A microelectrode array (MEA) integrated with clustering structures for investigating in vitro neurodynamics in confined interconnected sub-populations of neurons

Understanding how the information is coded in large neuronal networks is one of the major challenges for neuroscience. A possible approach to investigate the information processing capabilities of the neuronal ensembles is given by the use of dissociated neuronal cultures coupled to microelectrode arrays (MEAs). <br><br> Here, we describe a new strategy, based on MEAs, for studying in vitro neuronal network dynamics in interconnected sub-populations of cortical neurons. The rationale is to sub-divide the neuronal network into communicating clusters while preserving a high degree of functional connectivity within each confined sub-population, i.e. to achieve a compromise between a completely random large neuronal population and a patterned network, such as currently used with conventional MEAs. <br><br> To this end, we have realized and functionally characterized a Pt microelectrode array with an integrated EPON SU-8 clustering structure, allowing to confine five relatively large yet interconnected spontaneously developing neuronal networks (i.e. thousands of cells). The clustering structure consists of five chambers of 3 mm in diameter interconnected via 800 μm long and 300 μm wide microchannels and is integrated on the MEA of 60 thin-film Pt electrodes of 30 μm diameter. Tests of the Pt microelectrodes’ stability under stimulation showed a stable behavior up to 35,000 voltage stimuli and the biocompatibility was assessed with the cultures of dissociated rat's cortical neurons achieving cultures’ viability up to 60 days in vitro. <br><br> Compared to conventional MEAs, the monitoring of spontaneous and evoked activity and the computation of the Post-Stimulus Time Histogram (PSTH) within the clusters clearly demonstrates: (i) the capability to selectively activate (through poly-synaptic pathways) specific network regions and (ii) the confinement of the network dynamics mainly in the highly connected sub-networks.