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

总结 我们介绍了Connectome 2.0，下一代人体MRI扫描仪，用于成像结构解剖学， 连通性跨越微观，介观和宏观尺度。这项工作建立在我们的 在设计第一台最大梯度强度为300 mT/m的人体Connectome MRI扫描仪方面的专业知识 （Gmax），人类系统有史以来达到的最高水平，用于人类连接组计划（HCP）。的目标 HCP将绘制体内健康成人大脑的宏观结构连接， 弥散纤维束成像虽然这一文书对我们理解 宏观连接拓扑结构，我们在过去七年的扫描仪经验告诉我们， 专用的高梯度性能扫描仪也可以获得丰富的扩散测量阵列， 其提供了对神经组织微结构的无与伦比的体内评估，例如相对大小和 细胞和轴突的堆积密度。然而，当前的Connectome仪器在以下方面的能力有限： 解决了探测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，重要的技术因素，以及与大规模的生物相互作用， 切换梯度。我们使用第一代Connectome扫描仪的经验及其实现 限制激发了我们对下一代人类Connectome MRI扫描仪的多站点提案 （连接组2.0），以实现更广泛的细胞和轴突大小尺度，形态， 以及大脑中的相互联系。 我们的目标是将我们的初步经验转化为建立一个独一无二的高转换速率，超 高梯度强度MRI扫描仪，优化用于神经组织微观结构和神经 电路跨越多个长度尺度。为了最大限度地提高这种活体显微镜的分辨率， 我们将把扩散分辨率极限推到前所未有的水平， 将电流Gmax加倍至500 mT/m，将最大转换速率加倍至600 T/m/s;（2）突破极限 RF接收线圈和梯度表征，以实现最大灵敏度， 使用实时涡流校正的dMRI采集伪影;（3）开发新的脉冲序列， 实现有史以来在体内实现的最高扩散和空间分辨率;以及（4）校准 通过扩散的系统验证， 显微结构指标在高保真幻影和离体脑组织在逐步精细的尺度。我们 设想创建能够解决大脑2025任务的最终扩散MRI机器，以成像 跨尺度，从探测细胞异质性和可塑性所需的微观尺度， 介观尺度，用于列举定义细胞和神经元的皮质结构和连接性的区别。 骨髓组织边界，改善宏观连通性的估计。

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.