Gandolfo, M. | Search bioRxiv

bioRxiv2022

Design, implementation, and functional validation of a new generation of microneedle 3D high-density CMOS multi-electrode array for brain tissue and spheroids

In the last decades, planar multi-electrode arrays (MEAs) have been widely used to record activity from in vitro neuronal cell cultures and tissue slices. Though successful, this technique bears some limitations, particularly relevant when applied to three-dimensional (3D) tissue, such as brain slices, spheroids or organoids. For example, planar MEAs signals are informative on just one side of a 3D-organized structure. This limits the interpretation of the results in terms of network functions in a complex structured and hyperconnected brain tissue. Moreover, the side in contact with the MEAs often shows lower oxygenation rates and related vitality issues. To overcome these problems, we empowered a CMOS high-density multi-electrode array (HD-MEA) with thousands of microneedles (needles) of 65-90 m height, able to penetrate and record in-tissue signals, providing for the first time a 3D HD-MEA chip. We propose a CMOS-compatible fabrication process to produce arrays of needles of different widths mounted on large pedestals to create microchannels underneath the tissue. By using cerebellar and cortico-hippocampal slices as a model, we show that the needles efficiently penetrate the 3D tissue while the microchannels allow the flowing of maintenance solutions to increase tissue vitality in the recording sites. These improvements are reflected by the increase in electrodes sensing capabilities, the number of sampled neuronal units (compared to matched planar technology), and the efficiency of compound effects. Importantly, each electrode can also be used to stimulate the tissue with optimal efficiency due to the 3D structure. Furthermore, we demonstrate how the 3D HD-MEA can efficiently penetrate and get outstanding signals from in vitro 3D cellular models as brain spheroids. In conclusion, we describe a new recording device characterized by the highest spatio-temporal resolution reported for a 3D MEA and significant improvements in the quality of recordings, with a high signal-to-noise ratio and improved tissue vitality. The applications of this game-changing technique are countless, opening unprecedented possibilities in the neuroscience field and beyond.

neuroscience↗

bioRxiv2021

Boundary extension is constrained by naturalistic image properties

Boundary extension (BE) is a classic memory illusion in which observers remember more of a scene than was presented. According to predictive processing accounts, BE reflects the integration of visual input and expectations of what is beyond a scenes boundaries. According to normalization accounts, BE rather reflects one end of a normalization process towards a scenes typically-experienced viewing distance, such that close-up views give BE but distant views give boundary contraction. Here across four experiments, we show that BE strongly depends on depth-of-field (DOF), as determined by the aperture settings on a camera. Photographs with naturalistic DOF led to larger BE than photographs with unnaturalistic DOF, even when showing distant views. We propose that BE reflects a predictive mechanism with adaptive value that is strongest for naturalistic views of scenes. The current findings indicate that DOF is an important variable to consider in the study of scene perception and memory. Statement of RelevanceIn daily life, we experience a rich and continuous visual world in spite of the capacity limits of the visual system. We may compensate for such limits with our memory, by filling-in the visual input with anticipatory representations of upcoming views. The boundary extension illusion (BE) provides a tool to investigate this phenomenon. For example, not all images equally lead to BE. In this set of studies, we show that memory extrapolation beyond scene boundaries is strongest for images resembling human visual experience, showing depth-of-field in the range of human vision. Based on these findings, we propose that predicting upcoming views is conditional to a scene being perceived as naturalistic. More generally, the strong reliance of a cognitive effect, such as BE, on naturalistic image properties indicates that it is imperative to use image sets that are ecologically-representative when studying the cognitive, computational, and neural mechanisms of scene processing.

neuroscience↗