A multi-modal framework for characterizing myelin microstructure

用于表征髓鞘微结构的多模式框架

基本信息

  • 批准号:
    RGPIN-2016-06774
  • 负责人:
  • 金额:
    $ 1.75万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Discovery Grants Program - Individual
  • 财政年份:
    2016
  • 资助国家:
    加拿大
  • 起止时间:
    2016-01-01 至 2017-12-31
  • 项目状态:
    已结题

项目摘要

Magnetic resonance imaging (MRI) shows great promise for characterizing brain microstructure during development, aging, disease, and treatment. However, with its current resolution (on the order of millimeters), MRI remains a blunt tool that provides only a birds-eye view of the complex network of fibers (on the order of micrometers) that make up the bulk of white matter in the brain. The last ten years have seen tremendous advances in the field of quantitative magnetic resonance imaging (qMRI), enabling us to glean microstructural information on a scale that is orders of magnitude smaller than the native MRI resolution. Being able to do so will increase our understanding of brain development and become an invaluable tool for diagnosis and treatment of neurodegenerative disorders. The fibers in white matter consist of axons, most of which are wrapped in a myelin sheath that enables fast conduction of information. I have recently proposed a novel biophysical model of white matter relating the microstructural features, such as the relative myelin thickness (g­ratio), to macroscopic quantities such as the myelin volume fraction (MVF) and the axon volume fraction (AVF). Other researchers have independently confirmed the theoretical soundness of the model, so the next step is to use this model to increase the specificity of MRI in characterizing myelin microstructure. I propose combining quantitative MRI biomarkers to make it possible to decouple the contribution of axons from the contribution of myelin to the MR signal. Incorporating the individual MR biomarkers in a multi­modal framework will greatly increase the specificity of MRI to the microstructural features of myelin, making it possible to measure for the first time the myelin thickness (g-ratio) non-invasively. The methodology will be tested in mouse demyelination models, before translating it to clinical studies in multiple sclerosis. Measuring myelin thickness in multiple sclerosis will give neurologists a real-time tool for tracking the progression of a lesion during demyelination and remyelination, which would be extremely valuable in the development and evaluation of new therapeutic agents that promote remyelination. Along the way, many small discoveries will be made that lead toward better and more relevant quantitative MR protocols. The multi­modal framework proposed in this grant will contribute to expanding the qMRI toolbox, giving scientists new tools to attack a range of basic science and clinical questions related to brain microstructure.
磁共振成像(MRI)显示了在发育、衰老、疾病和治疗过程中表征大脑微观结构的巨大希望。然而,以目前的分辨率(毫米级),核磁共振成像仍然是一个迟钝的工具,只能提供复杂的纤维网络(微米级)的鸟瞰图,这些纤维网络构成了大脑中大部分的白质。在过去的十年里,定量磁共振成像(qMRI)领域取得了巨大的进步,使我们能够在比原生MRI分辨率小几个数量级的尺度上收集微观结构信息。能够做到这一点将增加我们对大脑发育的理解,并成为诊断和治疗神经退行性疾病的宝贵工具。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Stikov, Nikola其他文献

Vendor-neutral sequences and fully transparent workflows improve inter-vendor reproducibility of quantitative MRI
  • DOI:
    10.1002/mrm.29292
  • 发表时间:
    2022-06-03
  • 期刊:
  • 影响因子:
    3.3
  • 作者:
    Karakuzu, Agah;Biswas, Labonny;Stikov, Nikola
  • 通讯作者:
    Stikov, Nikola
The Myelin-Weighted Connectome in Parkinson's Disease.
  • DOI:
    10.1002/mds.28891
  • 发表时间:
    2022-04
  • 期刊:
  • 影响因子:
    8.6
  • 作者:
    Boshkovski, Tommy;Cohen-Adad, Julien;Misic, Bratislav;Arnulf, Isabelle;Corvol, Jean-Christophe;Vidailhet, Marie;Lehericy, Stephane;Stikov, Nikola;Mancini, Matteo
  • 通讯作者:
    Mancini, Matteo
Promise and pitfalls of g-ratio estimation with MRI
  • DOI:
    10.1016/j.neuroimage.2017.08.038
  • 发表时间:
    2018-11-15
  • 期刊:
  • 影响因子:
    5.7
  • 作者:
    Campbell, Jennifer S. W.;Leppert, Ilana R.;Stikov, Nikola
  • 通讯作者:
    Stikov, Nikola
Cross-relaxation imaging of human articular cartilage.
人类关节软骨的交叉解释成像。
  • DOI:
    10.1002/mrm.22865
  • 发表时间:
    2011-09
  • 期刊:
  • 影响因子:
    3.3
  • 作者:
    Stikov, Nikola;Keenan, Kathryn E.;Pauly, John M.;Smith, R. Lane;Dougherty, Robert F.;Gold, Garry E.
  • 通讯作者:
    Gold, Garry E.
Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI.
  • DOI:
    10.1016/j.neuroimage.2022.119327
  • 发表时间:
    2022-08-15
  • 期刊:
  • 影响因子:
    5.7
  • 作者:
    Maffei, Chiara;Girard, Gabriel;Schilling, Kurt G.;Aydogan, Dogu Baran;Adluru, Nagesh;Zhylka, Andrey;Wu, Ye;Mancini, Matteo;Hamamci, Andac;Sarica, Alessia;Teillac, Achille;Baete, Steven H.;Karimi, Davood;Yeh, Fang-Cheng;Yildiz, Mert E.;Gholipour, Ali;Bihan-Poudec, Yann;Hiba, Bassem;Quattrone, Andrea;Quattrone, Aldo;Boshkovski, Tommy;Stikov, Nikola;Yap, Pew-Thian;de Luca, Alberto;Pluim, Josien;Leemans, Alexander;Prabhakaran, Vivek;Bendlin, Barbara B.;Alexander, Andrew L.;Landman, Bennett A.;Canales-Rodriguez, Erick J.;Barakovic, Muhamed;Rafael-Patino, Jonathan;Yu, Thomas;Rensonnet, Gaetan;Schiavi, Simona;Daducci, Alessandro;Pizzolato, Marco;Fischi-Gomez, Elda;Thiran, Jean-Philippe;Dai, George;Grisot, Giorgia;Lazovski, Nikola;Puch, Santi;Ramos, Marc;Rodrigues, Paulo;Prckovska, Vesna;Jones, Robert;Lehman, Julia;Haber, Suzanne N.;Yendiki, Anastasia
  • 通讯作者:
    Yendiki, Anastasia

Stikov, Nikola的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Stikov, Nikola', 18)}}的其他基金

Improving reproducibility in MRI with vendor-neutral acquisitions and transparent workflows
通过供应商中立的采集和透明的工作流程提高 MRI 的可重复性
  • 批准号:
    RGPIN-2022-05308
  • 财政年份:
    2022
  • 资助金额:
    $ 1.75万
  • 项目类别:
    Discovery Grants Program - Individual
A multi-modal framework for characterizing myelin microstructure
用于表征髓鞘微结构的多模式框架
  • 批准号:
    RGPIN-2016-06774
  • 财政年份:
    2020
  • 资助金额:
    $ 1.75万
  • 项目类别:
    Discovery Grants Program - Individual
A multi-modal framework for characterizing myelin microstructure
用于表征髓鞘微结构的多模式框架
  • 批准号:
    RGPIN-2016-06774
  • 财政年份:
    2019
  • 资助金额:
    $ 1.75万
  • 项目类别:
    Discovery Grants Program - Individual
A multi-modal framework for characterizing myelin microstructure
用于表征髓鞘微结构的多模式框架
  • 批准号:
    RGPIN-2016-06774
  • 财政年份:
    2018
  • 资助金额:
    $ 1.75万
  • 项目类别:
    Discovery Grants Program - Individual
A multi-modal framework for characterizing myelin microstructure
用于表征髓鞘微结构的多模式框架
  • 批准号:
    RGPIN-2016-06774
  • 财政年份:
    2017
  • 资助金额:
    $ 1.75万
  • 项目类别:
    Discovery Grants Program - Individual

相似国自然基金

基于异构医学影像数据的深度挖掘技术及中枢神经系统重大疾病的精准预测
  • 批准号:
    61672236
  • 批准年份:
    2016
  • 资助金额:
    64.0 万元
  • 项目类别:
    面上项目

相似海外基金

Multi-modal insights of spatially distributed cells with associations of diseases and drug response
空间分布细胞与疾病和药物反应关联的多模式见解
  • 批准号:
    10714602
  • 财政年份:
    2023
  • 资助金额:
    $ 1.75万
  • 项目类别:
A scalable cloud-based framework for multi-modal mapping across single neuron omics, morphology and electrophysiology
一个可扩展的基于云的框架,用于跨单个神经元组学、形态学和电生理学的多模式映射
  • 批准号:
    10725550
  • 财政年份:
    2023
  • 资助金额:
    $ 1.75万
  • 项目类别:
Intelligent DEPression Tracking (I-DEPT) - A Novel, Longitudinal and Multi-Modal Machine Learning Framework for Quantifying Depression Symptoms
智能抑郁追踪 (I-DEPT) - 一种新颖的、纵向的、多模态的机器学习框架,用于量化抑郁症状
  • 批准号:
    10050419
  • 财政年份:
    2023
  • 资助金额:
    $ 1.75万
  • 项目类别:
    Collaborative R&D
A scalable integrated multi-modal single cell analysis framework for gene regulatory and cell-cell interaction networks
用于基因调控和细胞间相互作用网络的可扩展集成多模式单细胞分析框架
  • 批准号:
    2233887
  • 财政年份:
    2023
  • 资助金额:
    $ 1.75万
  • 项目类别:
    Continuing Grant
Multi-modal data integration to identify kinase substrates
多模式数据集成识别激酶底物
  • 批准号:
    10659156
  • 财政年份:
    2022
  • 资助金额:
    $ 1.75万
  • 项目类别:
Collaborative Research: SHIELD: Strategic Holistic Framework for Intrusion Prevention Using Multi-modal Data in Power Systems
合作研究:SHIELD:在电力系统中使用多模态数据进行入侵防御的战略整体框架
  • 批准号:
    2220346
  • 财政年份:
    2022
  • 资助金额:
    $ 1.75万
  • 项目类别:
    Standard Grant
Multi-modal data integration to identify kinase substrates
多模式数据集成识别激酶底物
  • 批准号:
    10451941
  • 财政年份:
    2022
  • 资助金额:
    $ 1.75万
  • 项目类别:
Fatigue INtelligent Discovery (FIND) - A Novel Machine Learning and Multi-Modal Fatigue Detection Framework
疲劳智能发现 (FIND) - 一种新颖的机器学习和多模式疲劳检测框架
  • 批准号:
    10042845
  • 财政年份:
    2022
  • 资助金额:
    $ 1.75万
  • 项目类别:
    Grant for R&D
A Multi-modal Teaching Framework for Robot Learning from Demonstration
机器人示范学习的多模态教学框架
  • 批准号:
    547015-2020
  • 财政年份:
    2022
  • 资助金额:
    $ 1.75万
  • 项目类别:
    Alexander Graham Bell Canada Graduate Scholarships - Doctoral
Collaborative Research: SHIELD: Strategic Holistic Framework for Intrusion Prevention Using Multi-modal Data in Power Systems
合作研究:SHIELD:在电力系统中使用多模态数据进行入侵防御的战略整体框架
  • 批准号:
    2220347
  • 财政年份:
    2022
  • 资助金额:
    $ 1.75万
  • 项目类别:
    Standard Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了