一种基于扩散的轴突频谱成像(AxSI)在体内绘制人脑轴突直径分布的方法。

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Neuroinformatics Pub Date : 2023-07-01 DOI:10.1007/s12021-023-09630-w
Hila Gast, Assaf Horowitz, Ronnie Krupnik, Daniel Barazany, Shlomi Lifshits, Shani Ben-Amitay, Yaniv Assaf
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引用次数: 0

摘要

在本文中,我们展示了一种称为轴突频谱成像(AxSI)的广义和简化管道,用于人脑轴突特征的体内估计。在体内和无创的情况下,对所有纤维系统的全脑轴突直径进行估计,将有助于探索大脑结构和功能关系的未知方面,重点是连通性和连接组分析。虽然轴突直径映射本身很重要,但它与传导速度的相关性将首次允许探索大脑内的信息传递机制。我们展示了轴突形态测量的各种众所周知的方面(例如,胼胝体轴突直径变化)以及其他较少探索的方面(例如,轴突直径为基础的上纵束分割成段)。此外,我们还为大量受试者创建了一个基于MNI的全脑平均轴突直径图,为未来探索轴突特性、连接体表征与大脑其他功能和行为方面之间的关系提供了参考基础。
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A Method for In-Vivo Mapping of Axonal Diameter Distributions in the Human Brain Using Diffusion-Based Axonal Spectrum Imaging (AxSI).

In this paper we demonstrate a generalized and simplified pipeline called axonal spectrum imaging (AxSI) for in-vivo estimation of axonal characteristics in the human brain. Whole-brain estimation of the axon diameter, in-vivo and non-invasively, across all fiber systems will allow exploring uncharted aspects of brain structure and function relations with emphasis on connectivity and connectome analysis. While axon diameter mapping is important in and of itself, its correlation with conduction velocity will allow, for the first time, the explorations of information transfer mechanisms within the brain. We demonstrate various well-known aspects of axonal morphometry (e.g., the corpus callosum axon diameter variation) as well as other aspects that are less explored (e.g., axon diameter-based separation of the superior longitudinal fasciculus into segments). Moreover, we have created an MNI based mean axon diameter map over the entire brain for a large cohort of subjects providing the reference basis for future studies exploring relation between axon properties, its connectome representation, and other functional and behavioral aspects of the brain.

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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
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