运动技能学习过程中功能连接体的重新布线动态

Saber Meamardoost, Mahasweta Bhattacharya, Eun Jung Hwang, Chi Ren, Linbing Wang, Claudia Mewes, Ying Zhang, Takaki Komiyama, Rudiyanto Gunawan
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引用次数: 0

摘要

在生物体的一生中,大脑的功能连接组不断地重新连接。在这项研究中,我们试图通过分析清醒小鼠在杠杆按压任务学习过程中M1的2/3层(L2/3)和5层(L5)神经元活动的钙成像来阐明小鼠初级运动皮层(M1)这种重新连接的运作原理。我们的研究结果表明L2/3和L5功能连接体遵循类似的学习诱导重新布线轨迹。更具体地说,连接体以双相方式重新连接,其中功能连接在最初的几个学习过程中增加,然后逐渐修剪以恢复到稳态网络密度水平。我们发现L2/3连接体的网络连通性的增加,而L5连接体的网络连通性的增加,产生与运动表现改善(更短的提示-奖励时间)相关的神经元共放电活动,而运动表现在整个修剪阶段保持相对稳定。结果显示了一个涉及奖励/绩效最大化和网络密度维持的双阶段重新布线原则。最后,我们证明了L2/3的连接体重新布线是围绕一组核心的运动相关神经元聚集的,这些神经元在连接体中形成了一个高度互联的中枢,这些核心神经元的活动在学习过程中稳定地编码运动。
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Rewiring Dynamics of Functional Connectomes during Motor-Skill Learning
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.
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