Context-dependent limb movement encoding in neuronal populations of motor cortex

Wolfgang Omlor,Balazs Laurenczy,Philipp Bethge,Fritjof Helmchen,Anna-Sophia Wahl,Fabian F Voigt,Pia Sipilä,Henry Lütcke,Martin E Schwab,Lazar T Sumanovski,Marcel Van ’T Hoff,I-Wen Chen

doi:10.1038/s41467-019-12670-z

Abstract

Neuronal networks of the mammalian motor cortex (M1) are important for dexterous control of limb joints. Yet it remains unclear how encoding of joint movement in M1 depends on varying environmental contexts. Using calcium imaging we measured neuronal activity in layer 2/3 of the M1 forelimb region while mice grasped regularly or irregularly spaced ladder rungs during locomotion. We found that population coding of forelimb joint movements is sparse and varies according to the flexibility demanded from individual joints in the regular and irregular context, even for equivalent grasping actions across conditions. This context-dependence of M1 encoding emerged during task learning, fostering higher precision of grasping actions, but broke apart upon silencing of projections from secondary motor cortex (M2). These findings suggest that M1 exploits information from M2 to adapt encoding of joint movements to the flexibility demands of distinct familiar contexts, thereby increasing the accuracy of motor output.

Highlights

Neuronal networks of the mammalian motor cortex (M1) are important for dexterous control of limb joints
We show that joint movements are differentially encoded in Layer 2/3 (L2/3) neuronal networks of M1 in the regular and irregular context, respectively, even for motor actions with matching kinematic profile, and that encoding strength increases if a higher flexibility is demanded and vice versa
We found that betweencondition differences in joint angle prediction by neuronal networks were significantly explained by corresponding between-condition differences in joint angle grasp-to-grasp variability (GGV) (Fig. 5d; P < 0.0001, r2 = 0.49, n = 9 networks, four joint angles for each; linear regression with clustered standard error; cluster variable = neuronal network; Methods)

Summary

Introduction

Neuronal networks of the mammalian motor cortex (M1) are important for dexterous control of limb joints. We found that population coding of forelimb joint movements is sparse and varies according to the flexibility demanded from individual joints in the regular and irregular context, even for equivalent grasping actions across conditions. This contextdependence of M1 encoding emerged during task learning, fostering higher precision of grasping actions, but broke apart upon silencing of projections from secondary motor cortex (M2). The representation of a specific limb movement may be flexibly modulated according to certain principles when the same motor action is executed in different environmental contexts Such contextdependent modulation of M1 neuronal encoding according to environment-characterizing meta-variables has been scarcely investigated so far. Using chemogenetic silencing we show that context-dependent modulation of L2/3 neuronal representation in M1 entails more precise limb movements and requires input from M2

Methods

Results

Conclusion