An MRI Deep Brain Adult Template With An Advanced Atlas-Based Tool For Diffusion Tensor Imaging Analysis.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2024-11-01 DOI:10.1038/s41597-024-04053-x

Jean-Jacques Lemaire, Rémi Chaix, Aigerim Dautkulova, Anna Sontheimer, Jérôme Coste, Ana-Raquel Marques, Adrien Wohrer, Carine Chassain, Omar Ouachikh, Omar Ait-Ader, Denys Fontaine

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引用次数: 0

Abstract

Understanding the architecture of the human deep brain is especially challenging because of the complex organization of the nuclei and fascicles that support most sensorimotor and behaviour controls. There are scant dedicated tools to explore and analyse this region. Here we took a transdisciplinary approach to build a new deep-brain MRI architecture atlas drawing on advanced clinical experience of MRI-based deep brain mapping. This new tool comprises a young-male-adult MRI template spatially normalized to the ICBM152, containing T1, inversion-recovery, and diffusion MRI datasets (in vivo acquisition), and an MRI atlas of 118 labelled deep brain structures. It is open-source and gives users high resolution image datasets to describe nuclear-based and axonal architecture, combining pioneering and recent knowledge. It is a useful addition to current 3D atlases and clinical tools.

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磁共振成像脑深部成人模板与基于图集的高级扩散张量成像分析工具。

由于支持大多数感觉运动和行为控制的神经核和神经束组织复杂，了解人类深部大脑的结构尤其具有挑战性。探索和分析这一区域的专用工具很少。在此，我们采用跨学科方法，借鉴基于核磁共振成像的大脑深部绘图的先进临床经验，建立了一个新的大脑深部核磁共振成像结构图集。这一新工具包括一个空间归一化为 ICBM152 的年轻男性-成年人 MRI 模板，其中包含 T1、反转恢复和弥散 MRI 数据集（活体采集），以及一个包含 118 个标记的大脑深部结构的 MRI 图集。它是开源的，为用户提供了高分辨率的图像数据集，用于描述基于核和轴突的结构，并结合了前沿和最新的知识。它是对当前三维地图集和临床工具的有益补充。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.