Large-scale, high-resolution multielectrode-array recording depicts functional network differences of cortical and hippocampal cultures.
Large-scale, high-resolution multielectrode-array recording depicts functional network differences of cortical and hippocampal cultures.
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DOI：
10.1371/journal.pone.0105324
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Beggs JM
中科院分区：
文献类型：
--
作者：
Ito S;Yeh FC;Hiolski E;Rydygier P;Gunning DE;Hottowy P;Timme N;Litke AM;Beggs JM
了解功能神经元网络的详细电路是神经科学的主要目标之一。近年来神经元记录技术的发展使得以亚毫秒级的时间分辨率同时记录数百个神经元的放电活动成为可能。在这里，我们使用了512通道多电极阵列系统，以记录数百个神经元的活动，在器官型培养的皮质海马脑切片从小鼠。为了探索网络结构，我们采用了小波变换的交叉相关图分类功能连接在不同的频率范围。用这种方法，我们直接比较，第一次，在任何准备，皮层和海马的神经元网络结构，在数百个神经元的规模，亚毫秒的时间分辨率。在我们研究的三个频率范围中，较低的两个频率范围（γ（30-80 Hz）和β（12-30 Hz）范围）显示了皮层和海马之间相似的网络结构，但在高频范围（100-1000 Hz），这些结构之间存在许多显著差异。皮层高频网络的度分布短尾，连接密度衰减长度短，聚类系数小，具有正的自相关性。我们的研究结果表明，我们的方法可以从不同的大脑区域的网络架构的频率依赖性的差异。至关重要的是，由于大脑区域之间的这些差异需要毫秒级的时间尺度来观察和表征，这些结果强调了高时间分辨率记录对于理解神经元系统中的功能网络的重要性。
Understanding the detailed circuitry of functioning neuronal networks is one of the major goals of neuroscience. Recent improvements in neuronal recording techniques have made it possible to record the spiking activity from hundreds of neurons simultaneously with sub-millisecond temporal resolution. Here we used a 512-channel multielectrode array system to record the activity from hundreds of neurons in organotypic cultures of cortico-hippocampal brain slices from mice. To probe the network structure, we employed a wavelet transform of the cross-correlogram to categorize the functional connectivity in different frequency ranges. With this method we directly compare, for the first time, in any preparation, the neuronal network structures of cortex and hippocampus, on the scale of hundreds of neurons, with sub-millisecond time resolution. Among the three frequency ranges that we investigated, the lower two frequency ranges (gamma (30–80 Hz) and beta (12–30 Hz) range) showed similar network structure between cortex and hippocampus, but there were many significant differences between these structures in the high frequency range (100–1000 Hz). The high frequency networks in cortex showed short tailed degree-distributions, shorter decay length of connectivity density, smaller clustering coefficients, and positive assortativity. Our results suggest that our method can characterize frequency dependent differences of network architecture from different brain regions. Crucially, because these differences between brain regions require millisecond temporal scales to be observed and characterized, these results underscore the importance of high temporal resolution recordings for the understanding of functional networks in neuronal systems.
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细胞外田地和电流的起源-EEG，ECOG，LFP和尖峰。
DOI：
10.1038/nrn3241
发表时间：
2012-05-18
期刊：
Nature reviews. Neuroscience
影响因子：
--
作者：
Buzsáki G;Anastassiou CA;Koch C
通讯作者：
Koch C
影响因子：
6.1
作者：
Berdondini, Luca;Imfeld, Kilian;Martinoia, Sergio
通讯作者：
Martinoia, Sergio
影响因子：
2.9
作者：
Baker, Robert E.;Corner, Michael A.;van Pelt, Jaap
通讯作者：
van Pelt, Jaap
DOI：
10.1073/pnas.0736909100
发表时间：
2003-04-15
影响因子：
11.1
作者：
Buonomano, DV
通讯作者：
Buonomano, DV
DOI：
10.1016/0165-3806(93)90108-m
发表时间：
1993-01-15
期刊：
DEVELOPMENTAL BRAIN RESEARCH
影响因子：
--
作者：
BUCHS, PA;STOPPINI, L;MULLER, D
通讯作者：
MULLER, D