Automatic Detection of Cortical Networks across Frequencies in Audiovisual Speech Integration

感知的大脑基础是复杂的，最近的研究表明，神经处理依赖于神经元单位的大规模振荡。用脑电图和脑磁图记录检测到的振荡皮层网络通常涉及几个频带，这表明多变量（多频率）分析方法在检测神经效应方面比单变量分析具有更好的灵敏度。然而，诸如相干性和相位同步的流行的连接性测量通常分析成对的空间位置，并且考虑来自每个位置的单个量，诸如指定频带内的幅度或相位。在美国国家科学基金会的资助下，Dimitrios Pantazis，Richard Leahy和Jintao Jiang博士将开发强大的多元统计方法，用于检测脑电图中的大脑相互作用。鉴于脑电图数据中的丰富信息，使用单频率方法进行分析需要预先了解相互作用发生的频率，或者相反，需要大量的测试，每种可能的相互作用类型一个。 在这个项目中，研究人员正在使用典型相关分析，它可以找到一个皮质部位的频率与另一个皮质部位的频率最佳相关的最佳组合。与传统的交互分析方法相比，该项目正在自动识别对皮层网络有重大贡献的频带。目标应用程序侧重于视听语音整合效果。在这个建议中开发的多变量方法被用来检测多感觉相互作用的皮层网站和帐户不同层次的相位重置视听语音刺激与不同的刺激发作muscle。这项研究将有助于在正常和病理大脑振荡皮层网络的检测。振荡脑活动的变化已被报道在广泛的神经系统疾病，包括癫痫，精神分裂症和阿尔茨海默氏病，和改进的方法来检测振荡活动和相关网络的存在和差异，反过来将推进对这些疾病的理解，并促进治疗干预措施的开发和评估。这一努力汇集了工程师和神经科学家，以解决广泛的科学和技术问题，因此，该项目为博士生的综合跨学科研究培训提供了机会。研究成果将通过专业会议和期刊向研究界广泛传播，开发的研究工具将通过开放源码软件BrainStorm分发给研究界。

The brain-basis of perception is complex, and recent research suggests that neural processing depends on large-scale oscillation of neuronal units. Oscillatory cortical networks detected with electroencephalography and magnetoencephalography recordings often involve several frequency bands, indicating that a multivariate (multi-frequency) analytic approach would have better sensitivity in detecting neural effects than univariate analysis. However, popular connectivity measures, such as coherence and phase synchrony, typically analyze pairs of spatial locations and take into account a single quantity from each location, such as amplitude or phase within a specified frequency band. With funding from the National Science Foundation, Drs. Dimitrios Pantazis, Richard Leahy, and Jintao Jiang will develop robust multivariate statistical methods for detecting brain interactions in electroencephalography. Given the wealth of information in electroencephalography data, analysis using a single frequency approach requires either prior knowledge of the frequencies at which interactions occur or, conversely, a large number of tests, one for each possible type of interaction. In this project, the researchers are using canonical correlation analysis, which can find the optimal combinations of frequencies in one cortical site that best correlate with frequencies at another cortical site. In contrast to conventional methods of interaction analysis, this project is automating the identification of frequency bands that contribute significantly to cortical networks. The target application focuses on audiovisual speech integration effects. The multivariate methods developed in this proposal are being used to detect multisensory interaction cortical sites and account for different levels of phase-resetting from audiovisual speech stimuli with different stimulus onset asynchronies. This research will facilitate the detection of oscillatory cortical networks both in the normal and pathologic brain. Changes in oscillatory brain activity have been reported in a wide array of neurological diseases, including epilepsy, schizophrenia, and Alzheimer's disease, and improved methodologies to detect the presence and differences in oscillatory activity and associated networks will in turn advance the understanding of these diseases and facilitate the development and assessment of therapeutic interventions. This effort brings together engineers and neuroscientists to tackle a broad range of scientific and technological problems, and as a result, the project offers opportunities for integrated interdisciplinary research training of doctoral students. Research results will be disseminated broadly to the research community through professional meetings and journals, and the developed research tools will be distributed to the research community through the open source software BrainStorm.