The Penn State Computational Biomechanics Group, The Pennsylvania State University, University Park, PA, United States of America.
J Neural Eng. 2018 Oct;15(5):056008. doi: 10.1088/1741-2552/aac96d. Epub 2018 Jun 1.
Micro-tissue engineered neural networks (micro-TENNs) are anatomically-inspired constructs designed to structurally and functionally emulate white matter pathways in the brain. These 3D neural networks feature long axonal tracts spanning discrete neuronal populations contained within a tubular hydrogel, and are being developed to reconstruct damaged axonal pathways in the brain as well as to serve as physiologically-relevant in vitro experimental platforms. The goal of the current study was to characterize the functional properties of these neuronal and axonal networks.
Bidirectional micro-TENNs were transduced to express genetically-encoded calcium indicators, and spontaneous fluorescence activity was recorded using real-time microscopy at 20 Hz from specific regions-of-interest in the neuronal populations. Network activity patterns and functional connectivity across the axonal tracts were then assessed using various techniques from statistics and information theory including Pearson cross-correlation, phase synchronization matrices, power spectral analysis, directed transfer function, and transfer entropy.
Pearson cross-correlation, phase synchronization matrices, and power spectral analysis revealed high values of correlation and synchronicity between the spatially segregated neuronal clusters connected by axonal tracts. Specifically, phase synchronization revealed high synchronicity of >0.8 between micro-TENN regions of interest. Normalized directed transfer function and transfer entropy matrices suggested robust information flow between the neuronal populations. Time varying power spectrum analysis revealed the strength of information propagation at various frequencies. Signal power strength was visible at elevated peak levels for dominant delta (1-4 Hz) and theta (4-8 Hz) frequency bands and progressively weakened at higher frequencies. These signal power strength results closely matched normalized directed transfer function analysis where near synchronous information flow was detected between frequencies of 2-5 Hz.
To our knowledge, this is the first report using directed transfer function and transfer entropy methods based on fluorescent calcium activity to estimate functional connectivity of distinct neuronal populations via long-projecting, 3D axonal tracts in vitro. These functional data will further improve the design and optimization of implantable neural networks that could ultimately be deployed to reconstruct the nervous system to treat neurological disease and injury.
微组织工程神经网络(micro-TENNs)是受解剖结构启发而设计的,旨在在结构和功能上模拟大脑中的白质通路。这些 3D 神经网络具有跨越包含在管状水凝胶中的离散神经元群体的长轴突束,并正在开发中,以重建大脑中受损的轴突通路,并作为具有生理相关性的体外实验平台。本研究的目的是表征这些神经元和轴突网络的功能特性。
双向 micro-TENNs 被转导以表达基因编码的钙指示剂,并使用实时显微镜以 20 Hz 的频率从神经元群体中的特定感兴趣区域记录自发荧光活性。然后使用来自统计学和信息论的各种技术,包括 Pearson 互相关、相位同步矩阵、功率谱分析、有向传递函数和传递熵,评估轴突束中跨轴突的网络活动模式和功能连接。
Pearson 互相关、相位同步矩阵和功率谱分析显示,通过轴突束连接的空间分离神经元簇之间具有高相关性和同步性。具体而言,相位同步显示 micro-TENN 感兴趣区域之间的同步性>0.8。归一化有向传递函数和传递熵矩阵表明神经元群体之间存在稳健的信息流。时变功率谱分析揭示了各种频率下信息传播的强度。信号功率强度在主导的 delta(1-4 Hz)和 theta(4-8 Hz)频段的高峰水平可见,并在较高频率下逐渐减弱。这些信号功率强度结果与归一化有向传递函数分析非常匹配,在 2-5 Hz 的频率之间检测到近乎同步的信息流。
据我们所知,这是第一个使用基于荧光钙活性的有向传递函数和传递熵方法来估计体外通过长投射 3D 轴突束的不同神经元群体功能连接的报告。这些功能数据将进一步改进可植入神经网络的设计和优化,最终可用于重建神经系统以治疗神经疾病和损伤。