CRII: RI: Using Large-Scale Neuroanatomy Datasets to Quantify the Mesoscale Architecture of the Brain

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Methods for revealing the global connections of the brain typically start by tracing a small number of neurons at a time. It is through performing many experiments, in different brain areas, and across many brains, that information can be aggregated and consolidated to produce detailed maps of the brain's global networks and architecture. The aim of this project is to develop new computational approaches for modeling the connectivity of the mouse brain, in order to reveal principles of wiring and information routing. The project will leverage whole-brain imaging datasets from the Allen Institute for Brain Science that each provide a small piece of the puzzle but when combined, can yield a picture of whole-brain connectivity. The outcomes of this research will be new maps of the global connectivity of the mouse brain, and a framework for studying the impact of disease and aging on whole-brain networks.This project will develop a novel framework for analyzing whole-brain connectomics datasets to model high-level (mesoscopic) principles of wiring and architecture. To do this, tools from matrix factorization will be used to decompose large datasets from many brains into a collection of learned neural pathways or "parts" that, when combined, describe large volumes of data as succinctly as possible. To address the size of the datasets, the use of subsampling-based approaches and randomized methods will be explored for massive-scale machine learning applications. Through intelligent and adaptive subsampling of the data, in combination with online methods for factorization, methods will be developed to process and learn from the entire Allen Institute Mouse Connectivity Atlas at less than 10-micron resolution. The outcomes of this project will be new tools for large-scale matrix factorization, models of the whole brain connectome, and discovery of wiring principles that could be useful in the development of next generation architectures for machine intelligence.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.