Personalized spatiotemporal hemodynamic response models for functional magnetic resonance imaging

功能性磁共振成像（fMRI）在表征 与人类心理功能相关的大脑回路和网络， 健康和病理性精神健康障碍的病理生理变化 衰老滥用药物等等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成像研究依赖于一个典型的简化假设（或 高度受限）血液动力学响应函数（HRF），其基本上 不准确。HRF因大脑区域、个体和年龄而异， 在中小型研究中，具有足够的准确度和精确度是有问题的。 因此，超过95%的fMRI研究使用固定形式的规范HRF。这导致 实质性偏差、功率损失和混淆。这些问题既适用于基于任务的 和连通性研究，这隐含地依赖于恒定HRF的假设 跨越地区和个人。特别关注通知（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 年龄5-100岁）结合先进的统计建模， 我们 HCP数据（n=约3，600个来自人群的高质量数据集 解决这个问题。在Aim中 1，我们将根据可靠性在整个生命周期内对比常用的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 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， 模型在目标2中，我们将同类最佳的HRF模型集成到一个新的高斯过程中 模型，并使用它导出一个特定的人口，时空HRF图集，提供 基于易于测量的特征（年龄、性别和身体）的定制HRF， 质量指数）和大脑区域。在目标3中，我们使用HRF图谱去卷积rs-fMRI 数据并构建HRF校正的连接体图谱。我们验证了HRF模型， 图谱和两个独立的HCP疾病连接体和CAM- CAN数据集（n=~700），并与 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任务相关的功能磁共振成像活动和连接， 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.