CRCNS: Bayesian Modeling of Interacting Time Series to Discover Cortical Networks Associated with Auditory Processing Dysfunction

CRCNS：交互时间序列的贝叶斯建模以发现与听觉处理功能障碍相关的皮层网络

• 批准号: 1607468
• 负责人: Adrian Lee
• 金额: $ 80万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1607468&HistoricalAwards=false
• 关键词: CRCNS; Bayesian; Modeling; Interacting; Time

Despite receiving normal audiological assessments, some listeners still complain to clinicians that they struggle to hear, particularly in noisy or crowded environments. However, systematic investigations into how the brain processes sound (and how it can go wrong) are lacking. The goal of this funded project is to apply novel statistical approaches to study patterns of activity in the brain while it processes sound in complex situations like when listening in a multi-talker environment.A wide variety of behavioral and electrophysiological responses will be collected under several different types of auditory stimulation. Behavioral data and physiological measures of brainstem response will be used to characterize individuals' hearing health in both monaural and binaural pathways, providing complementary information to their cortical magneto- and electroencephalography (M-EEG) responses during similar auditory tasks. Auditory attentional network connectivity will also be analyzed, to account for the neural underpinnings of aspects of auditory dysfunction such as the inability to maintain or switch attention between speakers. Using computationally-driven statistical approaches, flexible graphical model-based representations of high-dimensional time series will be learned, in order to characterize the auditory attentional network based on collected M-EEG data. Specifically, two computational aims will be tackled: 1) Construct Bayesian models to characterize dynamical cortical interactions at different spatial resolutions and 2) Develop models that infer connectivity structure at different canonical cortical rhythmic bands. This research program leverages the complementary expertise of the two investigators, bringing together auditory behavioral and systems neuroscience, with flexible and scalable statistical time series modeling approaches. Temporal structure is often ignored in big data analyses as well as in systems neuroscience, and this funded research will directly address this shortcoming by using a high-dimensional set of temporally continuous neural data. The cortical network discovered by this approach will enable neuroscientists to better understand the variability inherent in the auditory attentional network across both task types and individual differences in listening abilities.

尽管接受了正常的听力学评估，但一些听众仍然向临床医生抱怨他们很难听到，尤其是在嘈杂或拥挤的环境中。但是，对于缺乏大脑过程的声音（以及如何出错）的系统调查。这个资助的项目的目的是将新颖的统计方法应用于研究大脑中的活动模式，同时在复杂的情况下处理声音，例如在多对言语环境中聆听时。在几种不同类型的听觉刺激下，将收集多种行为和电生理反应。行为数据和脑干反应的生理测量方法将用于表征单声道和双耳途径中个体的听力健康，从而在类似的听觉任务中为其皮质磁磁和脑电图（M-EEG）反应提供了互补的信息。听觉注意力网络连接也将进行分析，以说明听觉功能障碍方面的神经基础，例如无法维持或在说话者之间保持注意力。使用计算驱动的统计方法，将学习基于高维时间序列的灵活图形模型表示，以根据收集的M-EEG数据来表征听觉注意网络。具体而言，将解决两个计算目的：1）构建贝叶斯模型以表征不同空间分辨率下的动力学皮质相互作用，并且2）开发模型来推断不同规范皮质节奏带的连接结构。该研究计划利用了两位研究人员的互补专业知识，将听觉行为和系统神经科学结合在一起，并使用灵活且可扩展的统计时间序列建模方法。大数据分析以及系统神经科学中通常会忽略时间结构，这项资助的研究将通过使用一组高维的时间连续神经数据直接解决这一缺点。通过这种方法发现的皮质网络将使神经科学家能够更好地了解任务类型的听觉注意网络中固有的可变性和听力能力的个体差异。