Project 1 - A comprehensive atlas of the larval zebrafish brain - Abstract Anatomical information about the brain, such as the identity of brain regions, the molecular and morphological makeup of individual neurons and their connectivity arrangements, are all critical information for formulating hypotheses of neural circuit function. While much anatomical data concerning the zebrafish brain are being generated by the community, the field lacks a central repository which is capable of ingesting, integrating and quantitatively describing these data. To meet this need, we propose to generate a multilayered, multimodal, and multiscale atlas of the larval zebrafish brain. This project will provide the necessary infrastructure to import data from all the team members as well as the international community, integrate these data into a common reference brain, and make it publicly available and easily accessible online. This Atlas will exist at 3 levels of resolution: 1) The macro-scale, where we will define and describe the known anatomical regions of the brain, and provide new tools for regional annotation and 3D visualization. 2) The micro-scale will aggregate diverse datasets describing individual neurons in the brain. These will include: molecular makeup (through transgenic and antigenic stains - neurotransmitter, neuropeptide, neuromodulator, gene expression, etc), functional properties (through calcium and perhaps voltage imaging), morphology (through single cell imaging/tracing, and EM reconstructions) and connectivity (through functional connectivity, viral tracing, patch clamp recordings and nano-scale EM data). Finally, 3) The nano-scale, which will aggregate whole-brain serial EM volumes from multiple individual larvae. It will provide the infrastructure for collaborative nano-scale annotations, such as identified synaptic connections and high-resolution morphology. Collectively, this Atlas will be a foundational resource for zebrafish neuroscientists, and will be an invaluable tool for creating and constraining biologically plausible neural circuit models, including the Multiscale Virtual Fish.
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