Research data for paper Functional mapping of the molluscan brain guided by synchrotron X-ray tomography

Data for paper published in Proceedings of the National Academy of SciencesFunctional mapping of the molluscan brain guided by synchrotron X-ray tomography<b>Abstract </b>Molluscan brains are comprised of morphologically consistent and functionally interrogable neurons offering rich opportunities for understanding how neural circuits drive behaviour. Nonetheless, detailed component-level CNS maps are completely lacking, total neuron numbers are unknown, and organizational principles remain poorly defined, limiting a full and systematic characterization of circuit operation. Here we establish an accessible, generalizable approach, harnessing synchrotron X-ray tomography, to rapidly determine the three-dimensional structure of the multi-millimeter-scale CNS of <i>Lymnaea</i>. Focusing on the feeding ganglia, we generate the first full neuron-level reconstruction, revealing key design principles and revising cell count estimates upwards threefold. Our atlas uncovers the superficial but also non-superficial ganglionic architecture, reveals the cell organization in normally hidden regions - ganglionic “dark-sides” - and details features of single-neuron morphology, together guiding targeted follow-up functional investigation based on intracellular recordings. Using this approach, we identify three pivotal, to date unreported, neuron classes: a command-like food-signalling cell type, a feeding central pattern-generator interneuron, and a unique behavior-specific motoneuron, together significantly advancing understanding of the function of this classical control circuit. Combining our morphological and electrophysiological data we also establish a first functional CNS atlas in <i>Lymnaea</i> as a shared and scalable resource for the research community. Our approach enables the rapid construction of cell atlases in large-scale nervous systems, with key relevance to functional circuit interrogation in a diverse range of model organisms.<b>Contents:</b>Excel file with multiple sheets, each related to a different dataset within the paper.readme.txt file containing Description information