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:
    10043972
  • 负责人:
  • 金额:
    $ 46.69万
  • 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.
项目总结 了解健康或疾病大脑中的神经回路和信号需要新的工具来成像 在不同的尺度上具有卓越的时空精度的神经元活动和功能连接 神经细胞和微血管的个体和群体、神经回路和皮质 层/柱,以及介观(或层流)尺度的功能连接到宏观的神经网络 规模和神经系统水平。基于血液的功能磁共振成像(FMRI)- 氧合水平依赖(BOLD)造影剂在神经科学中占有重要地位,它是 唯一一种可以无创地将人类神经元的活动和动态变化映射到 神经计算单元级别,以及图像功能连通性和休眠状态网络(RSN) 覆盖整个大脑。然而,fMRI 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的活动。潜在神经元活动的推断 在功能磁共振成像中,BOLD信号可以在微观和介观尺度上受到许多未知因素的影响。 虽然人们对fMRI信号与神经元活动之间的相关性进行了大量的研究,但 BOLD信号的神经生理学来源及其在定位神经元活动和功能中的特异性 皮质板层水平的连通性仍然难以捉摸。 为了解决技术挑战和解决关键的神经成像和神经科学问题,我们有 组建了一个由超高场(UHF)功能磁共振成像和多光子显微镜专家组成的跨学科团队 来自两家研究机构的影像研究领域开发出全球首个完全兼容的MRI体积成像系统 双光子显微成像(VTPMI)系统,工作在最高野值的动物核磁共振扫描仪之一 16.4吨特斯拉。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有关的关键神经生理信息, 星形胶质细胞、不同大小的血管和超高分辨率的fMRI数据,从而能够描绘细胞和层- 活着的大脑中特定的神经元活动。在该项目中开发的VTPMI-fMRI技术将是 用来研究神经-血管相关性和静息状态fMRI BOLD信号的特异性 绘制麻醉和清醒大脑中特定层的功能连接图,特别强调 研究抑制性中间神经元的作用。这个项目的发现和知识将是 具有变革性,有利于在精细尺度上理解和解释人类fMRI粗放信号 基本计算单元。

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:
    $ 46.69万
  • 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:
    $ 46.69万
  • 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:
    $ 46.69万
  • 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:
    $ 46.69万
  • 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:
    $ 46.69万
  • 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:
    $ 46.69万
  • 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:
    $ 46.69万
  • 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:
    $ 46.69万
  • 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:
    10463737
  • 财政年份:
    2020
  • 资助金额:
    $ 46.69万
  • 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:
    $ 46.69万
  • 项目类别:

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