Development and validation of empirical models of the neuronal population activity underlying non-invasive human brain measurements

开发和验证非侵入性人脑测量中神经元群活动的经验模型

基本信息

  • 批准号:
    9975889
  • 负责人:
  • 金额:
    $ 75.07万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2016
  • 资助国家:
    美国
  • 起止时间:
    2016-09-22 至 2023-06-30
  • 项目状态:
    已结题

项目摘要

Project Summary / Abstract A major obstacle in the study of human brain function is that we currently have limited understanding of how the measurements made by different instruments, such as fMRI and EEG, relate to one another and to the underlying neuronal circuitry. Significant efforts have led to development of models within various specialist fields, but fragmentation has held us back from advancing our interpretation of the spatiotemporal characteristics of non-invasive imaging signals. Bringing together the various models that pertain to signal interpretation would constitute a significant advance in what we can learn from non-invasive neuroimaging. In this project we take such an integrative approach to the study of cortical sensory systems. We intend to develop a set of connecting, empirically driven models that will predict how sensory stimuli are encoded in neuronal population activity underlying electrophysiological measures (AIM 1), and hemodynamic measures (AIM 2), leading to a comprehensive integrative model (AIM 3). The pivotal integrative model (AIM 3) will improve our understanding of, and revolutionize the information we can obtain from fMRI, the modality with the highest potential for mapping detailed functions non-invasively in humans. To achieve this we will combine hemodynamic and electrophysiological measurements at multiple spatial scales in humans, and in rodents at very high resolutions. This will include non-invasive (fMRI at 3T and 7T, MEG and EEG) and invasive (optical imaging, ECoG) modalities obtained from healthy humans. By obtaining multiple modality recordings from the same individuals, using the same stimuli and tasks, we will be able to unequivocally link clear and specific electrophysiological information to widely used fMRI technology, while significantly improving our understanding of the electrical and hemodynamic phenomena underlying brain activity. The research constitutes a multicenter endeavor to A) develop a comprehensive model to link external inputs to neuronal population physiology to non-invasive imaging measures, B) obtain state of the art multimodal recordings from the same individuals in order to bridge modalities and inform the models, C) validate the models with data from multiple modalities (ECoG, fMRI, MEG/EEG, optical recordings) and brain systems (visual, somatosensory and motor), and D) make algorithms and data available to the neuroscience community to foster further development beyond the project's lifetime. Moreover, the research will foster reconciliation of different theories about the relation between electrophysiology and fMRI and will lead to `breakthroughs in understanding the dynamic activity of the human brain'. Such breakthroughs will be essential in improving disease models of the nervous system, which rely on inferences about neuronal population activity from non-invasive imaging of human brain activity.
项目概要/摘要 研究人脑功能的一个主要障碍是我们目前对 不同仪器(例如功能磁共振成像和脑电图)进行的测量如何相互关联以及与 潜在的神经元电路。巨大的努力导致了各种专家模型的开发 领域,但碎片化阻碍了我们推进对时空的解释 非侵入性成像信号的特征。 Bringing together the various models that pertain to signal interpretation would constitute a significant advance in what we can learn from non-invasive neuroimaging.在 在这个项目中,我们采用了这种综合方法来研究皮质感觉系统。我们打算 开发一组相互连接的、经验驱动的模型,用于预测感官刺激如何编码 电生理测量 (AIM 1) 和血流动力学测量的神经元群活动 (AIM 2), leading to a comprehensive integrative model (AIM 3).关键综合模型(AIM 3)将 提高我们对功能磁共振成像(fMRI)获取信息的理解,并彻底改变我们的信息。 非侵入性地绘制人类详细功能的最大潜力。为了实现这一目标,我们将结合 对人类和啮齿动物进行多个空间尺度的血流动力学和电生理学测量 very high resolutions.这将包括非侵入性(3T 和 7T 的功能磁共振成像、脑磁图和脑电图)和侵入性(光学 从健康人体获得的成像、ECoG)模式。 By obtaining multiple modality recordings from the same individuals, using the same stimuli and tasks, we will be able to unequivocally link clear and specific electrophysiological information to widely used fMRI technology, while significantly improving our 了解大脑活动背后的电和血流动力学现象。 该研究是一项多中心努力,A) 开发一个综合模型来链接外部 对神经元群体生理学的输入到非侵入性成像测量,B) 获得最先进的技术 multimodal recordings from the same individuals in order to bridge modalities and inform the models, C) validate the models with data from multiple modalities (ECoG, fMRI, MEG/EEG, optical recordings) and brain 系统(视觉、体感和运动),D) 为神经科学提供算法和数据 社区以促进项目生命周期之外的进一步发展。此外,该研究将促进 关于电生理学和功能磁共振成像之间关系的不同理论的调和将导致 `breakthroughs in understanding the dynamic activity of the human brain'.这种突破至关重要 改善神经系统疾病模型,该模型依赖于对神经元群体的推论 来自人类大脑活动的非侵入性成像的活动。

项目成果

期刊论文数量(26)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Moving in on human motor cortex. Characterizing the relationship between body parts with non-rigid population response fields.
  • DOI:
    10.1371/journal.pcbi.1009955
  • 发表时间:
    2022-04
  • 期刊:
  • 影响因子:
    4.3
  • 作者:
  • 通讯作者:
FMRI and intra-cranial electrocorticography recordings in the same human subjects reveals negative BOLD signal coupled with silenced neuronal activity.
  • DOI:
    10.1007/s00429-021-02342-4
  • 发表时间:
    2022-05
  • 期刊:
  • 影响因子:
    3.1
  • 作者:
    Fracasso, Alessio;Gaglianese, Anna;Vansteensel, Mariska J.;Aarnoutse, Erik J.;Ramsey, Nick F.;Dumoulin, Serge O.;Petridou, Natalia
  • 通讯作者:
    Petridou, Natalia
Laminar processing of numerosity supports a canonical cortical microcircuit in human parietal cortex.
数量的层流处理支持人类顶叶皮层中的典型皮层微电路。
  • DOI:
    10.1016/j.cub.2021.07.082
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    vanDijk,JelleA;Fracasso,Alessio;Petridou,Natalia;Dumoulin,SergeO
  • 通讯作者:
    Dumoulin,SergeO
A visual encoding model links magnetoencephalography signals to neural synchrony in human cortex.
  • DOI:
    10.1016/j.neuroimage.2021.118655
  • 发表时间:
    2021-12-15
  • 期刊:
  • 影响因子:
    5.7
  • 作者:
    Kupers ER;Benson NC;Winawer J
  • 通讯作者:
    Winawer J
The many layers of BOLD. The effect of hypercapnic and hyperoxic stimuli on macro- and micro-vascular compartments quantified by CVR, M, and CBV across cortical depth.
  • DOI:
    10.1177/0271678x221133972
  • 发表时间:
    2023-03
  • 期刊:
  • 影响因子:
    6.3
  • 作者:
    Schellekens, Wouter;Bhogal, Alex A.;Roefs, Emiel C. A.;Baez-Yanez, Mario G.;Siero, Jeroen C. W.;Petridou, Natalia
  • 通讯作者:
    Petridou, Natalia
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Orrin Devinsky其他文献

Orrin Devinsky的其他文献

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{{ truncateString('Orrin Devinsky', 18)}}的其他基金

Machine learning approaches for improving EEG data utility in SUDEP research
用于提高 SUDEP 研究中脑电图数据效用的机器学习方法
  • 批准号:
    10593406
  • 财政年份:
    2021
  • 资助金额:
    $ 75.07万
  • 项目类别:
Advancing SUDEP risk prediction using a multicenter case-control approach
使用多中心病例对照方法推进 SUDEP 风险预测
  • 批准号:
    10290017
  • 财政年份:
    2021
  • 资助金额:
    $ 75.07万
  • 项目类别:
Advancing SUDEP risk prediction using a multicenter case-control approach
使用多中心病例对照方法推进 SUDEP 风险预测
  • 批准号:
    10463739
  • 财政年份:
    2021
  • 资助金额:
    $ 75.07万
  • 项目类别:

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