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

基本信息

批准号：
10585582
负责人：
Martin Lindquist
金额：
$ 79.42万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
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/10585582
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 Custom Data Data Set Development Disease Ecosystem Estimation Techniques Functional Magnetic Resonance Imaging Future Gaussian model Grant Human Image Individual Knowledge Lead Link Longevity Maps Measurable Measures Mental Depression Mental Health Mental Processes Mental disorders Methods Modeling Moods Morphologic artifacts Neurosciences Paper 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 base cognitive function cognitive neuroscience connectome connectome data depressed patient healthy aging hemodynamics high body mass index human data imaging study individual variation interest low socioeconomic status memory retrieval mental function mental health related disorder neuroimaging neurovascular neurovascular coupling next generation novel pathological aging relating to nervous system response secondary analysis sex spatiotemporal statistics substance misuse substance use virtual

项目摘要

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) 在表征方面显示出巨大的前景与人类心理功能和识别相关的大脑回路和网络心理健康障碍、健康和病理性的病理生理变化老龄化、药物滥用等许多领域都取得了巨大进步，但是绝大多数功能磁共振成像研究依赖于规范（或高度受限的）血流动力学响应函数（HRF）基本上是 HRF 因大脑区域、个体和年龄而异，但对其进行估计。在中小型研究中，具有足够的准确性和精密度是有问题的。因此，超过 95% 的 fMRI 研究使用固定形式的规范 HRF。严重的偏见、权力损失和混淆这些问题都适用于基于任务的情况。和连通性研究，隐含地依赖于恒定 HRF 的假设响应“特别利益通知”(NOSI)。关于使用人类连接组 [HCP] 数据进行二次分析”，建议使用寿命 5-100岁）结合先进的统计模型我们 HCP 数据（n=~3,600 个来自人们的高质量数据集 o 解决这个问题。 1、我们将基于可靠性来对比整个生命周期中常用的 HRF 模型以及“解码”任务状态和表型变量的能力（例如，认知功能和我们开发了提取有意义的表型信息的新方法。根据 HRF 形状和总体推断，并开发强大的软件以实现一流的在目标 2 中，我们最好将类 HRF 模型集成到新颖的高斯过程中。模型并使用它导出特定人口的时空 HRF 图集，提供基于易于测量的特征（年龄、性别和身体特征）定制 HRF 在目标 3 中，我们使用 HRF 图集对 rs-fMRI 进行解卷积。数据并构建 HRF 校正的连接组图，我们验证 HRF 模型，图集，以及两个独立的 HCP 疾病连接组和 CAM-上的连接组 CAN 数据集 (n=~700)，并与以下人员共享图集、连接组和软件集成这些大样本模型的开发将为研究界提供帮助。更准确和精确地估计任务相关的功能磁共振成像活动和连接性心理健康、衰老、药物滥用等方面的基础和临床研究。