The Neurophysiological Dynamics of Lexical and Sub-Lexical Representations

描述（由申请人提供）：本提案的总体目标是研究大脑如何从较低水平的语音和音位信息构建口语单词的词形和词汇表征。大脑能够在几百毫秒内解析声音感官输入的含义，这可能是由于经典语言网络中的局部和远距离皮质回路处理信息时，抽象程度增加的多层次表征。这些过程将使用综合的多模式神经生理学方法进行研究，该方法将高分辨率侵入性皮质电图（ECoG）与植入慢性硬膜下电极的癫痫患者相结合，以及提供全脑神经活动覆盖的非侵入性脑磁图（MEG）相结合。这些技术是高度互补的，将提供前所未有的时空分辨率来表征词汇处理，因为它在毫秒级上展开在相邻的神经群中。ECoG和MEG数据的主要分析将涉及构建一个神经“状态空间”，它代表了电极之间的共同活动。最近开发的方法允许将活动在状态空间中作为神经轨迹在时间上可视化。参与者将听到一个单词列表，每个单词由四个不同的说话者说出，并在200-400毫秒之间追踪从声学开始到假设的词汇和语义表征的神经轨迹。据推测，同一个单词的不同实例将从状态空间的不同部分开始（由于说话者之间的声学差异），并最终汇聚到代表与特定词汇项目相关的神经活动的区域。这些分析将与癫痫患者的ECoG数据一起进行，并将与健康对照者的MEG数据进行确认。MEG将提供关于这些轨迹的额外信息，因为它能够对整个皮层进行采样，并且可以检测到ECoG网格未覆盖的区域的相关活动（例如，对侧半球的区域）。最后，由于刺激将被仔细地控制语言特征，如词汇频率和语音趋同概率，因此有可能检查刺激水平的统计特性是如何通过假设的预测机制在大脑中编码的。总的来说，这些方法将阐明大脑中单词的抽象和高级表示过程，并将成为从动态、交互过程的角度描述语音感知神经代码的首批研究之一。理解大脑如何构建包含丰富、可塑和复杂含义的单词级表征，对于描述、诊断和治疗损害语言学习和处理的障碍至关重要。它还可能有助于改善人工耳蜗等辅助技术。解码神经表征的能力

DESCRIPTION (provided by applicant): The broad objective of this proposal is to investigate how the brain constructs word-form and lexical representations of spoken words from lower-level acoustic-phonetic and phonemic information. The brain's ability to parse meaning from acoustic sensory inputs within a few hundred milliseconds may arise from multiple levels of representation that increase in abstraction as information is processed through local and long-distance cortical circuits in the classical language network. These processes will be studied using an integrated, multimodal neurophysiological approach that combines high-resolution invasive electrocorticography (ECoG) in epilepsy patients implanted with chronic subdural electrodes, and non-invasive magnetoencephalography (MEG), which provides whole-brain coverage of neural activity. The techniques are highly complementary and will provide unprecedented spatiotemporal resolution to characterize lexical processing as it unfolds on the millisecond level across adjacent neural populations. The primary analysis for ECoG and MEG data will involve constructing a neural "state-space", which represents the common activity across electrodes. Recently-developed methods allow the activity to be visualized in the state-space across time as neural trajectories. Participants will hear a list of words, each spoken by four different speakers, and the neural trajectories will be traced from the acoustic onset to the hypothesized lexical and semantic representations between 200-400ms later. It is hypothesized that different instances of the same word will begin in different parts of the state-space (due to acoustic differences across speakers), and will eventually converge into areas that represent neural activity associated with a particular lexical item. These analyses will be performed with data from ECoG in epilepsy patients, and will also be confirmed with healthy controls in MEG. MEG will provide additional information about these trajectories, since it is able to sample the entire cortex, and may detect relevant activity in areas that are not covered by the ECoG grids (for example, areas in the contralateral hemisphere). Finally, since the stimuli will be carefully controlled for linguistic features such as lexical frequency and phonotactic probability, it will b possible to examine how stimulus-level statistical properties are encoded by hypothesized prediction mechanisms in the brain. In general, these approaches will elucidate the processes of abstraction and higher-level representation for words in the brain, and will be among the first studies to characterize the neural code for speech perception from the perspective of dynamic, interactive processes. Understanding how the brain constructs word-level representations that contain a rich, malleable, and complex meaning is crucial for characterizing, diagnosing, and treating disorders that impair language learning and processing. It may also help improve assistive technologies like cochlear implants. The ability to decode neural representations at this level will further our understanding of fundamental brain processes that underlie many aspects of higher cognitive function.