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。通过脑电图和脑磁图记录检测到的皮层振荡网络通常涉及多个频段，这表明多元（多频）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，流行的连通性测量，如相干性和相位同步，通常分析成对的空间位置，并考虑来自每个位置的单个数量，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。​鉴于脑电图数据中的信息丰富，使用单一频率方法进行分析，要么需要事先了解相互作用发生的频率，要么相反，需要进行大量测试，每种可能的相互作用类型都需要进行一次测试。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。与传统的相互作用分析方法相比，该项目正在自动识别对皮质网络有重要贡献的频带。目标应用侧重于视听语音整合效果。本提案中开发的多元方法被用于检测多感觉相互作用的皮质部位，并解释了不同刺激开始异步的视听语音刺激的不同程度的相位重置。这项研究将有助于在正常和病理大脑中检测振荡皮质网络。在包括癫痫、精神分裂症和阿尔茨海默病在内的一系列神经系统疾病中，已经报道了振荡性脑活动的变化，而检测振荡性活动和相关网络的存在和差异的改进方法将反过来促进对这些疾病的理解，并促进治疗干预措施的发展和评估。这一努力将工程师和神经科学家聚集在一起，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源软件“头脑风暴”向研究界分发。

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.