利用神经元交换成像(NEXI)和 300 mT/m 梯度量化人类灰质微观结构

Quentin Uhl, Tommaso Pavan, Malwina Molendowska, Derek K. Jones, Marco Palombo, Ileana Jelescu
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摘要

摘要 专门用于量化灰质微观结构的生物物理扩散模型正受到越来越多的关注。最近提出的两室神经元交换成像(NEXI)模型可以解释神经元、细胞外空间和跨细胞膜的交换。NEXI 参数估计需要多壳多扩散时间数据,迄今为止只在临床前磁共振成像(MRI)装置收集的动物数据上进行过实验。在这项工作中,使用 3 T Connectom MRI 系统和 300 mT/m 梯度实现了 NEXI 在体内人体皮层的转换,该系统可采集广泛的 b 值(0 - 7.5 ms/µm²),窗口涵盖短到中间的扩散时间(20 - 49 ms),适合特征交换时间(10 - 50 ms)。四种模型变体的微观结构估算:比较了 NEXI、NEXIdot(其扩展版增加了一个点区)以及校正对高 b 值信号影响特别大的里ician 噪声底(NEXIRM 和 NEXIdot,RM)的各自版本。在合成数据和人体活体数据中评估了每个模型变体估计值的可靠性。在后者中,比较了皮层微观结构估计值的受试者内(扫描-扫描)和受试者间的变异性。NEXIRM 更好的性能突出了在 NEXI 模型中纠正里氏偏差对获得人类皮层微结构参数准确估计值的重要性,以及 NEXI 框架对皮层微结构个体差异的敏感性。NEXI 在人类中的应用迈出了重要一步,为研究神经发育、衰老和各种神经退行性疾病开辟了新途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantifying human gray matter microstructure using neurite exchange imaging (NEXI) and 300 mT/m gradients
Abstract Biophysical models of diffusion tailored to quantify gray matter microstructure are gathering increasing interest. The two-compartment Neurite EXchange Imaging (NEXI) model has been proposed recently to account for neurites, extra-cellular space, and exchange across the cell membrane. NEXI parameter estimation requires multi-shell multi-diffusion time data and has so far only been implemented experimentally on animal data collected on a preclinical magnetic resonance imaging (MRI) set-up. In this work, the translation of NEXI to the human cortex in vivo was achieved using a 3 T Connectom MRI system with 300 mT/m gradients, that enables the acquisition of a broad range of b-values (0 – 7.5 ms/µm²) with a window covering short to intermediate diffusion times (20 – 49 ms) suitable for the characteristic exchange times (10 – 50 ms). Microstructure estimates of four model variants: NEXI, NEXIdot (its extension with the addition of a dot compartment), and their respective versions that correct for the Rician noise floor (NEXIRM and NEXIdot,RM) that particularly impacts high b-value signal, were compared. The reliability of estimates in each model variant was evaluated in synthetic and human in vivo data. In the latter, the intra-subject (scan-rescan) versus between-subjects variability of microstructure estimates was compared in the cortex. The better performance of NEXIRM highlights the importance of correcting for Rician bias in the NEXI model to obtain accurate estimates of microstructure parameters in the human cortex, and the sensitivity of the NEXI framework to individual differences in cortical microstructure. This application of NEXI in humans represents a significant step, unlocking new avenues for studying neurodevelopment, aging, and various neurodegenerative disorders.
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