Inter-modal Coupling Image Analytics

项目总结 现在几乎所有的脑成像研究都收集了多种成像方式，以求得出两者的测量结果。 来自不同成像序列的结构和功能。虽然量化数据科学家们关注的是 使用多模式成像、严格的统计方法预测结果的机器学习方法 对于检查成像方式之间的关系的研究一直滞后。目前，缺乏统计数据 评估多式联运耦合(IMCO)的方法给调查人员留下了缺乏的特别解决方案 统计能力差，容易出现第一类错误，对科学的严谨性和可重复性构成威胁。在这 应用，我们提出了稳健的方法，利用特定于对象的测量并使用非线性 建模以解决脑图或网络中的复杂关系，同时考虑重要的协变量 (目标1)。此外，我们将开发强大的方法来评估兴趣的影响(例如， 精神病理学、发育)在大脑网络中得到丰富(目标2)。对这种耦合的评估 统计关联和脑网络之间的关系将利用来自统计基因组学的工具(例如， 基因集浓缩分析)，以提供使用高密度的 立体的、个性化的大脑网络。最后，我们将使用这些工具来描述跨诊断 青少年精神病患者执行功能障碍与脑内结构-功能耦合异常有关 脑网络(目标3)。为此，我们将利用三个海量数据资源：费城 神经发育队列(PNC；n=1,601)、健康脑网络(n=3,200)和人类 连接组-发育(HCP-D；n=1,300)研究放在一起，拟议的工作建立在显著的 在第一个项目阶段取得成功，有望产生严格和可推广的方法来描述 大脑结构和功能的互补测量之间的关系。

PROJECT SUMMARY Almost all brain imaging studies now collect multiple imaging modalities, in an effort to derive measures of both structure and function from diverse imaging sequences. While quantitative data scientists have focused on machine learning approaches for predicting outcomes using multi-modal imaging, rigorous statistical methods for examining the relationship between imaging modalities have lagged behind. At present, the lack of statistical methodologies for assessing inter-modal coupling (IMCo) has left investigators with ad hoc solutions that lack statistical power and are prone to type I error, posing a threat to scientific rigor and reproducibility. In this application, we propose robust methods that leverage subject-specific measurements and use nonlinear modeling to address complex relationships in brain maps or networks, while accounting for important covariates (Aim 1). Furthermore, we will develop powerful approaches for assessing whether effects of interest (e.g., psychopathology, development) are enriched within brain networks (Aim 2). Assessment of this coupling between statistical associations and brain networks will capitalize upon tools from statistical genomics (e.g., gene set enrichment analysis) to provide principled methods for conducting enrichment analyses using high- dimensional, personalized brain networks. Finally, we will use these tools to delineate how trans-diagnostic executive dysfunction in youth with mental illness is related to abnormalities in structure-function coupling within brain networks (Aim 3). To do this, we will leverage three massive data resources: the Philadelphia Neurodevelopmental Cohort (PNC; n=1,601), the Healthy Brain Network (n=3,200), and the Human Connectome-Development (HCP-D; n=1,300) study Taken together, the proposed work builds upon the notable success in the first project period, promising to yield rigorous and generalizable methods for delineating the relationships between complementary measures of brain structure and function.