Resting-state functional connectivity mapping using Magnetic Particle Imaging (MPI)

项目摘要/摘要：自基于功能磁共振成像(FMRI)的神经成像技术发展以来 三十年前的研究，已经进行了大量的工作来描述人类内在的连通性 大脑，无论是对外界刺激的反应，还是静息状态。只有很小的进步。 FMRI的对比噪声比(CNR)，因此，fMRI的临床应用一直受到限制， 由于功能磁共振成像的CNR限制了其基于个体的功能神经成像的能力。 磁粒子成像(MPI)是一种利用大磁场的新型成像方式 注射超顺磁性氧化铁纳米颗粒(SPION)的时刻，以改进CNR。通过利用 MPI的CNR增加，本方案旨在验证fMPI检测静息状态CBV的能力 并使用这些信号来映射功能连接网络，类似于静息状态功能磁共振成像(RS-FMRI) FMRI)基于相关性的连通性映射。我们小组最近的研究已经证明了检测 在啮齿动物模型中，基于MPI的生理噪声信号随着注入的磁刺的剂量而变化。 在这项工作和正在进行的构建人类MPI扫描仪的努力的基础上，这项提议将具有以下特点 对静息状态下MPI层析成像时间序列数据中的信号波动及生成函数能力的检验 这些信号的连通性映射。我的中心假设是，有了这些原型 扫描仪和对信号敏感度的日益关注，生理成分的比率 在相关映射到其他非神经元产生的波动时所利用的信号将更高 MPI比传统的血氧水平依赖(BOLD)功能磁共振成像和更健壮的网络图 都会有结果。我计划生成第一个基于MPI的静息状态脑功能连接图，与 RS-fMRI，并有可能显示出更高的敏感度，以发现细微的网络变化，从而潜在地 提高我们对神经精神疾病患者个体的理解和表型的能力。 在这项提案中，我将首先展示描述啮齿类动物大脑皮层联合网络的能力 采用小口径MPI扫描仪，改进了现有的图像重建方法。的第二个目标 提案将首先集中于更新人类MPI扫描仪的硬件，以利用SPION的敏感性 需要执行静态fMPI实验，然后验证稳定和灵敏的体模 图像的时间序列，以检测血流动力学波动，占主导的生理“噪音”。然后我会验证 在猕猴模型中执行fMPI皮质关联网络表征的能力，其中有广泛的选择 可提供Spion示踪剂。第三个目标是在人体内进行rS-fmpi的特性研究。 该提案的成功完成将决定MPI作为一种新颖和高度敏感的技术的潜力 功能连接映射模式提高了我们发现个体大脑网络变化的能力 精神病患者。

PROJECT SUMMARY/ABSTRACT: Since the development of functional MRI (fMRI)-based neuroimaging studies thirty years ago, extensive work has been undertaken to describe the intrinsic connectivity of the human brain, both in response to external stimulus and in resting-state. There have been only modest improvements in the contrast-to-noise ratio (CNR) of fMRI, and resultingly, there have been limited clinical applications of fMRI, as the CNR of fMRI limits its ability for individual-based functional neuroimaging. Magnetic particle imaging (MPI) is a novel imaging modality that capitalizes on the large magnetic moment of injected super-paramagnetic iron oxide nanoparticles (SPIONs) for an improved CNR. By harnessing the increased CNR of MPI, this proposal aims to validate the ability of fMPI to detect resting-state CBV fluctuations and use these signals to map functional connectivity networks in analogy to resting-state fMRI (rs- fMRI) correlation-based connectivity mapping. Recent work in our group has demonstrated the ability to detect MPI-based physiological noise signals in a rodent model that scale with the dose of the injected magnetic SPION. Building off this work and an on-going effort to construct a human MPI scanner, this proposal will characterize the signal fluctuations in resting-state MPI tomographic time-series data and test the ability to generate functional connectivity mappings from these signals. My central hypothesis is that armed with these prototype scanners and an increased focus on signal sensitivity, the ratio of the physiological component of the signal leveraged in correlational mapping to other, non-neuronally-generated fluctuations will be higher in MPI than in conventional blood-oxygen-level-dependent (BOLD) fMRI and more robust network maps will result. I plan to generate the first MPI-based resting-state functional brain connectivity maps, compare to rs-fMRI, and potentially demonstrate improved sensitivity to uncover subtle network alterations, thus potentially advancing our ability to understand and phenotype individual patients with neuropsychiatric disease. In this proposal, I will first demonstrate the ability to characterize cortical association networks in rodents using a small-bore MPI scanner, after improving existing image reconstruction methods. The second aim of the proposal will focus first on updating the hardware of the human MPI scanner to harness the sensitivity of SPIONs necessary to perform resting-state fMPI experiments and then on validating a stable and sensitive phantom image time-series to detect hemodynamic fluctuations that dominate the physiologic “noise”. I will then validate the ability to perform fMPI cortical association network characterization in a macaque model where a wide choice of SPION tracer is available. The third aim is focused on performing rs-fMPI characterization studies in humans. The successful completion of the proposal will determine the potential of MPI as a novel and highly sensitive functional connectivity mapping modality advancing our ability to uncover altered brain networks in individual patients with mental illness.