Similar neural states, but dissimilar decoding patterns for motor control in parietal cortex.

ABSTRACT

Discrete neural states are associated with reaching movements across the fronto-parietal network. Here, the Hidden Markov Model (HMM) applied to spiking activity of the somato-motor parietal area PE revealed a sequence of states similar to those of the contiguous visuomotor areas PEc and V6A. Using a coupled clustering and decoding approach, we proved that these neural states carried spatiotemporal information regarding behaviour in all three posterior parietal areas. However, comparing decoding accuracy, PE was less informative than V6A and PEc. In addition, V6A outperformed PEc in target inference, indicating functional differences among the parietal areas. To check the consistency of these differences, we used both a supervised and an unsupervised variant of the HMM, and compared its performance with two more common classifiers, Support Vector Machine and Long-Short Term Memory. The differences in decoding between areas were invariant to the algorithm used, still showing the dissimilarities found with HMM, thus indicating that these dissimilarities are intrinsic in the information encoded by parietal neurons. These results highlight that, when decoding from the parietal cortex, for example, in brain machine interface implementations, attention should be paid in selecting the most suitable source of neural signals, given the great heterogeneity of this cortical sector.

Applying HMMs to spiking activity recorded from the somato-motor parietal area PE revealed discrete neural states related to reaching movements. These states were extremely similar to those present in the neighbouring visuomotor areas PEc and V6A. Our decoding approach showed that these states conveyed spatiotemporal behaviour information across all three posterior parietal areas. However, decoding accuracy was lower in PE compared to V6A and PEc, with V6A excelling in target inference. These differences held true even when changing the decoding algorithm, indicating intrinsic dissimilarities in information encoding by parietal different areas. These findings highlight the importance of selecting the appropriate neural signal sources in applications such as brain machine interfaces and pave the way for further investigation of the nontrivial diversity within the parietal cortex.