Improving functional MRI Analysis via Integrated One-Step Tensor-variate Methodology

项目摘要 这项提议将提供一种创新的综合统计方法来分析功能磁共振成像 (FMRI)数据。到目前为止，功能磁共振成像数据的海量决定了两阶段分析，fi首先将时间数据减少到 将每个体素转换为单个激活值，然后对激活区域进行空间分析。我们的基本前提是 即使在样本量较小的研究中，集成的一阶段全脑数据策略也将提高估计和能力。 建议的方法将普遍适用于fmri数据，但为了说明这些方法的价值，我们将 从两个对心理健康重要的领域重新分析可公开获得的数据集。自杀是一个主要的公共卫生问题， 疾控中心报告称，2017年三分之二的凶杀案都是由它造成的，但它仍然非常不可预测。近期 Work提供了34名受试者的fMRI数据，让他们接触10个单词，每个单词都有积极的、消极的或与死亡有关的 内涵。对这些非自愿数据的分析可以揭示自杀未遂者和理想者之间的差异 具有更高自杀风险的受试者，或识别组间具有最高歧视能力的词语，都是有用的结果 用于诊断和预防自杀。严重抑郁障碍(MDD)被认为是最常见的 到2030年，全世界的MDD患者中只有一半能得到治疗。最近的一项研究提供了39个人的功能磁共振数据 受试者使用经过验证的情绪、音乐和非音乐听觉范式。长期目标是利用音乐作为 MDD的诊断或治疗。我们将使用我们的方法重新评估性别、年龄和其他测量的协变量，如 作为音乐的主题评级，以前只进行描述性分析，以更好地检测大脑中的差异 MDD和控制之间的激活，包括一个MDD受试者由于机器过度运动而丢失数据。 我们的方法将直接对fMRI数据的复杂、高维结构进行建模，包括三个空间维度， 时间和对象，通过将多元线性回归扩展为更自然和更正确的张量线性回归 框架，以前被认为在计算上很难处理。我们的工作将使它变得可行，如果电力优势 正如我们预期的那样，我们的方法应该成为未来功能磁共振数据分析的标准。直线型 回归框架是实践者所熟悉的，它与我们将要开发的有效的、用户友好的fi软件一起，将 促进其在功能磁共振社区中的广泛采用。 我们在目标1中开发了张量-张量时间序列回归以及相关的方法来对患者进行分类和识别 AIM中的生物标记物2.将我们的方法应用于自杀和MDD数据集将有助于演示这些方法，而 揭示关于这两个非常重要的心理健康挑战的可操作信息。更广泛地说，提高了可靠性 更少的受试者和更短的任务的重复性将减少未来fMRI研究的成本、时间和不适感， 并可以鼓励在临床环境中采用功能磁共振成像，在这种情况下，病理检测之后可以进行诊断和 适当的干预。最后，我们提供的fl可扩展统计框架将鼓励进一步的建模创新 适应fMRI数据结构中的挑战和假设，包括那些尚未想象的数据。

Project Summary This proposal will deliver an innovative integrated statistical approach to analyze functional Magnetic Resonance Imaging (fMRI) data. The massive size of fMRI data has dictated, to date, a two-stage analysis, first reducing the temporal data at each voxel to a single activation value, followed by a spatial analysis for activated regions. Our basic premise is that an integrated one-stage, whole-brain data strategy will improve estimation and power even in studies with small sample sizes. The proposed methods will be generally applicable to fMRI data, but to illustrate the value of the methods, we will reanalyze publicly available datasets from two areas of importance to mental health. Suicide is a major public health concern, with the CDC reporting it to be the cause of two-thirds of all homicides in 2017, yet it remains highly unpredictable. Recent work provided fMRI data on 34 subjects upon exposing them to 10 words each with positive, negative or death-related connotations. Analysis of such involuntary data can reveal differences between suicide attempters and ideators, pinpoint subjects with elevated suicide risk, or identify the words with highest discriminatory power between groups, all useful outcomes for diagnosing and preventing suicide. Major Depressive Disorder (MDD) is projected to be the most prevalent cause of disease worldwide by 2030, yet only half of MDD patients receive treatment. A recent study provided fMRI data on 39 subjects using a validated emotional musical and nonmusical auditory paradigm. The long-term goal is to leverage music as a diagnostic or therapy for MDD. We will use our methods to re-evaluate sex, age, and other measured covariates, such as subject ratings of the music, which were previously only analyzed descriptively, to better detect differences in cerebral activation between MDD and controls, including one MDD subject with missing data due to excess motion in the machine. Our approach will directly model the complex, high-dimensional structure of fMRI data, including three spatial dimensions, time, and subject, by extending multivariate linear regression to a more natural and correct tensor-on-tensor linear regression framework, previously assumed to be computationally intractable. Our work will make it feasible and if the power advantages are as substantial as we expect, our approach should become the standard for fMRI data analysis in the future. The linear regression framework is familiar to practictioners, which along with the efficient, user-friendly software we will develop, will facilitate its wide adoption in the fMRI community. We develop tensor-on-tensor time series regression in Aim 1 and associated methods to classify patients and identify biomarkers in Aim 2. Application of our methods to a suicide and MDD datasets will serve to demonstrate the methods, while revealing actionable information about these two very important mental health challenges. More broadly, increased reliability and reproducibility with fewer subjects and shorter tasks will decrease the cost, time, and discomfort of future fMRI studies, and could encourage the adoption of fMRI in a clinical setting where pathology detection can be followed by diagnosis and appropriate intervention. Finally, the flexible statistical framework we provide will encourage further modeling innovation to accommodate challenges in and hypotheses about the structure of fMRI data, including those not yet imagine.