Personalized spatiotemporal hemodynamic response models for functional magnetic resonance imaging

用于功能磁共振成像的个性化时空血流动力学响应模型

• 批准号: 10705163
• 负责人: Martin Lindquist
• 金额: $ 76.51万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10705163
• 关键词: Accounting; Address; Affect; Affective; Age; Aging; Anxiety; Area; Atlases; Biophysics; Blood Vessels; Body mass index; Brain; Brain region; Characteristics; Clinical; Clinical Research; Code; Cognition; Cognitive; Communities; Complex; Computer software; Coupling; Data; Data Set; Development; Disease; Ecosystem; Estimation Techniques; Functional Magnetic Resonance Imaging; Future; Grant; Human; Image; Individual; Knowledge; Link; Longevity; Maps; Measurable; Measures; Mental Depression; Mental Health; Mental Processes; Mental disorders; Methods; Modeling; Moods; Morphologic artifacts; Neurosciences; Paper; Parameter Estimation; Participant; Patients; Persons; Phenotype; Population; Post-Traumatic Stress Disorders; Process; Proxy; Reproducibility; Research; Rest; Sampling; Series; Shapes; Signal Transduction; Socioeconomic Status; Statistical Models; Structure; Substance abuse problem; Symptoms; Techniques; Testing; Time; Variant; Work; age related; aged; cognitive function; cognitive neuroscience; connectome; connectome data; depressed patient; healthy aging; hemodynamics; high body mass index; human data; individual variation; interest; low socioeconomic status; memory retrieval; mental function; neural; neuroimaging; neurovascular; neurovascular coupling; next generation; novel; pathological aging; population stratification; regional difference; response; secondary analysis; sex; spatiotemporal; statistics; substance misuse; substance use; virtual

Functional Magnetic Resonance Imaging (fMRI) shows great promise in characterizing brain circuits and networks related to human mental function and identifying pathophysiological changes underlying mental health disorders, healthy and pathological aging, substance misuse, and beyond. Great strides are being made in many areas, but the vast majority of fMRI research relies on the simplifying assumption of a canonical (or highly constrained) hemodynamic response function (HRF) that is substantially inaccurate. The HRF varies across brain regions, individuals, and age, but estimating it with sufficient accuracy and precision is problematic in small to medium-sized studies. As a result, over 95% of fMRI studies use a canonical HRF of fixed form. This results in substantial bias, power loss, and confounding. These problems apply to both task-based and connectivity studies, which rely implicitly on the assumption of a constant HRF across regions and individuals. In response to the “Notice of Special Interest (NOSI) regarding the Use of Human Connectome [HCP] Data for Secondary Analysis”, propose to use the Lifespan aged 5-100) combined with advanced statistical modeling t we HCP data (n=~3,600 high-quality datasets from people o address this issue. In Aim 1, we will contrast commonly used HRF models across the lifespan based on reliability and ability to ‘decode’ task state and phenotypic variables (e.g., cognitive function and mood). We develop novel methods for extracting meaningful phenotypic information from HRF shape and population inference, and develop robust software for best-in-class models. In Aim 2, we integrate best-in-class HRF models into a novel Gaussian process model and use it derive a demographic-specific, spatiotemporal HRF atlas, providing customized HRFs based on readily measurable characteristics (age, sex, and body- mass index) and brain region. In Aim 3, we use the HRF atlas to deconvolve rs-fMRI data and construct an HRF-corrected connectome map. We validate the HRF models, atlas, and connectome on two independent HCP Disease Connectomes and the CAM- CAN dataset (n=~700), and share the atlas, connectome, and software integrations with the research community. The development of these large-sample models will provide more accurate and precise estimates of task-related fMRI activity and connectivity in basic and clinical studies of mental health, aging, substance use, and beyond.

功能磁共振成像（fMRI）表现出很大的前景 脑电路和与人类心理功能有关的网络和识别 健康和病理学的心理健康障碍的病理生理变化 衰老，滥用物质以及以下。在许多领域都取得了长足的进步，但是 绝大多数fMRI研究都依赖于规范的简化假设（或 高度约束）血液动力学反应函数（HRF）基本上是 不准确。人力资源管理范围跨大脑区域，个体和年龄，但估算 在中小型研究中，具有足够的准确性和精度是有问题的。 结果，超过95％的fMRI研究使用了固定形式的规范HRF。这导致 实质性偏见，功率损失和混杂。这些问题适用于两个基于任务的问题 和连通性研究，这些研究隐含地依赖于恒定HRF的假设 在各个地区和个人之间。回应“特殊关注通知（NOSI） 考虑使用人类连接组[HCP]进行次级分析”， 提议使用寿命 5-100岁）与先进的统计建模t结合 我们 HCP数据（n = 〜3,600来自人的高质量数据集 o解决这个问题。目标 1，我们将根据可靠性对比整个生命周期中常用的HRF模型进行对比 以及“解码”任务状态和表型变量的能力（例如，认知功能和 情绪）。我们开发了提取有意义的表型信息的新颖方法 从HRF形状和人口推断，并为一流的一流的软件开发可靠的软件 型号。在AIM 2中，我们将一流的HRF模型集成到了一个新颖的高斯过程中 模型并使用它得出人口特定的时空HRF地图集，提供 根据易于测量的特征（年龄，性别和身体 - 质量指数）和大脑区域。在AIM 3中，我们使用HRF图集来解vonvolve RS-FMRI 数据并构建由HRF校正的Connectome Map。我们验证HRF模型， Atlas和两个独立的HCP疾病连接组和CAM-上的Connectome 可以数据集（n = 〜700），并与 研究界。这些大样本模型的开发将提供 与任务相关的fMRI活动和连通性的更准确和精确的估计 心理健康，衰老，物质使用以及其他方面的基础和临床研究。