Rewiring Dynamics of Functional Connectomes during Motor-Skill Learning

Abstract

The brain’s functional connectome continually rewires throughout an organism’s life. In this study, we sought to elucidate the operational principles of such rewiring in mouse primary motor cortex (M1) by analyzing calcium imaging of layer 2/3 (L2/3) and layer 5 (L5) neuronal activity in M1 of awake mice during a lever-press task learning. Our results show that L2/3 and L5 functional connectomes follow a similar learning-induced rewiring trajectory. More specifically, the connectomes rewire in a biphasic manner, where functional connectivity increases over the first few learning sessions, and then, it is gradually pruned to return to a homeostatic level of network density. We demonstrated that the increase of network connectivity in L2/3 connectomes, but not in L5, generates neuronal co-firing activity that correlates with improved motor performance (shorter cue-to-reward time), while motor performance remains relatively stable throughout the pruning phase. The results show a biphasic rewiring principle that involves the maximization of reward/performance and maintenance of network density. Finally, we demonstrated that the connectome rewiring in L2/3 is clustered around a core set of movement-associated neurons that form a highly interconnected hub in the connectomes, and that the activity of these core neurons stably encodes movement throughout learning.

Keywords:

• Functional connectome
• rewiring
• learning
• motor skill
• motor cortex

Acknowledgement

The authors would like to acknowledge funding support from NSF-HDR IDEAS Lab (funding # 1939987, 1940202, 1940162, 1939999, and 1939992).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Functional connectomes and MATLAB and Python codes used in this study are available at https://github.com/CABSEL/Connectome-Rewiring. The method FARCI is available at https://github.com/CABSEL/FARCI.