The spatiotemporal dynamics of cortical speech representation

描述(申请人提供)：本研究的目的是研究在言语知觉过程中，跨听皮层的语音表征的组织结构和皮层区域之间的时空相互作用。大脑从传入的声学信息中解析含义的能力源于皮质网络中的复杂相互作用，但这些网络是如何组织的仍不清楚。高密度脑电地形图(ECoG)将被用来记录清醒的、行为正常的受试者听觉皮质中语音选择区域的神经活动。这些受试者是根据临床标准植入电极的药物难治性癫痫患者。通过结合多种计算方法，这项工作将阐明(1)ECoG阵列上单个电极的声学特征选择性，(2)这种特征选择性的空间组织，以及(3)神经群体对语音反应的时空动力学，目的是揭示群体水平分析中仅从单个站点无法恢复的新特性。参与者将被动地听不同说话者和不同性别的人说出的句子，同时记录ECoG信号。其他声音刺激将包括环境声音和调制限制的噪声涟漪。使用最大信息维度(MID)分析和声音刺激的不同参数，包括将声音分解成其相应的光谱时间调制以及识别语言参数的存在或不存在，将从对这些句子的神经反应中建立光谱时间感受野模型。这将揭示每个SIT对低水平和高水平声音特征的选择性，以及这种选择性跨皮质表面解剖的组织。比较不同类型的声音刺激的这种映射将揭示语音和非语音的组织是否不同，或者组织是否纯粹基于声音信号的声学特性。矢量自回归模型和相位耦合振荡器模型将被用来描述听觉皮质区域内的种群水平的相互作用。这些相互作用可能表现为跨空间的语音依赖细胞组件，这些组件分隔了不同语音特征的处理，这些特征在单个电极水平上无法被发现。这些实验的结果对从整体上理解神经编码有意义，并将揭示群体活动与单电极计算在语音处理中的重要性。阐明大脑如何代表对言语重要的低水平和高水平特征，对于理解、诊断和治疗沟通障碍至关重要，这些障碍包括语言学习延迟、失语症、阅读障碍和自闭症。此外，这项工作可能有助于改进脑机接口设计，包括人工耳蜗术。

DESCRIPTION (provided by applicant): The purpose of the proposed research is to investigate the organization of speech sound representation across the auditory cortex and the spatiotemporal interactions between cortical areas during speech perception. The brain's ability to parse meaning from incoming acoustic information arises from complex interactions within cortical networks, but how these networks are organized remains unclear. High-density intracranial electrocorticography (ECoG) will be used to record neural activity from speech-selective areas in the auditory cortex of awake, behaving human subjects. These subjects are patients with medication refractory epilepsy who have electrodes implanted based on clinical criteria. By combining multiple computational approaches this work will elucidate (1) the acoustic feature selectivity of single electrodes on the ECoG array, (2) the spatial organization of this feature selectivity, and (3) the spatiotemporal dynamics of neural population responses to speech sounds, with the goal of uncovering emergent properties within the population level analysis that cannot be recovered from single sites alone. Participants will listen passively to sentences spoken by a variety of speakers and of different genders while the ECoG signal is recorded. Additional sound stimuli will include environmental sounds and modulation limited noise ripples. Spectrotemporal receptive field models will be built from the neural responses to these sentences using maximally informative dimensions (MID) analysis and different parameterizations of the sound stimuli, including decomposition of the sounds into their corresponding spectrotemporal modulations and identification of the presence or absence of linguistic parameters such as manner of articulation. This will uncover the selectivity of each sit for low-level and high-level sound features as well as the organization of this selectivity across the anatomy of the cortical surface. Comparing this mapping for different types of sound stimuli will reveal whether organization differs for speech and non-speech sounds, or whether organization is based purely on acoustic properties of the sound signal. Vector autoregressive models and phase coupled oscillator models will be used to describe population-level interactions within areas of the auditory cortex. These interactions may manifest as speech-dependent cell assemblies across space that segregate processing for different phonetic features that cannot be uncovered at the single electrode level. Results from these experiments have implications for understanding neural coding as a whole, and will reveal the importance of population activity versus single electrode computations in speech processing. Elucidating how the brain represents low-level and high-level features important for speech is critical for understanding, diagnosing, and treating communication disorders including delayed language learning, aphasias, dyslexia, and autism. In addition, this work may help to improve brain machine interface design, including cochlear implants.