Inter-modal Coupling Image Analytics

项目总结 我们建议开发统计方法来分析局部协方差结构 多模式生物医学成像研究中的测量。尽管多模式成像研究和 虽然从每种模式中可获得的衍生测量方法激增，但很少有工作探索它们之间的关系 在不同的模式之间。值得注意的是，在青春期大脑的背景下，大脑结构和功能发生了戏剧性的演变 但典型的分析通常会单独评估每种成像表型。我们建议开发 用于测量不同成像模式提供的脑成像表型之间的关系的工具。 主体级别的协方差结构将允许识别度量之间的局部关系， 通过加权回归和基于奇异值分解的技术。这些技术将是 扩展到可容纳任何数量的成像方式，并纳入重复措施。我们将使用 由可重复性和可靠性联盟(CORR)提供的用于验证的大量测试-重新测试数据， 并确定特别可靠的耦合措施。丰富的多模式成像数据和关联 来自费城神经发育队列(PNC)的临床表型将使我们能够描绘出 通道耦合在正常的大脑发育中进化，也确定了这种关系是如何在 与精神病相关的谱系症状。该建议建立在两国之间已建立的合作基础上 PIS利用他们在成像统计、发育神经成像、 精神病理学，以及对PNC数据集的熟悉程度。建成后，该项目将提供 为神经科学界提供了强大的新工具，以及关于大脑发育和 早期精神病--谱系症状。

PROJECT SUMMARY We propose to develop statistical methods for the analysis of the local covariance structure between measurements in multimodal biomedical imaging studies. Despite the rise of multi-modal imaging studies and the proliferation of derived measures available from each modality, very little work has explored relationships between modalities. Notably, brain structure and function evolve dramatically in the context of adolescent brain development, but typical analyses usually evaluate each imaging phenotype separately. We propose to develop tools for measuring relationships between brain imaging phenotypes provided by disparate imaging modalities. Covariance structures at the subject level will allow identification of local relationships between measures, through weighted regressions and singular value decomposition-based techniques. These techniques will be extended to accommodate any number of imaging modalities and incorporate repeated measures. We will use extensive test-retest data provided by the Consortium of Reproducibility and Reliability (CORR) for validation, and to identify coupling measures that are particularly reliable. The rich multi-modal imaging data and associated clinical phenotypes from the Philadelphia Neurodevelopmental Cohort (PNC) will allow us to delineate how inter- modal coupling evolves in normal brain development, and also establish how such relationships are altered in association with psychosis-spectrum symptoms. This proposal builds upon established collaboration between the PIs and capitalizes on their complimentary expertise in imaging statistics, developmental neuroimaging, psychopathology, and intimate familiarity with the PNC dataset. Upon completion, this project will provide powerful new tools for the neuroscience community, as well as novel insights regarding brain development and early psychosis-spectrum symptoms.