Identification of neuronal network properties from the spectral analysis of calcium imaging signals in neuronal cultures.
Identification of neuronal network properties from the spectral analysis of calcium imaging signals in neuronal cultures.
复制标题
DOI：
10.3389/fncir.2013.00199
复制
发表时间：
2013
影响因子：
3.5
通讯作者：
Soriano J
中科院分区：
文献类型：
--
作者：
Tibau E;Valencia M;Soriano J
体外神经网络是研究活体神经网络连接发展以及连接、活动和功能之间相互作用的重要系统。这些培养的网络显示出丰富的自发活动，与潜在网络的连通性同时发展。在这项工作中，我们监测神经元培养的发展，并使用钙荧光成像记录它们的活动。我们使用光谱分析来表征神经元培养的全局动态和结构特征。我们首先观察到，功率谱可以用作网络状态的标志，例如，当抑制是活跃或沉默时，以及网络连接强度的度量。其次，功率谱识别出GABAA开关等网络中显著的发展变化。第三，通过在兴奋性神经元中使用ampa -谷氨酸受体拮抗剂CNQX控制网络瓦解的实验，对光谱密度的空间分布进行了分析，揭示了存在强连接、高度活跃的神经元群落，它们表现出同步振荡。我们的工作说明了光谱分析对体外网络研究的兴趣，以及它作为网络状态指标的潜在用途，例如比较健康和患病的神经网络。
Neuronal networks in vitro are prominent systems to study the development of connections in living neuronal networks and the interplay between connectivity, activity and function. These cultured networks show a rich spontaneous activity that evolves concurrently with the connectivity of the underlying network. In this work we monitor the development of neuronal cultures, and record their activity using calcium fluorescence imaging. We use spectral analysis to characterize global dynamical and structural traits of the neuronal cultures. We first observe that the power spectrum can be used as a signature of the state of the network, for instance when inhibition is active or silent, as well as a measure of the network's connectivity strength. Second, the power spectrum identifies prominent developmental changes in the network such as GABAA switch. And third, the analysis of the spatial distribution of the spectral density, in experiments with a controlled disintegration of the network through CNQX, an AMPA-glutamate receptor antagonist in excitatory neurons, reveals the existence of communities of strongly connected, highly active neurons that display synchronous oscillations. Our work illustrates the interest of spectral analysis for the study of in vitro networks, and its potential use as a network-state indicator, for instance to compare healthy and diseased neuronal networks.
登录
查看更多内容
影响因子：
19.9
作者：
Bassett, Danielle S.;Gazzaniga, Michael S.
通讯作者：
Gazzaniga, Michael S.
影响因子：
16.2
作者：
Grienberger, Christine;Konnerth, Arthur
通讯作者：
Konnerth, Arthur
影响因子：
5.3
作者：
Lynall, Mary-Ellen;Bassett, Danielle S.;Bullmore, Edward T.
通讯作者：
Bullmore, Edward T.
影响因子：
64.8
作者：
Ohki, K;Chung, S;Reid, RC
通讯作者：
Reid, RC
影响因子：
2.5
作者：
Opitz, T;De Lima, AD;Voigt, T
通讯作者：
Voigt, T