Mapping the superficial white matter connectome of the human brain using ultra high resolution multi-contrast diffusion MRI

使用超高分辨率多重对比扩散 MRI 绘制人脑浅层白质连接组图

基本信息

批准号：
10623234
负责人：
NIKOLAOS MAKRIS
金额：
$ 81.15万
依托单位：
BRIGHAM AND WOMEN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2026-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10623234
关键词：
Address Aging Algorithms Alzheimer&apos s Disease Anatomy Architecture Atlases Auditory area Bipolar Disorder Brain Brain Diseases Brain Mapping Communities Complex Computer software Data Data Set Databases Diffusion Magnetic Resonance Imaging Disease Epilepsy Fascicle Fiber Generations Goals Health Human Huntington Disease Image Individual Iron Literature Location Machine Learning Magnetic Resonance Imaging Maps Methods Monkeys Morphology Myelin Names Neuroanatomy Ontology Play Relaxation Resolution Role Schizophrenia Shapes Source System Testing Tissues Tracer Translating Visual autism spectrum disorder comparative connectome deep learning design experimental study human subject improved in vivo inter-individual variation millimeter neurodevelopment novel novel strategies open source prevent tractography ultra high resolution white matter

项目摘要

Abstract In this 5-year R01 project entitled “Mapping the superficial white matter connectome of the human brain using ultra high resolution multi-contrast diffusion MRI,” we propose to create the first atlas of the human brain’s superficial white matter (SWM) using sub-millimeter ultra high resolution diffusion MRI (dMRI). The SWM is located between the deep white matter and the cortex. It plays an important role in neurodevelopment and aging, and it has been implicated in a large number of diseases including Alzheimer’s, Huntington’s, epilepsy, autism spectrum disorder, schizophrenia, and bipolar disorder. Despite its significance in health and disease, the SWM is vastly underrepresented in current descriptions of the human brain connectome. The SWM contains short, u- shaped association fiber bundles called u-fibers. Multiple challenges have thus far prevented comprehensive mapping of the human brain’s SWM. These challenges include the inadequate spatial resolution of dMRI data, which prevents u-fiber tracing using current tractography methods, as well as the small size, high curvature, and high inter-subject variability of the u-fibers. An additional challenge is the lack of ground truth information. Our understanding of human neuroanatomy relies heavily on the results of invasive tracer studies in monkeys, but the detailed neuroanatomy of the SWM in monkeys has not yet been systematically compiled or analyzed. We propose to address these challenges to create the most comprehensive description of the SWM to date. Our strategy includes using ultra high spatial resolution dMRI acquisitions (~700µm isotropic or better) at multiple echo times (TE), novel dMRI tractography methods designed for tracing u-fibers, anatomically informed machine learning to parcellate the u-fibers, and expert neuroanatomical generation of the SWM connectivity matrix from monkey tracer studies. Furthermore, we will develop a novel ontological framework to organize and name the SWM systems of the monkey and human brains. Overall, these steps will enable robust in-vivo tracing and capturing of inter-subject variability of the SWM of the human brain at an unprecedented spatial resolution. Our proposed deliverables will be the first comprehensive, anatomically curated atlases of the SWM in human and monkey, which will enable the study of the SWM in health and disease. We will publicly release all image data, tractography atlases, monkey connectivity matrices, extracted fascicles, and all software as open source.

抽象的在这个为期 5 年的 R01 项目中，题为“利用超高分辨率多重对比扩散 MRI”，我们建议创建第一张人类大脑图谱使用亚毫米超高分辨率扩散 MRI (dMRI) 的浅表白质 (SWM)。位于深层白质和皮质之间，在神经发育和衰老中起着重要作用，它与许多疾病有关，包括阿尔茨海默氏症、亨廷顿舞蹈症、癫痫症、自闭症尽管 SWM 在健康和疾病方面具有重要意义，但它仍然适用于谱系障碍、精神分裂症和双相情感障碍。在目前对人类大脑连接组的描述中，SWM 包含短的 u-。迄今为止，多种挑战阻碍了一种称为 u 纤维的形状关联纤维束的全面开发。这些挑战包括 dMRI 数据的空间分辨率不足、防止使用当前的纤维束追踪方法以及小尺寸、高曲率和 u 纤维的高受试者间变异性另一个挑战是缺乏真实信息。对人类神经解剖学的理解在很大程度上依赖于对猴子进行侵入性示踪剂研究的结果，但是猴子 SWM 的详细神经解剖学尚未得到系统的整理或分析。我们建议应对这些挑战，创建迄今为止最全面的 SWM 描述。我们的策略包括在多个条件下使用超高空间分辨率 dMRI 采集（~700μm 各向同性或更好）回波时间 (TE)、专为追踪 u 纤维而设计的新型 dMRI 纤维束成像方法、解剖学信息机器学习分割 u 纤维，并通过专家神经解剖学生成 SWM 连接矩阵此外，我们将开发一种新颖的本体论框架来组织和命名猴子追踪研究。总体而言，这些步骤将使猴子和人类大脑的 SWM 系统能够实现强大的体内追踪和分析。以前所未有的空间分辨率捕获人脑 SWM 的受试者间变异性。拟议的交付成果将是第一个全面的、解剖学上整理的人类和动物 SWM 地图集。猴子，这将使 SWM 在健康和疾病方面的研究成为可能。我们将公开发布所有图像数据，纤维束成像图集、猴子连接矩阵、提取的分册以及所有开源软件。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Accelerating joint relaxation-diffusion MRI by integrating time division multiplexing and simultaneous multi-slice (TDM-SMS) strategies.

通过整合时分复用和同时多切片 (TDM-SMS) 策略来加速关节松弛扩散 MRI。

DOI：
发表时间：
2022-06
期刊：
影响因子：
3.3
作者：
Ji, Yang;Hoge, W Scott;Gagoski, Borjan;Westin, Carl;Rathi, Yogesh;Ning, Lipeng
通讯作者：
Ning, Lipeng

Reduced cross-scanner variability using vendor-agnostic sequences for single-shell diffusion MRI.

使用与供应商无关的序列进行单壳扩散 MRI，减少跨扫描仪的变异性。

DOI：
发表时间：
2024-03-12
期刊：
影响因子：
3.3
作者：
Liu, Qiang;Ning, Lipeng;Shaik, Imam Ahmed;Liao, Congyu;Gagoski, Borjan;Bilgic, Berkin;Grissom, William;Nielsen, Jon;Zaitsev, Maxim;Rathi, Yogesh
通讯作者：
Rathi, Yogesh

DDParcel: Deep Learning Anatomical Brain Parcellation From Diffusion MRI.

DDParcel：来自扩散 MRI 的深度学习解剖脑分区。

DOI：
发表时间：
2024-03
期刊：
影响因子：
10.6
作者：
Zhang, Fan;Cho, Kang Ik Kevin;Seitz;Ning, Lipeng;Legarreta, Jon Haitz;Rathi, Yogesh;Westin, Carl;O'Donnell, Lauren J;Pasternak, Ofer
通讯作者：
Pasternak, Ofer

A review and experimental evaluation of deep learning methods for MRI reconstruction.

MRI 重建深度学习方法的回顾和实验评估。