High-fidelity multimode fibre-based endoscopy for deep brain in vivo imaging

Progress in neuroscience constantly relies on the development of new techniques to investigate the complex dynamics of neuronal networks. An ongoing challenge is to achieve minimally-invasive and high-resolution observations of neuronal activity in vivo inside deep brain areas. A perspective strategy is to utilise holographic control of light propagation in complex media, which allows converting a hair-thin multimode optical fibre into an ultra-narrow imaging tool. Compared to current endoscopes based on GRIN lenses or fibre bundles, this concept offers a footprint reduction exceeding an order of magnitude, together with a significant enhancement in resolution. We designed a compact and high-speed system for fluorescent imaging at the tip of a fibre, achieving micron-scale resolution across a 50 um field of view, and yielding 7-kilopixel images at a rate of 3.5 frames/s. Furthermore, we demonstrate in vivo observations of cell bodies and processes of inhibitory neurons within deep layers of the visual cortex and hippocampus of anesthetised mice. This study forms the basis for several perspective techniques of modern microscopy to be delivered deep inside the tissue of living animal models while causing minimal impact on its structural and functional properties.Comment: 10 pages, 2 figures, Supplementary movie: https://drive.google.com/file/d/1Fm0G3TAIC49LVX6FaEiAtlefkWx1T2a5/vie

Hybrid and fast: A novel in silico approach with reduced computational cost to predict failures of in vivo needle-based implantations

Penetrating neural interfaces, connecting peripheral nerves to robotic devices (e.g., hand prostheses), could be inserted through tungsten needles, which are able to minimize damages and scarring due to the puncture wounds. Unfortunately, puncturing needles may fail independently on the material fracture toughness. In addition, independently on internal biotic causes, needles’ performances may decrease during in vivo trials. External biotic causes seems to be related to these effects, even if the exact genesis of phenomena, decreasing the in vivo reliability, is still partially unknown. Therefore, this work provides a hybrid computational approach, simultaneously using theoretical relationships and novel fast silico models of nerves. This framework is able to lower computational times needed to predict in vivo performances by using in vitro reliability and local differences between in vivo and in vitro mechanical response of nerves