LEAPS-MPS: Functional Data Analysis for Conditional Quantiles with Applications in Medical Studies

LEAPS-MPS：条件分位数的功能数据分析及其在医学研究中的应用

• 批准号: 2213140
• 负责人: Eliana Christou
• 金额: $ 21.35万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2213140&HistoricalAwards=false
• 关键词: LEAPS; MPS; Functional; Data; Analysis

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Functional Data Analysis (FDA) investigates data containing information that varies over a continuum such as the temporal and spatial domains, with tremendous implications for many real-world applications, including finance, natural language processing, electric grid stabilization, and especially the medical field. For example, data types such as functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) provide multiple observations for different locations of the brain and scalp, and are prime contenders of FDA. However, both fMRI and EEG data are often highly skewed and may contain outliers; currently, traditional FDA methods are not able to analyze them fully and accurately. Despite the many recent advancements in the FDA literature, this area is still in its infancy with much potential yet to be uncovered. The project addresses this gap by developing a novel approach for analyzing functional data that is better able to account for heavy skewness and outlying observations. This approach will be used to examine both fMRI and EEG data with the goal of investigating two important neurological disorders: (1) attention deficit hyperactivity disorder (ADHD) and (2) alcoholism. Neurological disorders exhibit brain activity characterized by drastically different biochemical and electrical behavior where the output is either atypically high or atypically low, and hence more effectively analyzed using this new method. The results will be incorporated into open-source software for reproducibility and further research, and for encouraging possible future collaborations in the medical community. A new summer research program will support students from underrepresented backgrounds who will analyze the fMRI and EEG data sets and will provide recommendations and suggestions to the medical community through public dissemination. This experience will improve students’ attitudes towards STEM careers, help them develop skills necessary for the 21st-century workforce, and encourage them to seek advanced degree opportunities. The project will provide research training opportunities at the graduate level as well. The infinite dimensional nature of functional data motivates the use of dimension reduction techniques, whereas the skewness and outlying observations often encountered in medical data require the use of quantile regression (QR). Despite recent advancements in FDA, there is no current work on the intersection of both dimension reduction and QR for FDA. Therefore, the project addresses this gap by (1) developing a dimension reduction technique for QR with infinite dimensional functional predictors, (2) extending the method to incorporate additional categorical predictors that are common in medical studies, and (3) integrating the methods into students’ training through investigation of the medical disorders of ADHD and alcoholism. This work will develop computationally efficient algorithms that will be disseminated through the PI’s existing software. Finally, this research will lead to new critically needed statistical methods for medical researchers working with rare diseases and will help clinicians to garner more actionable insight.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项是根据2021年《美国救援计划法》（公法117-2）全部或部分资助的。功能数据分析（FDA）研究了包含随临时和空间领域等连续元素而变化的信息，对许多现实世界应用，包括财务，自然语言处理，电网稳定，尤其是医疗领域，对许多现实世界应用产生了巨大影响。例如，诸如功能磁共振成像（fMRI）和脑电图（EEG）之类的数据类型为大脑和头皮的不同位置提供了多个观察结果，并且是FDA的主要竞争者。但是，fMRI和EEG数据通常都高度偏斜，并且可能包含异常值。当前，传统的FDA方法无法完全准确地分析它们。尽管FDA文献最近取得了许多进步，但该领域仍处于起步阶段，尚未发现很大的潜力。该项目通过开发一种分析功能数据的新方法来解决这一差距，该功能数据能够更好地解释偏度和外围观察结果。该方法将用于检查fMRI和EEG数据，目的是研究两种重要的神经系统疾病：（1）注意力缺陷多动障碍（ADHD）和（2）酒精中毒。神经系统疾病暴露的脑活动为特征，其特征是截然不同的生化和电气行为，其中输出在非典型上是高度高或非典型的低，因此使用这种新方法更有效地分析。该结果将纳入开源软件中，以进行再现和进一步研究，并鼓励医学界可能的未来合作。一项新的夏季研究计划将支持来自代表性不足的背景的学生，他们将分析fMRI和EEG数据集，并将通过公共传播向医学界提供建议和建议。这种经验将改善学生的参与者，从事STEM职业，帮助他们发展21世纪劳动力所需的技能，并鼓励他们寻求高级学位机会。该项目还将在研究生层面提供研究培训机会。功能数据的无限尺寸性质激发了缩小维度的使用，而在医学数据中经常遇到的偏度和偏远观测需要使用分位数回归（QR）。尽管FDA最近取得了进步，但目前尚无关于降低和QR的交集的工作。因此，该项目通过（1）使用无限维度功能预测因子来开发QR的降低技术来解决这一差距，（2）扩展了在医学研究中使用其他类别预测指标的方法，（3）通过研究ADHD和酒精中毒的医学疾病来将方法整合到学生的培训中。这项工作将开发计算高效的算法，这些算法将通过PI的现有软件进行传播。最后，这项研究将为患有罕见疾病的医学研究人员提供新的急需统计方法，并将帮助临床医生获得更可行的见解。该奖项反映了NSF的法定使命，并通过使用基金会的知识分子优点和更广泛的审查标准评估来诚实地获得支持。