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

自然环境中的声音是来自许多不同来源的复杂混合物。这个项目试图了解人类如何将混合的声音组织成有意义的物体。对听觉物体的感知不是来自大脑的任何特定部分，而是来自大脑许多区域的协调活动；此外，声音与听觉物体的绑定可能会非常迅速地转换。因此，研究听觉对象是如何形成的，以及切换发生得有多快，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，并以非常快的速度进行。该项目旨在开发新的理论方法来整合和分析来自大规模电极阵列的脑记录的复杂动态数据集。该建模方法将提供对正常和临床受损大脑中人类听觉感知的理解的洞察力。在过去的三十年里，已经取得了重大的进展，表征了定位于听觉皮质的听觉知觉的神经关联。然而，这些神经关联可能并不局限于听觉皮质，或者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方面的进展。当受试者在涉及快速知觉转换的双稳态听觉任务中自我报告他们的知觉时，将从受试者那里收集大规模皮层脑电(ECoG)记录。下一步，将使用一种称为动态模式分解(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大脑皮质激活的时空模式。然后，DMD提取的特征将被用于构建数据驱动的低维动态模型，该模型捕捉多个皮质区域的时间演变，编码听觉刺激和感知状态。

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.