Multi-area activity in mouse motor cortex associated with one- and two-handed oromanual dexterity
Barrett, J. M.; Glaser, J. I.; Miri, A.; Shepherd, G. M. G.
Show abstract
Cortical dynamics during goal-directed dexterous hand movements are mainly understood from paradigms involving use of the contralateral hand. To study how movement-related activity changes when the ipsilateral or both hands are used, we exploited a natural form of rodent manual dexterity - food handling - that rodents can perform uni- or bimanually. We sampled kilohertz 3D kinematics as mice used either or both hands to manipulate food, while recording spiking activity in forelimb primary (fl-M1) and secondary (fl-M2) motor cortices, and in a lateral oral and manual (LOM) motor cortex area implicated in oromanual food handling. Unit- and population-level analyses showed that activity in fl-M1 and fl-M2 depended on both laterality (ipsi- vs contralateral) and "manuality" (uni- vs bimanual), with few differences between the two areas. By comparison, activity in LOM was largely laterality- and manuality-invariant. These results demonstrate how activity in multiple areas of mouse motor cortex varies as the same task is performed unimanually with either hand or bimanually with both. Our findings support a model in which fl-M1 and fl-M2 maintain separable information about both forelimbs for bimanual coordination, while LOM encodes ingestion-related forelimb parameters necessary for oromanual coordination.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Cortical Reactivation of Non-Spatial and Spatial Memory Representations Coordinate with Hippocampus to Form a Memory Dialogue 97%
- Place cell map genesis via competitive learning and conjunctive coding in the dentate gyrus 96%
- Continuous multiplexed population representations of task context in the mouse primary visual cortex 96%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.