Statistical Methods for Mapping Human Brain Development

描述(申请人提供)：现代神经成像技术将发展神经科学带入了一个前所未有的突破时代的门槛。随着在越来越多的儿童和成人样本中获得高分辨率的脑扫描，研究人员正在绘制大脑在整个生命周期内的正常发育情况，以及与精神障碍相关的发育异常。这些研究通常需要在数万个大脑位置进行模型拟合，部分由于HIH的计算负荷，研究人员倾向于采用不太理想的方法。一个突出的例子是拟合皮层厚度随年龄发展的多项式模型。在拟合单个模型时，非参数平滑比多项式相关性具有众所周知的优势，但到目前为止，平滑方法还没有应用于同时拟合数千个模型的设置。更广泛地说，迫切需要最先进的统计方法来处理由发育中的大脑研究产生的海量神经成像数据集。这项提议的目的是提供一个全面的工具包，用于对正常和异常的大脑发育进行统计分析。调查人员已经开始为此目的开发一些创新的技术，并建立了一个强大的多机构合作，非常适合迎接未来的许多挑战。第一个具体目标集中在对代表感兴趣数量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， 以年龄等预测因子为条件的。第二个目标包括几种与神经成像特别相关的假设检验方法，包括多项式零假设与平滑替代方案的测试，以及对发展轨迹和其他复杂结果的群体差异的测试。第三个目标最初是为了对大脑位置网格中每个点上的样条线拟合进行简洁的视觉表示，目的是开发新的方法来对大量功能数据进行聚类。建议的方法将应用于通过多种成像模式获取的数据，包括静息状态功能 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，许多方法可以应用于神经成像领域以外的领域。因此，拟议的研究将对统计方法以及神经科学、精神病学和其他学科产生重大影响。 与公共健康相关：脑成像已成为研究人脑如何正常发育以及某些精神障碍如何反映发育异常的关键工具。大脑成像研究产生了越来越多的海量数据，标准的数据分析技术无法从这些数据中提取科学相关的信息。这项拟议的工作将开发新的统计方法来分析这些数据集，这将促进对大脑的科学理解，并最终可能导致改善精神疾病的治疗。

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. PUBLIC HEALTH RELEVANCE: Brain imaging has emerged as a critical tool for the studying how the human brain normally develops, and how some psychiatric disorders may reflect abnormalities of development. Increasingly massive quantities of data are being generated by brain imaging studies, and standard data analysis techniques are not equipped to extract scientifically relevant information from these data. The proposed work will develop new statistical methods for analyzing such data sets, which will advance scientific understanding of the brain and may ultimately lead to improved treatments for mental illness.