Dynamic structure of motor cortical neuron coactivity carries behaviorally relevant information.
Dynamic structure of motor cortical neuron coactivity carries behaviorally relevant information.
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DOI：
10.1162/netn_a_00298
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发表时间：
2023
影响因子：
4.7
通讯作者：
MacLean, Jason N.
中科院分区：
文献类型：
--
作者：
Sundiang, Marina;Hatsopoulos, Nicholas G.;MacLean, Jason N.
熟练的自主运动是由初级运动皮层（M1）中相互连接的神经元网络进行计算的基础。神经元之间的协同活动模式反映了计算。使用成对尖峰时间统计，共活动可以概括为一个功能网络（FN）。在这里，我们表明，从非人类灵长类动物的延迟到达任务构建的FN的结构是行为特异性的：低维嵌入和图对齐分数表明，从更接近的目标到达方向构建的FN在网络空间中也更接近。使用短的时间间隔在一个试验中，我们构建了时间的FN，并发现时间的FN遍历一个低维子空间中达到特定的轨迹。对齐分数表明，FN成为可分离的和相应的解码后不久的指令线索。最后，我们观察到，在FN的相互连接短暂减少以下的指令线索，与外部的信息记录人口暂时改变网络的结构在这一刻的假设一致。目前还不清楚运动皮层神经元如何灵活地执行产生运动所需的计算。我们假设，神经元的协同活动包含运动信息，它的动态可以揭示如何在一个任务的人口开关计算。我们将协同活动量化为一个功能网络（FN），其中单个神经元作为节点，群体协同活动作为有向加权边。我们还在整个试验的短时期内构建了FN，以确定共同活动何时开始携带信息并研究这些相互作用的动态结构。在指令提示之后，FN中的相互连接暂时减少，并且不久之后，FN对于到达方向变得最大可解码。
Skillful, voluntary movements are underpinned by computations performed by networks of interconnected neurons in the primary motor cortex (M1). Computations are reflected by patterns of coactivity between neurons. Using pairwise spike time statistics, coactivity can be summarized as a functional network (FN). Here, we show that the structure of FNs constructed from an instructed-delay reach task in nonhuman primates is behaviorally specific: Low-dimensional embedding and graph alignment scores show that FNs constructed from closer target reach directions are also closer in network space. Using short intervals across a trial, we constructed temporal FNs and found that temporal FNs traverse a low-dimensional subspace in a reach-specific trajectory. Alignment scores show that FNs become separable and correspondingly decodable shortly after the Instruction cue. Finally, we observe that reciprocal connections in FNs transiently decrease following the Instruction cue, consistent with the hypothesis that information external to the recorded population temporarily alters the structure of the network at this moment. It remains unclear how motor cortical neurons flexibly perform the computations necessary to generate movement. We hypothesized that neuronal coactivity contains movement information, and its dynamics can reveal how the population switches computations during a task. We quantified coactivity as a functional network (FN) with single neurons as nodes and population coactivity as directed weighted edges. We also constructed FNs within short epochs across a trial to determine when coactivity begins to carry information and to investigate the dynamic structure of these interactions. Following the Instruction cue, reciprocal connections in FNs transiently decrease, and shortly after, FNs become maximally decodable for reach direction.
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影响因子：
2.5
作者：
AERTSEN, AMHJ;GERSTEIN, GL;PALM, G
通讯作者：
PALM, G
影响因子：
4.3
作者：
Dechery JB;MacLean JN
通讯作者：
MacLean JN
影响因子：
8.6
作者：
Garlaschelli, D;Loffredo, MI
通讯作者：
Loffredo, MI
DOI：
10.1073/pnas.95.26.15706
发表时间：
1998-12-22
影响因子：
11.1
作者：
Hatsopoulos, NG;Ojakangas, CL;Donoghue, JP
通讯作者：
Donoghue, JP
影响因子：
5.3
作者：
Hatsopoulos, Nicholas G.;Xu, Qingqing;Amit, Yali
通讯作者：
Amit, Yali