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                【95周年校庆系列◥讲座】Brain Regions Identified as Being Associated with Verbal Reasoning through the Use of Imaging Regression via Internal Variation

                时间:2020-06-19         阅读:

                光华讲坛——社会名流与企业ξ 家论坛第 5821 期


                主题Brain Regions Identified as Being Associated with Verbal Reasoning through the Use of Imaging Regression via Internal Variation

                主讲人耶鲁大学 张和平教授

                主持人统计学院 林华『珍教授


                直播平台及会议IDzoom会议,会议ID:980 5888 4717


                主办单位:统计研究中心 数据科学︼与商业智能联合实验室 统计学院 科研处


                Heping Zhang is Susan Dwight Bliss Professor of Biostatistics, Professor of Statistics and Data Science, and Professor of Child Study at Yale University. Dr. Zhang published over 300 research articles and monographs in theory and applications of statistical methods and in several areas of biomedical research including epidemiology, genetics, mental health, and reproductive health. He directs the Collaborative Center for Statistics in Science that coordinates major national research networks to understand the etiology of pregnancy outcomes and to evaluate treatment effectiveness for infertility. He is a fellow of the American Statistical Association and a fellow of the Institute of Mathematical Statistics. He was named the 2008 Myrto Lefokopoulou distinguished lecturer by Harvard School of Public Health and a 2011 Medallion Lecturer by the Institute of Mathematical Statistics. Professor Zhang is the Editor of the Journal of the American Statistical Association – Applications and Case Studies.

                张和平,耶鲁大学Susan Dwight Bliss生物统计学教授,统计学和数据科学教授,儿童研究教授。张老师在包括流行病学、遗传学、心理健康和生殖健康等多个生物医学研究领域发表了300多篇研究论╱文和专著。他领导了科学统计合作中心,该中心协调主要的国家研究网络,以了解妊娠→结局的病因,并评估不孕症的治疗效果。他是ASA和IMS的fellow。2008年他∏被哈佛大学公共卫生学院提名为Myrto Lefokopoulou杰出讲师,2011年被IMS提名为Medallion Lecturer。张老师是JASA- Applications and Case Studies的主编。


                Brain-imaging data have been increasingly used to understand intellectual disabilities. Despite significant progress in biomedical research, the mechanisms for most of the intellectual disabilities remain unknown. Finding the underlying neurological mechanisms has been proved difficult, especially in children due to the rapid development of their brains. We investigate verbal reasoning, which is a reliable measure of individuals’ general intellectual abilities, and develop a class of high-order imaging regression models to identify brain subregions which might be associated with this specific intellectual ability. A key novelty of our method is to take advantage of spatial brain structures, and specifically the piecewise smooth nature of most imaging coefficients in the form of high-order tensors. Our approach provides an effective and urgently needed method for identifying brain subregions potentially underlying certain intellectual disabilities. The idea behind our approach is a carefully constructed concept called Internal Variation (IV). The IV employs tensor decomposition and provides a computationally feasible substitution for Total Variation (TV), which has been considered in the literature to deal with similar problems but is problematic in high order tensor regression. Before applying our method to analyze the real data, we conduct comprehensive simulation studies to demonstrate the validity of our method in imaging signal identification. Then, we present our results from the analysis of a dataset based on the Philadelphia Neurodevelopmental Cohort for which we preprocessed the data including re-orienting, bias-field correcting, extracting, normalizing and registering the magnetic resonance images from 978 individuals. Our analysis identified a subregion across the cingulate cortex and the corpus callosum as being associated with individuals’ verbal reasoning ability, which, to the best of our knowledge, is a novel region that has not been reported in the literature. This finding is useful in further investigation of functional mechanisms for verbal reasoning.

                This is a joint work with Long Feng and Xuan Bi.

                脑成像数据已越来越多地用于了解智力障碍。尽管生物医学研究取得了重大进展,但大↙多数智力障碍的机制仍不清楚。发现潜在的神经机制已经被证明是困难的,尤其是儿∏童,因为他们的大脑发展迅速。我们研究语言推理,这是一个可靠的◣衡量个人一般智力能力的方法,并开发了一类高阶成像回归模型,以确定可能与这种特定智力能力相关的大脑亚区域々。我们的方法的一个关键创新点是利用空间大脑结构,特别是在高阶张量形式的成像系卐数的分段光滑性质。我们的方法提供了一◇种有效且迫切需要的方法来识别大脑亚区域潜在的某些智力残疾。我们的方法⌒旨意精心构建一个称为内部变异的概念。内部变∩异采用张量分解,为全变异提∑ 供了一种计算上可行的替代,文献中已经考虑了全变异来处理类似问题,但在高阶张量回归中存在问★题。在应用我们的方法对真实数据进行分析之前,我们进行了全面的仿真研究,以证明我们的方法在成像信号识别中的有效性。然后,我们展示了基于费♀城神经发育队列的数据集的分析结果,我们对978个人的Ψ磁共振图像进行了数据预处理,包括重∴新定向、偏斜场校正、提取、归一化和登记。我们的分析确定了一个横跨扣带皮层和胼胝体的亚区域与个人¤的语言推理能力有关,就我们所知,这是一个尚未在文献中报道的新区域。这一发现对进一步研究语言推理的功能机制是有用的。该论文是和Long Feng、Xuan Bi.共同完成的。