Area-specific thalamocortical synchronization underlies the transition from motor planning to execution.

We studied correlated firing between motor thalamic and cortical cells in monkeys performing a delayed-response reaching task. Simultaneous recording of thalamocortical activity revealed that around movement onset, thalamic cells were positively correlated with cell activity in the primary motor cortex but negatively correlated with the activity of the premotor cortex. The differences in the correlation contrasted with the average neural responses, which were similar in all three areas. Neuronal correlations reveal functional cooperation and opposition between the motor thalamus and distinct motor cortical areas with specific roles in planning vs. performing movements. Thus, by enhancing and suppressing motor and premotor firing, the motor thalamus can facilitate the transition from a motor plan to execution.

An assumption-free quantification of neural responses to electrical stimulations.

BACKGROUND: Connectivity between brain regions provides the fundamental infrastructure for information processing. The standard way to characterize these interactions is to stimulate one site while recording the evoked response from a second site. The average stimulus-triggered response is usually compared to the pre-stimulus activity. This requires a set of prior assumptions regarding the amplitude and duration of the evoked response. NEW METHOD: We introduce an assumption-free method for detecting and clustering evoked responses. We used Independent Component Analysis to reduce the dimensions of the response vectors, and then clustered them according to a Gaussian mixture model. This enables both the detection and categorization of responsive sites into different subtypes. RESULTS: Our method is demonstrated on recordings obtained from the sensory-motor cortex of behaving primates in response to stimulation of the cerebello-thalamo-cortical tract. We detected and classified the evoked responses of local field potential (LFP) and local spiking activity (multiunit activity-MUA). We found a strong association between specific input (LFP) and output (MUA) patterns across cortical sites, further supporting the physiological relevance of the proposed method. COMPARISON WITH EXISTING METHODS: Our method detected the vast majority of sites found in the conventional, significant threshold-crossing method. However, we found a subgroup of sites with a robust response that were missed when using the conventional method. CONCLUSION: Our method provides a useful, assumption-free tool for detecting and classifying neural evoked responses in a physiologically-relevant manner.