Advancing simultaneous fMRI-multiphoton imaging technique to study brain function and connectivity across different scales at ultrahigh field

推进同步功能磁共振成像多光子成像技术,研究超高场下不同尺度的大脑功能和连接性

基本信息

  • 批准号:
    10463737
  • 负责人:
  • 金额:
    $ 55.69万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-09-30 至 2025-08-31
  • 项目状态:
    未结题

项目摘要

PROJECT SUMMARY Understanding the neural circuitry and signaling in health or diseased brain requires new tools that can image neuronal activity and functional connectivity with superior spatiotemporal precision across various scales from individual and population of neural cells and microvessel at microscopic scale, neural circuits and cortical layers/columns, and functional connectivity at mesoscopic (or laminar) scale to neural networks at macroscopic scale and the nervous system level. Functional magnetic resonance imaging (fMRI) based on the blood- oxygenation-level-dependent (BOLD) contrast has gained a prominent position in neuroscience, and it is the only neuroimaging modality that can noninvasively map human neuronal activity and dynamic change to the level of neural computational units, and image functional connectivity and resting-state networks (RSNs) covering the entire brain. However, the fMRI BOLD signal is determined by a complex interplay between vascular and metabolic changes, thus, indirectly reflecting neuronal activity. The inference of underlying neuronal activity on the fMRI BOLD signal can be affected by many unknown factors at microscopic and mesoscopic scales. Although great efforts have been made to study the correlation between fMRI signals and neuronal activity, the neurophysiology origin of the BOLD signal and its specificity in mapping neuronal activity and functional connectivity at cortical lamina level remains elusive. To tackle technical challenges and address critical neuroimaging and neuroscience questions, we have formed an interdisciplinary team with experts in the ultrahigh-field (UHF) fMRI and multi-photon microscopy imaging research fields from two research institutions to develop the world first MRI fully compatible volumetric two-photon microscopy imaging (VTPMI) system, which works in one of the highest field animal MRI scanners at 16.4T Tesla. This novel VTPMI-fMRI multimodal neuroimaging system will make it possible to simultaneously measure key neurophysiological information related to activities and dynamics of excitatory/inhibitory neurons, astrocytes, different sized vessels, and ultrahigh-resolution fMRI data, thus enables delineation of cell- and layer- specific neuronal activity in the living brain. The VTPMI-fMRI technology developed in this project will be employed to study the neuro-vascular correlation and the specificity of resting-state fMRI BOLD signals for mapping the layer-specific functional connectivity in anesthetized and awake brains, with particular emphasis on investigating the roles of inhibitory interneurons. The findings and knowledge from this project will be transformative and beneficial for understanding and interpreting the human fMRI BOLD signals at the fine scale of fundamental computational units.
项目摘要 了解健康或患病大脑中的神经回路和信号需要新的工具, 神经元活动和功能连接,在各种尺度上具有上级时空精度, 显微镜下神经细胞和微血管个体和群体、神经回路和皮质 层/列,以及介观(或层)尺度下的功能连接到宏观尺度下的神经网络 规模和神经系统水平。基于血液的功能性磁共振成像(fMRI)- 氧合水平依赖性(BOLD)对比在神经科学中获得了突出的地位,它是 唯一的神经成像方式,可以非侵入性地映射人类神经元活动和动态变化, 神经计算单元的水平,以及图像功能连接和静息态网络(RSN) 覆盖了整个大脑。然而,功能磁共振成像BOLD信号是由血管之间复杂的相互作用决定的, 和代谢变化,从而间接反映神经元活动。对潜在神经元活动的推断 在微观和介观尺度上,功能磁共振成像BOLD信号会受到许多未知因素的影响。 尽管已经做出了很大的努力来研究功能磁共振成像信号和神经元活动之间的相关性, BOLD信号的神经生理学起源及其在映射神经元活动和功能中的特异性 皮质层水平的连通性仍然难以捉摸。 为了应对技术挑战并解决关键的神经成像和神经科学问题,我们 与超高场(UHF)功能磁共振成像和多光子显微镜的专家组成了一个跨学科团队 来自两个成像研究领域的研究机构,开发出世界上第一个MRI完全兼容的体积 双光子显微成像(VTPMI)系统,可在最高场动物MRI扫描仪中工作 16.4T特斯拉这种新型的VTPMI-fMRI多模式神经成像系统将使同时 测量与兴奋性/抑制性神经元的活动和动力学相关的关键神经生理学信息, 星形胶质细胞,不同大小的血管,和超高分辨率的功能磁共振成像数据,从而使描绘细胞和层, 活体大脑中特定的神经元活动。本项目开发的VTPMI-fMRI技术将 用于研究神经血管相关性和静息态fMRI BOLD信号的特异性, 绘制麻醉和清醒大脑中的层特异性功能连接,特别强调 研究抑制性中间神经元的作用。该项目的成果和知识将 对于理解和解释精细尺度上的人类fMRI BOLD信号具有变革性和有益性 基本计算单位的概念

项目成果

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Wei Chen其他文献

Wei Chen的其他文献

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{{ truncateString('Wei Chen', 18)}}的其他基金

An ensemble deep learning model for tumor bud detection and risk stratification in colorectal carcinoma.
用于结直肠癌肿瘤芽检测和风险分层的集成深度学习模型。
  • 批准号:
    10564824
  • 财政年份:
    2023
  • 资助金额:
    $ 55.69万
  • 项目类别:
Establishing translational neuroimaging tools for quantitative assessment of energy metabolism and metabolic reprogramming in healthy and diseased human brain at 7T
建立转化神经影像工具,用于定量评估 7T 健康和患病人脑的能量代谢和代谢重编程
  • 批准号:
    10714863
  • 财政年份:
    2023
  • 资助金额:
    $ 55.69万
  • 项目类别:
SCH: New Advanced Machine Learning Framework for Mining Heterogeneous Ocular Data to Accelerate
SCH:新的先进机器学习框架,用于挖掘异构眼部数据以加速
  • 批准号:
    10601180
  • 财政年份:
    2022
  • 资助金额:
    $ 55.69万
  • 项目类别:
SCH: New Advanced Machine Learning Framework for Mining Heterogeneous Ocular Data to Accelerate
SCH:新的先进机器学习框架,用于挖掘异构眼部数据以加速
  • 批准号:
    10665804
  • 财政年份:
    2022
  • 资助金额:
    $ 55.69万
  • 项目类别:
Cellular Interactions in Vascular Calcification of Chronic Kidney Disease
慢性肾病血管钙化中的细胞相互作用
  • 批准号:
    10525401
  • 财政年份:
    2022
  • 资助金额:
    $ 55.69万
  • 项目类别:
Console Replacement and Upgrade of 9.4 Tesla Animal Instrument
9.4特斯拉动物仪控制台更换升级
  • 批准号:
    10414184
  • 财政年份:
    2022
  • 资助金额:
    $ 55.69万
  • 项目类别:
Deep-learning-based prediction of AMD and its progression with GWAS and fundus image data
基于 GWAS 和眼底图像数据的 AMD 及其进展的深度学习预测
  • 批准号:
    10226322
  • 财政年份:
    2020
  • 资助金额:
    $ 55.69万
  • 项目类别:
Advancing simultaneous fMRI-multiphoton imaging technique to study brain function and connectivity across different scales at ultrahigh field
推进同步功能磁共振成像多光子成像技术,研究超高场下不同尺度的大脑功能和连接性
  • 批准号:
    10043972
  • 财政年份:
    2020
  • 资助金额:
    $ 55.69万
  • 项目类别:
Advancing simultaneous fMRI-multiphoton imaging technique to study brain function and connectivity across different scales at ultrahigh field
推进同步功能磁共振成像多光子成像技术,研究超高场下不同尺度的大脑功能和连接性
  • 批准号:
    10268184
  • 财政年份:
    2020
  • 资助金额:
    $ 55.69万
  • 项目类别:
Deep-learning-based prediction of AMD and its progression with GWAS and fundus image data
基于 GWAS 和眼底图像数据的 AMD 及其进展的深度学习预测
  • 批准号:
    10056062
  • 财政年份:
    2020
  • 资助金额:
    $ 55.69万
  • 项目类别:

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