Reverse Engineering of Feedforward Cortical-Hippocampal Neural Networks Relevant for Preclinical Disease Modelling

Abstract

Engineered biological neural networks are indispensable tools for investigating neural function in both healthy and diseased states from the subcellular to the network level. Neurons in vitro self-organize over time into networks of increasing structural and functional complexity, thus maintaining emergent dynamics of neurons in the brain. While in vitro neural network model systems have advanced significantly over the past decade, there is still a need for models able to recapitulate topological and functional organization of brain networks. Especially relevant in this context are interfaces which can support the establishment of multinodal interconnected networks of different neural populations, which at the same time enable control of the direction of connectivity between the nodes. An added required feature of such interfaces is compatibility with electrophysiological techniques and optical imaging tools to facilitate studies of neural network structure-function dynamics. In this study, we applied a custom-designed microfluidic device with Tesla-valve inspired microchannels for structuring multinodal neural networks with controllable feedforward connectivity between rat primary cortical and hippocampal neurons. By interfacing these devices with nanoporous microelectrode arrays, we demonstrate how both spontaneously evoked and stimulation induced activity propagates, as intended, in a feedforward pattern between the different interconnected neural nodes. Moreover, we show that these multinodal networks exhibit functional dynamics suggestive of capacity for both segregated and integrated activity. To advocate the broader applicability of this model system, we also provide proof of concept of long-term culturing of subregion- and layer specific neurons extracted from the entorhinal cortex and hippocampus of adult Alzheimers-model mice and rats. We show that these neurons re-form structural connections and develop spontaneous spiking activity after 15 days in vitro. Our results thus highlight the suitability and potential of our approach for reverse engineering of biologically and anatomically relevant multinodal neural networks supporting the study of dynamic structure-function relationships in both healthy and pathological conditions.

Functional Complexity of Engineered Neural Networks Self-Organized on Novel 3D Interfaces

“…Engineered neural networks can be designed to recapitulate key hallmarks of this structural and functional complexity in vitro (3)(4)(5). Such model systems facilitate studies of neural network function and dysfunction at the micro-and mesoscale level in a highly controllable microenvironment (6,7). Typically, these networks are established on planar interfaces.…”