Department of Clinical Neurophysiology, University of Twente, P.O. Box 217, 7500 AE, Enschede, The Netherlands.
Laney Graduate School, Emory University, Atlanta, GA, 30307, USA.
Sci Rep. 2022 Jun 10;12(1):9656. doi: 10.1038/s41598-022-13674-4.
Tools to estimate brain connectivity offer the potential to enhance our understanding of brain functioning. The behavior of neuronal networks, including functional connectivity and induced connectivity changes by external stimuli, can be studied using models of cultured neurons. Cultured neurons tend to be active in groups, and pairs of neurons are said to be functionally connected when their firing patterns show significant synchronicity. Methods to infer functional connections are often based on pair-wise cross-correlation between activity patterns of (small groups of) neurons. However, these methods are not very sensitive to detect inhibitory connections, and they were not designed for use during stimulation. Maximum Entropy (MaxEnt) models may provide a conceptually different method to infer functional connectivity. They have the potential benefit to estimate functional connectivity during stimulation, and to infer excitatory as well as inhibitory connections. MaxEnt models do not involve pairwise comparison, but aim to capture probability distributions of sets of neurons that are synchronously active in discrete time bins. We used electrophysiological recordings from in vitro neuronal cultures on micro electrode arrays to investigate the ability of MaxEnt models to infer functional connectivity. Connectivity estimates provided by MaxEnt models correlated well with those obtained by conditional firing probabilities (CFP), an established cross-correlation based method. In addition, stimulus-induced connectivity changes were detected by MaxEnt models, and were of the same magnitude as those detected by CFP. Thus, MaxEnt models provide a potentially powerful new tool to study functional connectivity in neuronal networks.
用于估计大脑连接的工具具有增强我们对大脑功能理解的潜力。可以使用培养神经元的模型来研究神经元网络的行为，包括功能连接和外部刺激引起的连接变化。培养的神经元往往是成群活跃的，当它们的发射模式显示出显著的同步性时，就说它们是功能上连接的。推断功能连接的方法通常基于（小群）神经元活动模式之间的成对互相关。然而，这些方法对于检测抑制性连接不是很敏感，并且它们不是为刺激期间使用而设计的。最大熵 (MaxEnt) 模型可能提供一种推断功能连接的概念上不同的方法。它们具有在刺激期间估计功能连接的潜在好处，并且可以推断兴奋性和抑制性连接。MaxEnt 模型不涉及成对比较，而是旨在捕捉在离散时间箱中同步活动的神经元集合的概率分布。我们使用微电极阵列上的体外神经元培养物的电生理记录来研究 MaxEnt 模型推断功能连接的能力。MaxEnt 模型提供的连接估计与条件发射概率 (CFP) 得出的估计高度相关，CFP 是一种基于交叉相关的既定方法。此外，MaxEnt 模型检测到刺激引起的连接变化，其幅度与 CFP 检测到的变化相同。因此，MaxEnt 模型为研究神经元网络中的功能连接提供了一种潜在的强大新工具。
