Computational platform for integrated multimodal analysis of brain networks across resolution scales

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All behaviour and thoughts are made possible by complex networks within the brain. Mapping the connections of these networks is essential for understanding how the brain processes information and how these networks are disrupted and contribute to disease. This difficult mapping requires computational tools to automatically distill information from multiple imaging modalities such as microscopy data and brain scans, and integrate microscopic cellular processes with macroscopic connectivity across the whole brain. Current analysis approaches, however, suffer from several limitations including not producing optimal results, relying on costly human intervention or being computationally expensive. To address these opportunities, our overall goal is to build a research program for automated mapping of brain connectivity and integrated analysis of this multimodal data. We will develop novel artificial intelligence (AI) algorithms to perform precise analysis and modelling of this brain connectivity data at multiple levels of measurement. Using mouse models, we will visualize, extract and model the cell-to-cell projections and connections in key brain regions and across the whole brain. Establishing the proposed AI-based platform will address a pressing need in the field for automated and accurate mapping of brain networks at the whole-brain level. This research program will develop and optimize robust AI models designed for the analysis and modeling of large 3D microscopy and brain imaging data. Our computational models have the potential to enhance our understanding of brain connectivity at the cellular level. Importantly, these tools can enable many other neuroscience, imaging or network science applications, thereby advancing the broad field of bioinformatics research.