Multimodal Resting State Network Tools

描述(由申请人提供)： 我们解决了如何将脑电图和脑磁图(EMEG)与非并发采集的功能磁共振成像(FMRI)数据相结合的临床和科学上的重要问题，以非侵入性地研究从0.08赫兹到100赫兹以上的时间频率范围内的大规模静息状态脑网络。低频血流动力学信号与空间模糊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，这一应用中的新方法正在解决这一问题。这些方法利用在静息fMRI中发现的动态相关性的空间结构来提供EMEG逆技术，该技术进而利用正在进行的大脑活动的高度随机性质。基于fMRI连通性的大脑皮层分割将被用来提供两种互补的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，而对偶向量波束形成器(DVB)提供对两个源区域可能相关的活动的局部估计。MaxEntCov和DVB从相反的角度出发，在旨在提供收敛解的迭代过程中组合以优化模型参数。在收敛之后，估计器可用于导出各种基于EMEG的功能连通性度量。我们的计算方法将使用准现实模拟进行验证。研究效用将使用一个正在进行的项目的数据进行评估，该项目旨在研究向精神病转化的过程。最后，原型多模式静息状态分析工具将与商业EMSE(R)Suite软件集成。 公共卫生相关性： 基于低频功能磁共振成像的静息状态连接性研究在基础神经科学和翻译神经科学的应用中取得了丰硕的成果。神经生理学指标(脑电和脑磁图)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。这个问题是通过本申请中描述的新的计算方法来解决的，该方法使用MRI来促进来自神经生理学大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。我们的目标的成功实现将证明多模式休眠状态网络工具是可行的、有用的，并为商业开发做好了准备。

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.