CRCNS: Collaborative Research: Dynamic Models of Human Auditory Perceptual Switching Informed by Large-Scale ECoG Recordings

CRCNS：协作研究：大规模 ECoG 记录提供的人类听觉感知切换动态模型

• 批准号: 1514556
• 负责人: Bingni Brunton
• 金额: $ 14.56万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1514556&HistoricalAwards=false
• 关键词: CRCNS; Collaborative; Research; Dynamic; Models

Sounds in natural environments are complex mixtures from many different sources. This project seeks to understand how humans organize mixtures of sounds into meaningful objects. Perceptions of auditory objects arise not from any particular part of the brain, but rather from coordinated activity across many brain regions; further, binding of sounds to auditory objects may switch very rapidly. Therefore, the study of how auditory objects are formed and how rapid switching occurs requires analyzing recordings of brain activity in humans across many brain areas and at very high speed. This project aims to develop new theoretical methods for integrating and analyzing complex dynamic data sets of brain recordings from large-scale electrode arrays. The modeling approach will provide insight in the understanding of human auditory perception in both normal and clinically impaired minds. Significant advances have been made in the past three decades characterizing neural correlates of auditory perceptions localized to the auditory cortex. Nevertheless, these neural correlates are likely not restricted to the auditory cortex, or to any particular part of the brain. To understand the neural mechanisms of auditory perceptual representation and perceptual switching, the current project combines advances in both experimental design and theory. Large-scale electrocorticography (ECoG) recordings will be collected from human subjects as they self-report their perceptions during a bistable auditory task involving rapid perceptual switching. Next, spatial-temporal patterns of cortical activation during the task will be extracted from these large time-series datasets using a data-driven method novel to neuroscience known as dynamic mode decomposition (DMD). Features extracted by DMD will then be used to build data-driven, low-dimensional dynamic models that capture the temporal evolution of multiple cortical areas, encoding both the auditory stimulus and the perceptual state.

自然环境中的声音是来自许多不同来源的复杂混合物。该项目旨在了解人类如何将声音的混合物组织成有意义的对象。对听觉对象的感知并不是来自大脑的任何特定部分，而是来自许多大脑区域的协调活动;此外，声音与听觉对象的结合可能会非常迅速地转换。因此，研究听觉对象是如何形成的以及如何快速切换需要分析人类大脑活动的记录，这些记录跨越许多大脑区域，并且速度非常快。该项目旨在开发新的理论方法，用于整合和分析来自大规模电极阵列的大脑记录的复杂动态数据集。建模方法将提供洞察力在正常和临床受损的头脑中的人类听觉感知的理解。在过去的三十年中，已经取得了显着的进展，表征神经相关的听觉感知定位到听觉皮层。然而，这些神经相关性可能并不局限于听觉皮层或大脑的任何特定部分。为了了解听觉知觉表征和知觉转换的神经机制，目前的项目结合了实验设计和理论的进步。将从人类受试者中收集大规模皮层电图（ECoG）记录，因为他们在涉及快速感知切换的听觉任务期间自我报告他们的感知。接下来，将使用神经科学中称为动态模式分解（DMD）的数据驱动方法从这些大型时间序列数据集中提取任务期间皮层激活的时空模式。然后，DMD提取的特征将用于构建数据驱动的低维动态模型，该模型捕获多个皮层区域的时间演变，对听觉刺激和感知状态进行编码。