Abstract
The cerebellar-thalamo-cortical (CTC) system plays a major role in controlling timing and coordination of voluntary movements. However, the functional impact of this system on motor cortical sites has not been documented in a systematic manner. We addressed this question by implanting a chronic stimulating electrode in the superior cerebellar peduncle (SCP) and recording evoked multiunit activity (MUA) and the local field potential (LFP) in the primary motor cortex (n= 926), the premotor cortex (n= 357) and the somatosensory cortex (n= 345). The area-dependent response properties were estimated using the MUA response shape (quantified by decomposing into principal components) and the time-dependent frequency content of the evoked LFP. Each of these signals alone enabled good classification between the somatosensory and motor sites. Good classification between the primary motor and premotor areas could only be achieved when combining features from both signal types. Topographical single-site representation of the predicted class showed good recovery of functional organization. Finally, the probability for misclassification had a broad topographical organization. Despite the area-specific response features to SCP stimulation, there was considerable site-to-site variation in responses, specifically within the motor cortical areas. This indicates a substantial SCP impact on both the primary motor and premotor cortex. Given the documented involvement of these cortical areas in preparation and execution of movement, this result may suggest a CTC contribution to both motor execution and motor preparation. The stimulation responses in the somatosensory cortex were sparser and weaker. However, a functional role of the CTC system in somatosensory computation must be taken into consideration.
Original language | English |
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Pages (from-to) | 141-152 |
Number of pages | 12 |
Journal | Biological Cybernetics |
Volume | 112 |
Issue number | 1-2 |
DOIs | |
State | Published - 1 Apr 2018 |
Bibliographical note
Publisher Copyright:© 2017, Springer-Verlag GmbH Germany.
Keywords
- Cerebellum
- Local field potential
- Machine learning
- Motor control
- Multiunit activity
- Thalamocortical