Personalized spatiotemporal hemodynamic response models for functional magnetic resonance imaging

功能磁共振成像(FMRI)在特征化方面显示出巨大的前景 与人类心理功能和识别有关的大脑回路和网络 健康和病理性精神健康障碍背后的病理生理变化 衰老，物质滥用，甚至更多。在许多领域都取得了长足的进步，但 绝大多数的功能磁共振研究依赖于规范(或 高度受限)血流动力学响应函数(HRF)基本上 不准确。HRF因大脑区域、个体和年龄的不同而不同，但估计它 要有足够的准确度和精密度，在中小型研究中是有问题的。 因此，超过95%的功能磁共振研究使用固定形式的规范HRF。这将导致 严重的偏向、断电和混乱。这些问题都适用于基于任务的 和连通性研究，它们隐含地依赖于恒定的HRF的假设 跨地区、跨个人。回应《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)与高级统计建模相结合 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。在AIM 1，我们将基于可靠性对比常用的HRF型号的使用寿命 以及对任务状态和表型变量(例如，认知功能和 心情)。我们开发了新的方法来提取有意义的表型信息 从HRF形状和人口推断，并开发强大的软件，以实现同类最佳 模特们。在目标2中，我们将同类中最好的HRF模型集成到一个新的高斯过程中 建模并使用它导出特定于人口统计的时空HRF地图集，提供 基于易于测量的特征(年龄、性别和身体- 质量指数)和大脑区域。在目标3中，我们使用hrf图谱对rs-fmri进行去卷积。 数据并构建经HRF校正的连接组图谱。我们验证了HRF模型， 图集和两个独立的HCP疾病连接体上的连接体和CAM- CAN数据集(n=~700)，并与共享图集、Connectome和软件集成 研究界。这些大样本模型的开发将提供 更准确和精确地估计与任务相关的fMRI活动和连接性 心理健康、衰老、物质使用等方面的基础和临床研究。

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.