Statistical Methods for Mapping Human Brain Development

描述（由申请人提供）：现代神经成像技术已经将发育神经科学带到了一个前所未有的突破时代的门槛。随着在越来越大的儿童和成人样本中获得高分辨率的大脑扫描，研究人员正在绘制大脑在整个生命周期中的正常发育以及与精神疾病相关的发育异常。这些研究通常需要在数万个大脑位置拟合模型，部分由于这种高计算量，研究人员倾向于采用次优方法。一个突出的例子是拟合一个多项式模型的皮质厚度随年龄的发展。非参数平滑提供了众所周知的优势，多项式依赖时，拟合一个单一的模型，但到目前为止，平滑方法还没有发现应用程序的设置中，成千上万的模型同时拟合。更广泛地说，迫切需要最先进的统计方法来处理发育中大脑研究产生的大量神经成像数据集。 该提案的目的是提供一个全面的工具包，用于正常和异常大脑发育的统计分析。研究人员已经开始为此开发一些创新技术，并建立了强大的多机构合作，非常适合应对未来的许多挑战。第一个具体目标集中于对表示感兴趣的量的分布的平均值或给定百分位数的大量曲线的计算上可行的估计， 以年龄等预测因素为条件。第二个目标包括几种与神经影像学特别相关的假设检验方法，包括多项式零假设对平滑替代品的检验，以及发育轨迹和其他复杂结果的组差异检验。第三个目标，最初的动机是需要简洁的视觉表示样条适合在每个点的网格中的大脑位置，是开发新的方法聚类大量的功能数据。 所提出的方法将被应用于多种成像方式，包括静息状态功能采集的数据 磁共振成像、扩散张量成像和皮质厚度测量。这里提出的大多数方法适用于任何成像方式，许多可以应用于神经成像领域之外。因此，拟议中的研究将对统计方法和神经科学，精神病学和其他学科产生重大影响。

DESCRIPTION (provided by applicant): Modern neuroimaging technology has brought developmental neuroscience to the threshold of an era of unprecedented breakthroughs. With high-resolution brain scans acquired in increasingly large samples of children and adults, investigators are mapping both the normal development of the brain over the lifespan, and the developmental abnormalities that are associated with psychiatric disorders. These studies typically entail fitting models at tens of thousands of brain locations, and due in part to this hih computational load, investigators have tended to settle for suboptimal methods. A prominent example is fitting a polynomial model for the development of cortical thickness with age. Nonparametric smoothing offers well-known advantages over polynomial dependence when fitting a single model, but to date, smoothing methodology has not found application to settings in which many thousands of models are fitted concurrently. More broadly, there is a critical need for state-of-the-art statistical methods to tackle the massive neuroimaging data sets generated by studies of the developing brain. The objective of this proposal is to provide a comprehensive toolkit for statistical analyses of normal and abnormal brain development. The investigators have begun to develop a number of innovative techniques toward this end, and have forged a strong multi-institution collaboration ideally suited to meeting the many challenges that lie ahead. The first specific aim focuses on computationally feasible estimation of large numbers of curves representing the mean, or a given percentile, of the distribution of a quantity of interest, conditional on a predictor such as age. The second aim encompasses several hypothesis testing methods that are particularly relevant to neuroimaging, including tests of polynomial null hypotheses against smooth alternatives, as well as tests for group differences in developmental trajectories and other complex outcomes. The third aim, originally motivated by the need for succinct visual representations of spline fits at each point in a grid of brain locations, is to develop novel methods for clustering large amounts of functional data. The proposed methods will be applied to data acquired by multiple imaging modalities, including resting-state functional magnetic resonance imaging, diffusion tensor imaging, and cortical thickness measurement. Most of the methods proposed here are applicable to any imaging modality, and many can be applied outside the field of neuroimaging. Thus the proposed research will have a significant impact both on statistical methodology and on neuroscience, psychiatry, and other disciplines.