A Data Structure for Real-Time Aggregation Queries of Big Brain Networks

AbstractRecent advances in neuro-imaging allowed big brain-initiatives and consortia to create vast resources of brain data that can be mined by researchers for their individual projects. Exploring the relationship between genes, brain circuitry, and behavior is one of the key elements of neuroscience research. This requires fusion of spatial connectivity data at varying scales, such as whole brain correlated gene expression, structural and functional connectivity. With ever-increasing resolution, these tend to exceed the past state-of-the art in size and complexity by several orders of magnitude. Since current analytical workflows in neuroscience involve time-consuming manual data-aggregation, incorporating efficient techniques for handling big connectivity data is a necessity. We propose a novel data structure enabling the interactive exploration of heterogeneous neurobiological connectivity data with billions of edges. Based on this data structure we realizedAggregation Queries, i.e. the aggregated connectivity from, to or between brain areas allows experts to compare the multimodal networks residing at different scales, or levels of hierarchically organized anatomical atlases. Executed on-demand on volumetric gene expression and connectivity data, they allow an interactive dissection of networks in real-time and based on their spatial context. The data structure is optimized in order to be accessible directly from the hard disk, since connectivity of large-scale networks ...
Source: Neuroinformatics - Category: Neuroscience Source Type: research