Network Reconstruction as a Novel High-Level Marker of Functional Neuronal Viability.

Neuronal viability is essential for the maintenance of neuronal networks. Already slight noxious modifications, for example, the selective interruption of interneurons' function, which enhances the excitatory drive inside a network, may already be harmful for the overall network. To monitor neuronal viability on the network level, we implemented a network reconstruction approach that infers the effective connectivity of cultured neurons from live-cell fluorescence microscopy recordings. Neuronal spiking is reported by the fast calcium sensor Fluo8-AM using a relatively high sampling rate (27.33 Hz) to detect fast events such as action potential-evoked rises in intracellular calcium. Spiking records are then subjected to a machine learning-based set of algorithms that reconstruct the neuronal network. Then, the topology of the neuronal network can be analyzed via various parameters, such as the modularity, the centrality, or the characteristic path length. In summary, these parameters describe the network and how it is influenced by experimental modulations, for example, hypoxia, nutrient deficiency, co-culture models, or application of drugs and other factors.