Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, Genova, Italy.
ScreenNeuroPharm, Sanremo, Italy.
PLoS Comput Biol. 2023 Feb 13;19(2):e1010825. doi: 10.1371/journal.pcbi.1010825. eCollection 2023 Feb.
Nowadays, in vitro three-dimensional (3D) neuronal networks are becoming a consolidated experimental model to overcome most of the intrinsic limitations of bi-dimensional (2D) assemblies. In the 3D environment, experimental evidence revealed a wider repertoire of activity patterns, characterized by a modulation of the bursting features, than the one observed in 2D cultures. However, it is not totally clear and understood what pushes the neuronal networks towards different dynamical regimes. One possible explanation could be the underlying connectivity, which could involve a larger number of neurons in a 3D rather than a 2D space and could organize following well-defined topological schemes. Driven by experimental findings, achieved by recording 3D cortical networks organized in multi-layered structures coupled to Micro-Electrode Arrays (MEAs), in the present work we developed a large-scale computational network model made up of leaky integrate-and-fire (LIF) neurons to investigate possible structural configurations able to sustain the emerging patterns of electrophysiological activity. In particular, we investigated the role of the number of layers defining a 3D assembly and the spatial distribution of the connections within and among the layers. These configurations give rise to different patterns of activity that could be compared to the ones emerging from real in vitro 3D neuronal populations. Our results suggest that the introduction of three-dimensionality induced a global reduction in both firing and bursting rates with respect to 2D models. In addition, we found that there is a minimum number of layers necessary to obtain a change in the dynamics of the network. However, the effects produced by a 3D organization of the cells is somewhat mitigated if a scale-free connectivity is implemented in either one or all the layers of the network. Finally, the best matching of the experimental data is achieved supposing a 3D connectivity organized in structured bundles of links located in different areas of the 2D network.
如今,体外三维(3D)神经元网络正成为一种成熟的实验模型,可克服二维(2D)组合的大多数内在局限性。在 3D 环境中,实验证据表明存在更广泛的活动模式谱,其特征是爆发特征的调制,这与在 2D 培养物中观察到的特征不同。然而,尚不完全清楚和理解是什么促使神经元网络走向不同的动力学状态。一种可能的解释可能是潜在的连接性,它可以在 3D 空间中涉及比 2D 空间更多的神经元,并且可以按照明确的拓扑方案进行组织。受通过记录由多层结构组成并与微电极阵列(MEA)耦合的 3D 皮层网络获得的实验发现的驱动,在本工作中,我们开发了一个由漏电流积分和放电(LIF)神经元组成的大规模计算网络模型,以研究可能的结构配置能够维持新兴的电生理活动模式。特别是,我们研究了定义 3D 组件的层数和层内和层间连接的空间分布的作用。这些配置产生了不同的活动模式,可以与来自真实体外 3D 神经元群体的模式进行比较。我们的结果表明,与 2D 模型相比,引入 3 维性会导致整体放电和爆发率降低。此外,我们发现存在获得网络动力学变化所需的最小层数。但是,如果在网络的一个或所有层中实现无标度连接,则可以减轻细胞 3D 组织产生的影响。最后,通过假设组织在 2D 网络的不同区域中存在结构化束的 3D 连接,实现了与实验数据的最佳匹配。