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

用于跨分辨率尺度脑网络综合多模态分析的计算平台

• 批准号: RGPIN-2021-03728
• 负责人: Goubran, Maged
• 金额: $ 1.75万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=760716
• 关键词: Computational; platform; integrated; multimodal; analysis

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.

所有的行为和想法都是由大脑中复杂的网络实现的。绘制这些网络的连接对于理解大脑如何处理信息以及这些网络如何被破坏并导致疾病至关重要。这种困难的映射需要计算工具来自动从多种成像模式（如显微镜数据和大脑扫描）中提取信息，并将微观细胞过程与整个大脑的宏观连通性相结合。然而，目前的分析方法受到几个限制，包括不产生最佳结果，依赖于昂贵的人工干预或计算昂贵。为了抓住这些机会，我们的总体目标是建立一个研究计划，用于大脑连接的自动映射和这种多模态数据的综合分析。我们将开发新的人工智能（AI）算法，以在多个测量级别上对这种大脑连接数据进行精确分析和建模。使用小鼠模型，我们将可视化，提取和建模关键脑区和整个大脑中的细胞到细胞的投射和连接。建立拟议的基于人工智能的平台将解决该领域对全脑水平的自动化和准确映射大脑网络的迫切需求。该研究计划将开发和优化强大的AI模型，用于分析和建模大型3D显微镜和大脑成像数据。我们的计算模型有可能增强我们在细胞水平上对大脑连接的理解。重要的是，这些工具可以实现许多其他神经科学，成像或网络科学应用，从而推进生物信息学研究的广泛领域。