Statistical Methods for Mapping Human Brain Development

绘制人类大脑发育图谱的统计方法

• 批准号: 8517820
• 负责人: PHILIP T REISS
• 金额: $ 40万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2017-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8517820
• 关键词: Adult; Age; Attention; Attention deficit hyperactivity disorder; Brain; Brain imaging; Brain region; Brain scan; Child; Collaborations; Collection; Complex; Data; Data Analyses; Data Set; Dependence; Development; Diffusion Magnetic Resonance Imaging; Discipline; Disease; Exhibits; Functional Magnetic Resonance Imaging; Goals; Human; Human Development; Image; Individual; Institution; Lead; Location; Longevity; Maps; Measurement; Mental Health; Mental disorders; Methodology; Methods; Modeling; Neurosciences; Outcome; Polynomial Models; Psychiatry; Research; Research Personnel; Resolution; Rest; Sampling; Scientific Advances and Accomplishments; Statistical Methods; Techniques; Technology; Testing; Thick; Visual; Work; base; comparison group; flexibility; forging; imaging modality; improved; innovation; insight; interest; meetings; model development; neuroimaging; novel; tool

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.

描述（由申请人提供）：现代神经影像技术已将发育神经科学带入了一个前所未有的突破时代的门槛。通过对越来越多的儿童和成人样本进行高分辨率脑部扫描，研究人员正在绘制一生中大脑的正常发育情况以及与精神疾病相关的发育异常情况。这些研究通常需要在数万个大脑位置拟合模型，部分由于计算量大，研究人员倾向于采用次优方法。一个突出的例子是拟合皮质厚度随年龄变化的多项式模型。在拟合单个模型时，非参数平滑比多项式依赖具有众所周知的优势，但迄今为止，平滑方法尚未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大脑发育研究产生的大量神经影像数据集。 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。研究人员已开始为此开发许多创新技术，并建立了强有力的多机构合作，非常适合应对未来的许多挑战。第一个具体目标侧重于对表示感兴趣数量分布的平均值或给定百分位数的大量曲线进行计算上可行的估计， 以年龄等预测因素为条件。第二个目标包括与神经影像学特别相关的几种假设检验方法，包括针对平滑替代方案的多项式零假设检验，以及对发育轨迹和其他复杂结果的群体差异的检验。第三个目标最初是出于对大脑位置网格中每个点拟合样条的简洁视觉表示的需要，是开发用于聚类大量功能数据的新方法。 所提出的方法将应用于通过多种成像方式采集的数据，包括静息态功能成像 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，并且许多方法可以应用于神经成像领域之外。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。