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Characterization of Neurodevelopmental Disease Trajectories using Richly Annotated Sequences of Graphs (RICHGRAPH)

使用丰富注释的图序列 (RICHGRAPH) 表征神经发育疾病轨迹

基本信息

批准号：
290781790
负责人：
Professor Dr. Klaus Maier-Hein
金额：
--
依托单位：
Klinik für Psychiatrie und Psychotherapie
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2016
资助国家：
德国
起止时间：
2015-12-31 至 2020-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/290781790?language=en
关键词：
Characterization Neurodevelopmental Disease Trajectories using

项目摘要

Mental disorders are the main cause of suicide, disability, and early retirement in Europe. While recent developments in magnetic resonance imaging have opened unprecedented windows into the developing brain, the full potential of computational methods to analyse the longitudinal and multi-modal imaging information at hand has not yet been exploited. After psychiatric neuroimaging research has long been focusing on dedicated regions in the brain (local analysis), the emerging field of connectomics has enabled a characterization of topological properties (global analysis) by establishing graphs as a representation of the brain. Current techniques, however, require the available multi-modal information to be reduced to a simple set of edges that capture the relationship between different brain regions in a single weight and thus fail to exploit the manifold MRI-derived quantitative parameters as potentially complementary sources of information. They are also unable to model network dynamics. Inspired by major progress in the field of social network analysis, this project will develop the next generation of connectomics techniques based on rich graphs that enable holistic processing of heterogeneous data from multiple sources over time. Advanced machine learning techniques applied to sequences of rich graphs will allow analyses and prediction of neurodevelopmental trajectories based on both local tissue characteristics and global network features. Comprehensive validation studies with large cohorts of patients and controls that include longitudinal imaging, genetic, environmental, and behavioral data will be performed with the long-term goal of (1) establishing novel multi-modal and longitudinal imaging biomarkers that represent pathological changes before the appearance of clinical symptoms, (2) linking brain imaging traits to gene variants and (3) advancing our understanding of the underlying biological processes to pave the way for development of new treatments.

精神疾病是欧洲自杀、残疾和提前退休的主要原因。虽然磁共振成像的最新发展为发育中的大脑打开了前所未有的窗口，但用于分析手头的纵向和多模态成像信息的计算方法的全部潜力尚未被利用。在精神病学神经影像学研究长期以来一直专注于大脑中的专用区域（局部分析）之后，新兴的连接组学领域通过建立图形作为大脑的表示来实现拓扑特性的表征（全局分析）。然而，目前的技术，需要将可用的多模态信息减少到一个简单的边缘集，捕捉不同的大脑区域之间的关系，在一个单一的权重，从而无法利用的流形MRI衍生的定量参数作为潜在的互补信息源。他们也无法模拟网络动态。受社交网络分析领域重大进展的启发，该项目将开发基于丰富图形的下一代连接组学技术，这些技术可以随着时间的推移对来自多个来源的异构数据进行整体处理。应用于丰富图形序列的先进机器学习技术将允许基于局部组织特征和全局网络特征分析和预测神经发育轨迹。将进行包括纵向成像、遗传、环境和行为数据的大型患者和对照队列的综合验证研究，长期目标是（1）建立代表临床症状出现前病理变化的新型多模态和纵向成像生物标志物，（2）将大脑成像特征与基因变异联系起来，（3）推进我们对潜在生物过程的理解，为开发新的治疗方法铺平道路。