BIOSTATISTICS FOR CONNECTOMES

描述（由申请人提供）：本提案旨在开发面向对象的数据分析（OODA）方法，该方法高度新颖，与现有的人脑扫描连接体分析方法相补充，连接体被定义为代表大脑解剖或功能连接的图形。OODA是统计学的一个新兴领域，其中将经典统计方法（例如，假设检验、回归、估计、置信区间）应用于图或函数等数据对象。通过直接分析数据对象，我们可以避免在将数据对象转换为数值汇总统计时必然发生的信息丢失。这个提议的概念飞跃是OODA统计方法将被开发并直接应用于连接体，而不需要将它们转换为导致信息丢失的汇总特征。通过提供在不丢失信息的情况下分析连接体集的统计工具，可能会实现对神经学和医学的新见解。本提案的具体目标是：(1)开发用于分析人类连接组数据的OODA方法和软件。更具体地说，一个数学框架的假设检验，回归和主成分分析将开发模型和分析一组连接体。所提出的方法将允许比较连接体组（例如，病例与对照组的大脑结构/功能是否不同？），执行回归建模连接体作为受试者协变量（如年龄、性别、疾病）的函数，或纵向（例如，大脑结构/功能如何随时间变化，男性和女性的变化是否不同？），并测量连接体群体结构或功能变化的来源(例如，人群中大脑结构/功能的自然变异性是什么？(2)使用我们的共同研究者生成的现有连接组数据集验证特定目标1中开发的工具，以回答与其研究目标相关的临床问题。在连接组数据中开发的方法的验证将有助于评估拟开发方法的生物学意义；(3)通过使用图理论测量分析特定目标2中使用的相同数据，比较特定目标1中开发的连接组OODA方法的性能，这些方法与现有的人脑扫描连接组分析方法相辅相成。

DESCRIPTION (provided by applicant): This proposal aims to develop object oriented data analysis (OODA) methods that are highly novel and complementary to existing methods of analysis of human brain scan connectomes, defined as graphs representing brain anatomical or functional connectivity. OODA is an emerging field of statistics where classical statistical approaches (e.g., hypothesis testing, regression, estimation, confidence intervals) are applied to data objects such as graphs or functions. By analyzing data objects directly we can avoid loss of information that necessarily occurs when data objects are transformed into numerical summary statistics. The conceptual leap in this proposal is that OODA statistical methods will be developed and applied directly to connectomes without needing to transform them into summary features which incur loss of information. By providing statistical tools that analyze sets of connectomes without loss of information, new insights into neurology and medicine may be achieved. The Specific Aims of this proposal are: (1) Develop OODA methods and software for analyzing human connectome data. More specifically, a mathematical framework for hypothesis testing, regression and Principal Components Analysis will be developed to model and analyze set of connectomes. The proposed methodology will allow to compare groups of connectomes (e.g., Is the brain structure/function different in cases versus controls?), to perform regression for modeling connectomes as a function of subject covariates such as age, gender, disease, or longitudinally (e.g., How does the brain structure/function change over time and does it change differently in males and females?), and to measure sources of structural or functional variation across populations of connectomes (e.g., What is the natural variability of brain structure/function within the population?); (2) Validate the tools developed in Specific Aim 1 using existing connectome datasets generated by our co-investigators to answer clinical questions relevant to their research objectives. The validation of the methodology to be developed in connectome data from will contribute to assess the biological significance of the methods proposed to be developed; and (3) Compare the performance of connectome OODA methods developed in Specific Aim 1 as complementary to existing methods of analysis of human brain scan connectomes by analyzing the same data used in Specific Aim 2 using graph-theoretical measurements. PUBLIC HEALTH RELEVANCE: Developing statistical methods for hypothesis testing, regression, and principal component analysis of sets of connectomes without loss of information, new insights into neurology and medicine may be achieved. We propose to develop statistical methods within the framework of object oriented data analysis (OODA) which do not require a reduction of the connectome to features, and therefore avoid loss of information. This proposal will help bridge the translation of connectome to clinical applications.