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

通过集成一步张量变量方法改进功能 MRI 分析

• 批准号: 10608866
• 负责人: Ranjan Maitra
• 金额: $ 22.8万
• 依托单位: IOWA STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-22 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10608866
• 关键词: Accounting; Adolescent; Adopted; Adoption; Age; Area; Auditory; Behavioral Research; Biological Markers; Biomedical Research; Brain; Centers for Disease Control and Prevention (U.S.); Cerebrum; Cessation of life; Classification; Clinical; Clinical Research; Communities; Complex; Data; Data Analyses; Data Set; Dependence; Detection; Development; Diagnosis; Diagnostic; Dimensions; Discriminant Analysis; Disease; Elements; Emotional; Exposure to; Feeling suicidal; Functional Magnetic Resonance Imaging; Future; Goals; Health; Homicide; Image; Intervention; Knowledge; Learning; Linear Models; Linear Regressions; Major Depressive Disorder; Measurement; Measures; Mental Health; Methodology; Methods; Modeling; Motion; Music; Outcome; Pathology; Patients; Performance; Public Health; Reporting; Reproducibility; Research Proposals; Sample Size; Sensitivity and Specificity; Series; Stimulus; Structure; Suicide; Suicide attempt; Suicide prevention; Techniques; Thinking; Time; United States; Work; addiction; biomarker discovery; biomarker selection; cognitive development; cost; disability; disorder control; flexibility; high dimensionality; holistic approach; imaging study; improved; innovation; large datasets; outcome prediction; response; sex; simulation; suicidal risk; suicide attempter; user friendly software

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.

项目摘要 该建议将提供创新的综合统计方法来分析功能磁共振成像 （fMRI）数据。迄今为止，FMRI数据的庞大规模决定了两阶段的分析，首先降低了临时数据 每个体素到单个激活值，然后进行活化区域的空间分析。我们的基本前提是 综合的一阶段，全脑数据策略即使在样本量较小的研究中也可以提高估计和权力。 所提出的方法通常适用于fMRI数据，但是为了说明方法的价值，我们将 将公开可用的数据集从两个重要的领域重新分析到心理健康。自杀是一个主要的公共卫生问题， 疾病预防控制中心（CDC）报告是2017年所有凶杀案中三分之二的原因，但仍然是不可预测的。最近的 工作提供了34名受试者的fMRI数据，将其暴露于10个单词中，每个单词与正面，负或死亡有关 内涵。对这种非自愿数据的分析可以揭示自杀式意见和想法之间的差异 自杀风险升高或识别群体之间具有最高歧视性的单词，所有有用的结果 用于诊断和预防自杀。主要抑郁症（MDD）预计是最普遍的原因 到2030年，全球疾病，但只有一半的MDD患者接受治疗。最近的一项研究提供了39的fMRI数据 使用经过验证的情感音乐和非音乐听觉范式的主题。长期目标是利用音乐作为 MDD的诊断或疗法。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，例如 作为音乐的主题评分，以前仅被描述性地分析，以更好地检测脑的差异 MDD和控件之间的激活，包括一个MDD受试者，由于机器中超过运动而缺少数据。 我们的方法将直接建模fMRI数据的复杂，高维结构，包括三个空间维度，即 时间和主题，通过将多元线性回归扩展到更自然，更正确的张量线性回归 框架，以前假定在计算上是棘手的。我们的工作将使它变得可行，如果有优势 正如我们预期的那样，我们的方法将来应该成为fMRI数据分析的标准。线性 回归框架对实践者很熟悉，这与我们将开发的有效，用户友好的软件一起将 促进其在功能磁共振成像社区中的广泛采用。 我们在AIM 1和相关方法中开发张张量的时间序列回归，以对患者进行分类并确定 AIM 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这些方法，而 揭示有关这两个非常重要的心理健康挑战的可行信息。更广泛地，可靠性提高 并且对较少受试者的可重复性和较短的任务将降低未来功能磁共振成前研究的成本，时间和不适感， 并可以鼓励在临床环境中采用fMRI，诊断和 适当的干预。最后，我们提供的灵活统计框架将鼓励进一步建模创新 适应FMRI数据结构的挑战和假设，包括尚未想象的挑战。