Multimodal Resting State Network Tools

描述（由申请人提供）： 我们解决了临床和科学上的重要问题，如何整合脑电图和脑磁图（EMEG）与非同时获得的功能磁共振成像（fMRI）数据的非侵入性研究的大规模，静息状态的大脑网络的时间频率范围从0.08 Hz到超过100 Hz。低频血流动力学信号与空间模糊的电磁信号的统一分析一直是这一领域取得进展的关键障碍，并且在本申请中通过新方法来解决。这些方法利用在静息功能磁共振成像中发现的动态相关性的空间结构来通知EMEG逆技术，该技术反过来利用正在进行的大脑活动的高度随机性。基于fMRI连通性的皮质包裹将用于告知两种互补的EMEG源估计方法：最大熵源协方差源估计器（MaxEntCov）提供全局一致的解决方案，并且双矢量波束形成器（DVB）提供两个源区域中可能相关的活动的局部估计。MaxEntCov和DVB从相反的角度出发，结合起来，在迭代过程中优化模型参数，旨在提供收敛的解决方案。在收敛之后，估计器可以用于导出各种基于EMG的功能连接性测量。我们的计算方法将使用准现实模拟进行验证。研究效用将使用正在进行的项目的数据进行评估，以研究转化为精神病的过程。最后，原型多模态静止状态分析工具将与商业EMSE（R）套件软件集成。 公共卫生相关性： 静息态连接的低频功能磁共振成像研究已在基础和翻译神经科学的应用富有成效。神经生理学测量（EEG和MEG）反映了更广泛的大脑活动的动态范围，但在大脑中的定位不明确。该问题通过本申请中描述的新的计算方法来解决，该方法使用MRI来促进来自神经生理学脑活动的两个互补估计器的证据的收敛。原型计算工具将在精神病学研究环境中进行评估。我们的目标的成功完成将证明多模式静息态网络工具是可行的，有用的，并准备商业开发。

DESCRIPTION (provided by applicant): We address the clinically and scientifically important problem of how to integrate electroencephalography and magnetoencephalography (EMEG) with non-concurrently acquired functional magnetic resonance imaging (fMRI) data for the noninvasive study of large-scale, resting-state brain networks in temporal frequencies ranging from 0.08 Hz to more than 100 Hz. The unified analysis of low frequency hemodynamic signals with spatially ambiguous electromagnetic signals has been a critical barrier to progress in this field, and is addressed by novel methods in this application. These methods exploit the spatial structure of dynamic correlations found in resting fMRI to inform an EMEG inverse technique which, in turn, exploits the highly stochastic nature of ongoing brain activity. Cortical parcellations based on fMRI connectivity will be used to inform two complementary EMEG source estimation methods: A maximum entropy source covariance source estimator (MaxEntCov) provides a globally consistent solution, and a dual vector beamformer (DVB) provides local estimates for possibly- correlated activity in two source regions. MaxEntCov and DVB, which start from opposite perspectives, are combined to optimize model parameters in an iterative process that is designed to provide a convergent solution. After convergence, the estimators may be used to derive various EMEG-based functional connectivity measures. Our computational methods will be verified using quasi-realistic simulations. Research utility will be evaluated using data from an ongoing project to study the process of conversion to the psychosis. Finally, the prototype multimodal resting-state analysis tools will be integrated with commercial EMSE(R) Suite software. PUBLIC HEALTH RELEVANCE: Resting-state connectivity studies using low frequency fMRI have been fruitful in basic and translational neuroscience applications. Neurophysiological measures (EEG and MEG) reflect a much wider dynamic range of brain activity, but suffer from ambiguous localization in the brain. This problem is addressed by novel computational methods described in this application, which use MRI to facilitate a convergence of evidence from two complementary estimators of neurophysiological brain activity. Prototype computational tools will be evaluated in a psychiatric research setting. Successful completion of our aims will demonstrate that multimodal resting state network tools are feasible, useful, and ready for commercial development.