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

项目摘要 该提案将提供一种创新的综合统计方法来分析功能性磁共振成像 （fMRI）数据。迄今为止，fMRI数据的巨大规模决定了两阶段分析，首先减少时间数据， 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， 即使在小样本量的研究中，集成的一阶段全脑数据策略也将改善估计和功效。 所提出的方法将普遍适用于fMRI数据，但为了说明方法的价值，我们将 重新分析来自两个对心理健康重要的领域的公开数据集。自杀是一个主要的公共卫生问题， 疾病预防控制中心报告说，它是2017年三分之二的凶杀案的原因，但它仍然非常不可预测。最近 一项研究提供了34名受试者的fMRI数据，这些受试者分别暴露于10个单词， 内涵对这些非自愿数据的分析可以揭示自杀者和自杀意念者之间的差异， 自杀风险升高的受试者，或识别组间具有最高区分力的单词，所有有用的结果 来诊断和预防自杀重度抑郁症（MDD）预计将是最常见的原因， 到2030年，世界范围内的MDD患者将减少到100%，但只有一半的MDD患者接受治疗。最近的一项研究提供了39名 受试者使用经验证的情感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，以前只进行了分析，以更好地检测大脑的差异， MDD和对照之间的激活，包括一名MDD受试者由于机器过度运动而丢失数据。 我们的方法将直接模拟功能磁共振成像数据的复杂，高维结构，包括三个空间维度， 通过将多元线性回归扩展为更自然和正确的张量对张量线性回归， 框架，以前被认为是计算上难以处理的。我们的工作将使其可行，如果权力的优势， 如我们所期望的那样，我们的方法应该成为未来功能磁共振成像数据分析的标准。线性 回归框架对于实践者来说是熟悉的，沿着我们将开发的高效、用户友好的软件， 促进其在功能磁共振成像社区的广泛采用。 我们在Aim 1中开发了张量对张量时间序列回归和相关方法来对患者进行分类并识别 目标2中的生物标志物。将我们的方法应用于自杀和MDD数据集将有助于演示该方法， 揭示有关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可重复性将降低未来功能磁共振成像研究的成本，时间和不适， 并且可以鼓励功能磁共振成像在临床环境中的应用，在临床环境中，病理检测可以通过诊断来进行， 适当的干预。最后，我们提供的灵活的统计框架将鼓励进一步的建模创新， 适应功能磁共振成像数据结构的挑战和假设，包括那些还没有想象出来的。

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.