Selectivity for grip type and action goal in macaque inferior parietal and ventral premotor grasping neurons.

Grasping objects requires the selection of specific grip postures in relation to objects' physical properties. Furthermore, grasping acts can be embedded into actions aimed at different goals, depending on the context in which the action is performed. Here we assessed whether information on grip and action type integrate at the single neuron level within the parieto-frontal motor system. For this purpose, we trained three monkeys to perform simple grasp-to-eat and grasp-to-place actions, depending on contextual cues, in which different grip types were required, in relation to target features. We recorded 173 grasping neurons: 86 from the inferior parietal area PFG and 87 from the ventral premotor area F5. Results showed that most neurons in both areas are selective for the grip type, but the discharge of many of them, particularly in PFG, appears to differ in relation to action context. Kinematics data and control experiments indicated that neuronal selectivity appears to more likely depend on the action goal triggered by the context rather than on specific contextual elements. The temporal dynamics of grip and goal selectivity showed that grasping neurons reflect first "how" the object has to be grasped (grip), to guide and monitor the hand shaping phase, then "why" the action is performed (goal), very likely to facilitate subsequent motor acts following grasping. These findings suggest that, in the parieto-frontal system, grip types and action goals are processed by both parallel and converging pathways, and area PFG appears to be particularly relevant for integrating this information for action organization

Cortical and subcortical connections of parietal and premotor nodes of the monkey hand mirror neuron network

Mirror neurons (MNs) are a class of cells originally discovered in the monkey ventral premotor cortex (PMv) and inferior parietal lobule (IPL). They discharge during both action execution and action observation and appear to play a crucial role in understanding others' actions. It has been proposed that the mirror mechanism is based on a match between the visual description of actions, encoded in temporal cortical regions, and their motor representation, provided by PMv and IPL. However, neurons responding to action observation have been recently found in other cortical regions, suggesting that the mirror mechanism relies on a wider network. Here we provide the first description of this network by injecting neural tracers into physiologically identified IPL and PMv sectors containing hand MNs. Our results show that these sectors are reciprocally connected, in line with the current view, but IPL MN sectors showed virtually no direct connection with temporal visual areas. In addition, we found that PMv and IPL MN sectors share connections with several cortical regions, including the dorsal and mesial premotor cortex, the primary motor cortex, the secondary somatosensory cortex, the mid-dorsal insula and the ventrolateral prefrontal cortex, as well as subcortical structures, such as motor and polysensory thalamic nuclei and the mid-dorsal claustrum. We propose that each of these regions constitutes a node of an â\u80\u9cextended networkâ\u80\u9d, through which information relative to ongoing movements, social context, environmental contingencies, abstract rules, and internal states can influence MN activity and contribute to several socio-cognitive functions

Toward Automated Electrode Selection in the Electronic Depth Control Strategy for Multi-unit Recordings

Multi-electrode arrays contain an increasing number of electrodes. The manual selection of good quality signals among hundreds of electrodes becomes impracticable for experimental neuroscientists. This increases the need for an automated selection of electrodes containing good quality signals. To motivate the automated selection, three experimenters were asked to assign quality scores, taking one of four possible values, to recordings containing action potentials obtained from the monkey primary somatosensory cortex and the superior parietal lobule. Krippendorff’s alpha-reliability was then used to verify whether the scores, given by different experimenters, were in agreement. A Gaussian process classifier was used to automate the prediction of the signal quality using the scores of the different experimenters. Prediction accuracies of the Gaussian process classifier are about 80% when the quality scores of different experimenters are combined, through a median vote, to train the Gaussian process classifier. It was found that predictions based also on firing rate features are in closer agreement with the experimenters’ assignments than those based on the signal-to-noise ratio alone.status: publishe