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A Data Structure for Real-Time Aggregation Queries of Big Brain Networks.
Neuroinformatics ( IF 3 ) Pub Date : 2019-06-25 , DOI: 10.1007/s12021-019-09428-9
Florian Johann Ganglberger 1 , Joanna Kaczanowska 2 , Wulf Haubensak 2 , Katja Bühler 1
Affiliation  

Recent 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 realized Aggregation 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 typically exceeds the memory size of current consumer level PCs. This allows experts to embed and explore their own experimental data in the framework of public data resources without the need for their own large-scale infrastructure. Our data structure outperforms state-of-the-art graph engines in retrieving connectivity of arbitrary user defined local brain areas. We demonstrate the feasibility of our approach by analyzing fear-related functional neuroanatomy in mice. Further, we show its versatility by comparing multimodal brain networks linked to autism. Importantly, we achieve cross-species congruence in retrieving human psychiatric traits networks, which facilitates the selection of neural substrates to be further studied in mouse models.

中文翻译:

大大脑网络实时聚集查询的数据结构。

神经成像技术的最新进展使大型的大脑计划和联合会能够创建大量的大脑数据资源,研究人员可以针对自己的项目进行挖掘。探索基因,大脑电路和行为之间的关系是神经科学研究的关键要素之一。这需要融合不同规模的空间连通性数据,例如全脑相关基因表达,结构和功能连通性。随着分辨率的不断提高,它们的大小和复杂性往往会比过去的现有技术高几个数量级。由于当前神经科学中的分析工作流程涉及耗时的手动数据聚合,因此必须采用有效的技术来处理大型连接数据。我们提出了一种新颖的数据结构,该结构使得能够以数十亿条边进行异构神经生物学连接性数据的交互式探索。基于此数据结构,我们意识到聚合查询,即来自大脑区域,大脑区域之间或大脑区域之间的聚合连接性,使专家可以比较不同规模或层次结构的解剖图谱层次上的多峰网络。根据体积基因表达和连接性数据按需执行,它们允许基于空间上下文实时地对网络进行交互式解剖。数据结构经过优化,以便可以直接从硬盘访问,因为大型网络的连接通常超过了当前消费者级别PC的内存大小。这使专家可以在公共数据资源框架内嵌入和探索自己的实验数据,而无需他们自己的大规模基础架构。在检索任意用户定义的本地大脑区域的连接性方面,我们的数据结构优于最新的图形引擎。我们通过分析与小鼠恐惧相关的功能性神经解剖学来证明我们的方法的可行性。此外,我们通过比较与自闭症相关的多峰脑网络来展示其多功能性。重要的是,我们在检索人类精神特征网络时实现了跨物种一致性,这有助于选择要在小鼠模型中进一步研究的神经底物。
更新日期:2019-06-25
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