Maibach, M. | bioRxiv Science

bioRxiv2024

Three-photon in vivo imaging of neurons and glia in the medial prefrontal cortex with sub-cellular resolution

The medial prefrontal cortex (mPFC) is important for higher cognitive functions, including working memory, decision making, and emotional control. In vivo recordings of neuronal activity in the mPFC have been achieved via invasive electrical and optical approaches. Here we apply low invasive three-photon in vivo imaging in the mPFC of the mouse at unprecedented depth. Specifically, we measure neuronal and astrocytic Ca2+-transient parameters in awake head-fixed mice up to a depth of 1700 {micro}m. Furthermore, we longitudinally record dendritic spine density (0.41 {+/-}0.07 {micro}m-1) deeper than 1 mm for a week. Using 1650 nm wavelength to excite red fluorescent microglia, we quantify their processes motility (58.9 {+/-}2% turnover rate) at previously unreachable depths (1100 {micro}m). We establish three-photon imaging of the mPFC enabling neuronal and glial recordings with subcellular resolution that will pave the way for novel discoveries in this brain region.

neuroscience↗

bioRxiv2024

A deep-learning strategy to identify cell types across species from high-density extracellular recordings

High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but dont reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals, revealing the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetic activation and pharmacology using the cerebellum as a testbed to generate a curated ground-truth library of electrophysiological properties for Purkinje cells, molecular layer interneurons, Golgi cells, and mossy fibers. We train a semi-supervised deep-learning classifier that predicts cell types with greater than 95% accuracy based on waveform, discharge statistics, and layer of the recorded neuron. The classifiers predictions agree with expert classification on recordings using different probes, in different laboratories, from functionally distinct cerebellar regions, and across animal species. Our classifier extends the power of modern dynamical systems analyses by revealing the unique contributions of simultaneously-recorded cell types during behavior.

neuroscience↗