Quantifying human gray matter microstructure using neurite exchange imaging (NEXI) and 300 mT/m gradients

Quentin Uhl, Tommaso Pavan, Malwina Molendowska, Derek K. Jones, Marco Palombo, Ileana Jelescu
{"title":"Quantifying human gray matter microstructure using neurite exchange imaging (NEXI) and 300 mT/m gradients","authors":"Quentin Uhl, Tommaso Pavan, Malwina Molendowska, Derek K. Jones, Marco Palombo, Ileana Jelescu","doi":"10.1162/imag_a_00104","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"10 6","pages":"1-19"},"PeriodicalIF":0.0000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Imaging Neuroscience","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1162/imag_a_00104","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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
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。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Optimization and validation of multi-echo, multi-contrast SAGE acquisition in fMRI BOLD fMRI responses to amplitude-modulated sounds across age in adult listeners Developmental trajectories of the default mode, frontoparietal, and salience networks from the third trimester through the newborn period GABA levels decline with age: A longitudinal study Unveiling hidden sources of dynamic functional connectome through a novel regularized blind source separation approach
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1