Connectome 2.0: Developing the next generation human MRI scanner for bridging studies of the micro-, meso- and macro-connectome

摘要 我们介绍了Connectome 2.0，下一代人类MRI扫描仪，用于成像结构解剖和 跨越微观、中观和宏观尺度的连通性。这项工作是建立在我们的 设计第一台最大梯度强度为300 mT/m的人体Connectome MRI扫描仪的专业知识 (GMAX)，人类系统有史以来达到的最高水平，人类连接组项目(HCP)。的目标是 HCP是为了绘制健康成人大脑在活体内的宏观结构联系 扩散束造影术。虽然这一文书对我们理解 宏观连接拓扑，我们在过去七年使用扫描仪的经验教会了我们 专用的高梯度性能扫描仪还可以获取丰富的扩散测量结果 为神经组织微结构提供无与伦比的在体评估，如相对大小和 细胞和轴突的堆积密度。然而，当前的Connectome仪器在其能力方面受到限制 解析探索大脑微观和中观结构所需的全部长度尺度， 由于基本的设计限制、重要的技术元素，以及与大型生物的快速交互 切换渐变。我们使用第一代Connectome扫描仪的经验及其实现 限制促使我们提出下一代人类Connectome核磁共振扫描仪的多站点计划 (Connectome 2.0)以实现对更广泛的细胞和轴突大小、形态、 大脑各处都有相互联系。 我们的目标是将我们最初的经验转化为构建一种独一无二的高转换率、超 专为研究神经组织微结构和神经功能而优化的高梯度强度磁共振扫描仪 跨越多个长度尺度的电路。为了最大限度地提高活体显微镜的分辨率以供研究 对于活着的人脑，我们将把扩散分辨率极限推向前所未有的水平：(1)近 将目前的GMAX提高一倍至500Mt/m，将最大回转速度提高两倍至600T/m/S；(2)突破限制 的射频接收线圈和梯度特性，以实现最大的灵敏度，同时大大降低 使用实时涡流校正dMRI采集的伪影；(3)开发新的脉冲序列以 达到体内有史以来最高的扩散和空间分辨率；以及(4)校准 通过系统验证扩散获得的下一代仪器的测量结果 高保真模体和体外脑组织中的微结构度量逐渐精细。我们 设想创造能够满足大脑2025年成像任务的终极扩散磁共振成像机器 从探索细胞异质性和可塑性所需的微观尺度，到 用于列举定义细胞和细胞的皮质结构和连接性差异的介观尺度 骨髓结构边界，到宏观连通性估计的改进。

SUMMARY We present Connectome 2.0, the next-generation human MRI scanner for imaging structural anatomy and connectivity spanning the microscopic, mesoscopic and macroscopic scales. This work builds upon our expertise in engineering the first human Connectome MRI scanner with 300 mT/m maximum gradient strength (Gmax), the highest ever achieved for a human system, for the Human Connectome Project (HCP). The goal of the HCP was to map the macroscopic structural connections of the in vivo healthy adult human brain using diffusion tractography. While this instrument has made important contributions to our understanding of macroscale connectional topology, our experience with the scanner over the last seven years has taught us that dedicated high-gradient performance scanners can also acquire a rich array of diffusion measurements that provide unparalleled in vivo assessment of neural tissue microstructure, such as the relative size and packing density of cells and axons. However, the current Connectome instrument is limited in its ability to resolve the full range of length scales needed to probe the microscopic and mesoscopic structure of the brain, due to basic design limitations, important technical elements, and biological interactions with the large rapidly switching gradients. Our experience with the first generation Connectome scanner and realization of its limitations motivates our multi-site proposal for the next generation human Connectome MRI scanner (Connectome 2.0) to achieve sensitivity to a broader range of cellular and axonal size scales, morphologies, and interconnections represented throughout the brain. Our goal here is to translate our initial experience into building a one-of-a-kind high-slew rate, ultra- high-gradient strength MRI scanner that is optimized for the study of neural tissue microstructure and neural circuits across multiple length scales. In order to maximize the resolution of this in vivo microscope for studies of the living human brain, we will push the diffusion resolution limit to unprecedented levels by (1) nearly doubling the current Gmax to 500 mT/m and tripling the maximum slew rate to 600 T/m/s; (2) pushing the limits of the RF receive coils and gradient characterization to enable maximum sensitivity with greatly reduced artifacts using real-time eddy current corrected dMRI acquisitions; (3) developing new pulse sequences to achieve the highest diffusion- and spatial-resolution ever achieved in vivo; and (4) calibrating the measurements obtained from this next generation instrument through systematic validation of the diffusion microstructural metrics in high-fidelity phantoms and ex vivo brain tissue at progressively finer scales. We envision creating the ultimate diffusion MRI machine capable of addressing the BRAIN 2025 mandate to image across scales, from the microscopic scale needed to probe cellular heterogeneity and plasticity, to the mesoscopic scale for enumerating the distinctions in cortical structure and connectivity that define cyto- and myeloarchitechtonic boundaries, to improvements in estimates of macroscopic connectivity.