensorimotor transformations for goal-directed movement

Neurophysiology and neuroimaging evidence shows that the brain represents multiple environmental and body-related features to compute transformations from sensory input to motor output. However, it is unclear how these features interact during goal-directed movement. To investigate this issue, we examined the representations of sensory and motor features of human hand movements within the left-hemisphere motor network. In a rapid event-related fMRI design, we measured cortical activity as participants performed right-handed movements at the wrist, with either of two postures and two amplitudes, to move a cursor to targets at different locations. Using a multivoxel analysis technique with rigorous generalization tests, we reliably distinguished representations of task-related features (primarily target location, movement direction, and posture) in multiple regions. In particular, we identified an interaction between target location and movement direction in the superior parietal lobule, which may underlie a transformation from the location of the target in space to a movement vector. In addition, we found an influence of posture on primary motor, premotor, and parietal regions. Together, these results reveal the complex interactions between different sensory and motor features that drive the computation of sensorimotor transformations.

Human sensorimotor cortex represents conflicting visuomotor mappings

Behavioral studies have shown that humans can adapt to conflicting sensorimotor mappings that cause interference after intensive training. While previous research works indicate the involvement of distinct brain regions for different types of motor learning (e.g., kinematics vs dynamics), the neural mechanisms underlying joint adaptation to conflicting mappings within the same type of perturbation (e.g., different angles of visuomotor rotation) remain unclear. To reveal the neural substrates that represent multiple sensorimotor mappings, we examined whether different mappings could be classified with multivoxel activity patterns of functional magnetic resonance imaging data. Participants simultaneously adapted to opposite rotational perturbations (+90° and - 90°) during visuomotor tracking. To dissociate differences in movement kinematics with rotation types, we used two distinct patterns of target motion and tested generalization of the classifier between different combinations of rotation and motion types. Results showed that the rotation types were classified significantly above chance using activities in the primary sensorimotor cortex and the supplementary motor area, despite no significant difference in averaged signal amplitudes within the region. In contrast, low-level sensorimotor components, including tracking error and movement speed, were best classified using activities of the early visual cortex. Our results reveal that the sensorimotor cortex represents different visuomotor mappings, which permits joint learning and switching between conflicting sensorimotor skills.