Introduction The activity of the primary motor cortex (M1) across stages of a motor task evolves through attractors that capture the dominant activity when preparing and executing a task [1, 2]. This suggests that dimensionality reduction (DR) plays a key role in how M1 controls movements. In primates, in addition to M1, motor commands are generated by a network of frontal areas, the premotor cortex. Neurons in the premotor ventral (PMv) and dorsal (PMd) cortices discharge in relation to various parameters of movements and send projections to M1 [3]. We extend DR techniques to PMv, PMd, and M1 to characterize variation in neural population activity in context of reaching and grasping movements and identify the most explanatory neurons in these cortices.
Methods Our data was collected from four rhesus macaque monkeys implanted with microelectrode arrays in the distal (hand) representation of M1, PMv, and PMd. We recorded isolated neurons spiking activity while monkeys performed a custom-made reach-to-grasp task. Following instruction cues, they reached with their left or right arm to grab a pellet or press on a plate using precision grasps in a vertical or horizontal orientation. For each neuron, we computed spike density estimates (SDE) by splicing peri-event windows and normalizing across all trials (like in [4]) for each hand-orientation combination. The condition-wise SDEs of all neurons were concatenated along the time dimension to perform principal component analysis (PCA) for each cortex.
Results PCA of the neural population activity in each cortex demonstrates differences across conditions. For each cortex, the first 3 principal components capture over 90% of the variance of the neural population dynamics. Low-dimensional trajectories of neural population activity in M1 shows greater divergence in neural activity when varying the hand used than varying target orientation. However, these low-dimensional trajectories across conditions are more similar for the premotor areas, with PMv having the most similarity. Moreover, the principal angles between the subspaces of the principal components for the hand used show that the neuron weights are more consistent for PMv, demonstrating less effector dependence in PMv than in PMd or M1.
Discussion The principal components (PCs) in each cortex indicate the weight assigned to each neuron which yields a sorting based on the explainability of the population dynamics. Combining this sorting with independent classification techniques of individual neurons allows for selection and classification of the most important neuron types in a population. Meanwhile, the differences in effector dependence and principal angles between M1, PMd, and PMv suggest a hierarchical structure of signals. Effector-independent PMv activity may structure the common movement parameters before PMd facilitates the more effector-dependent preparation. Low-dimensional representations such as PCA could explain this structure through the coupling of PCs across cortices.
References [1] Churchland, M. M., et al. Stimulus onset quenches neural variability: a widespread cortical phenomenon. Nature neuroscience, 13(3), 369–378. https://doi.org/10.1038/nn.2501 [2] Davare, M., et al. Dissociating the role of ventral and dorsal premotor cortex in precision grasping. The Journal of neuroscience, 26(8), 2260–2268. https://doi.org/10.1523/JNEUROSCI.3386-05.2006 [3] Shenoy, K. V., Sahani, M. et Churchland, M. M. (2013). Cortical Control of Arm Movements: A Dynamical Systems Perspective. Annual Review of Neuroscience, 36, 337–359. https://doi.org/10.1146/annurev-neuro-062111-150509 [4] Zimnik, A. A.-O., et al. Identifying Interpretable Latent Factors with Sparse Component Analysis. bioRxiv: the preprint server for biology, https://doi.org/10.1101/2024.02.05.578988
Acknowledgement This research was supported by CIHR Grant No. 175069 and the FRQNT Strategic Clusters Program (Centre UNIQUE - Centre de recherche Neuro-IA du Québec).
