Neural Systems in Auditory and Speech Categorization

听觉和言语分类中的神经系统

• 批准号: 10194447
• 负责人: Bharath Chandrasekaran
• 金额: $ 28.63万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10194447
• 关键词: Acoustics; Adult; Articulation; Auditory; Auditory area; Behavior; Behavioral; Biological Models; Brain; Categories; Classification; Corpus striatum structure; Cues; Data; Dimensions; Dorsal; Electrocorticogram; Feedback; Foundations; Functional Magnetic Resonance Imaging; Goals; Head; Hippocampus (Brain); Individual; Knowledge; Lead; Learning; Location; Maps; Measurement; Measures; Mediating; Methods; Modality; Monitor; Neurobiology; Neuronal Plasticity; Neurophysiology - biologic function; Outcome; Participant; Pathway interactions; Performance; Prefrontal Cortex; Process; Rewards; Role; Sensory; Signal Transduction; Speech; Speech Perception; Speech Sound; Stimulus; Stream; Structure; Superior temporal gyrus; System; Temporal Lobe; Testing; Time; Training; Ventral Striatum; Visual; base; caudate nucleus; density; design; experience; experimental study; innovation; learning outcome; multimodality; neural circuit; neural model; neurobiological mechanism; neuroimaging; neurophysiology; non-Native; novel; putamen; relating to nervous system; response; sound; spatiotemporal; speech accuracy

Using complementary multi-modal neuroimaging methods (functional magnetic resonance imaging (fMRI) and electrocorticography (ECoG)) in conjunction with rigorous behavioral approaches, we will examine the role of multiple cortico-striatal and sensory cortical networks in the acquisition and automatization of novel non- speech and speech categories in the mature adult brain. We test the scientific premise of a dual-learning systems (DLS) model by probing neural function using fMRI or ECoG during the process of feedback-dependent category learning. In contrast to popular single-learning system (SLS) approaches, DLS posits that two neurally- dissociable cortico-striatal systems are critical to speech learning: an explicit, sound-to-rule cortico-striatal system, that maps sounds onto rules, and an implicit, sound-to-reward cortico-striatal system that implicitly associates sounds with actions that lead to immediate reward. Per DLS, the two systems contribute to the emerging expertise of the learner. Via closed loops, the highly plastic cortico-striatal systems ‘train’ key less labile temporal lobe networks to categorize information by validated rules or rewards. Once categories are learned to the point of automaticity, cortico-striatal networks are no longer required to mediate behavior. Instead, abstract categorical information within the temporal cortex drives highly accurate speech categorization. In Aim 1.1, we use fMRI to examine the relative dominance of the two cortico-striatal networks in learning multidimensional non-speech category structures that are experimenter-constrained to either rely on rules (rule- based, RB), or on implicit integration of multidimensional cues (information-integration, II). We predict that key regions of the sound-to-rule network, the prefrontal cortex (PFC), hippocampus, and caudate nucleus show greater activation during RB, relative to II learning; in contrast, key regions within the sound-to-reward network, the putamen and the ventral striatum show greater activation during II, relative to RB learning. In Aims 1.2 and 1.3, we leverage the temporal precision of ECoG measurements from high-density grids in temporal, PFC, and Hippocampal regions to examine the extent to which temporal lobe representational changes during RB learning are an outcome of error-monitoring processes within the PFC and hippocampus. In Aim 2, we probe neural function using fMRI or ECoG to assess network and representational changes during the acquisition of non- native supra-segmental and segmental categories to native-like performance levels. We predict that early ‘novice’ speech acquisition involves sound-to-rule mapping; later ‘experienced’ involves sound-to-reward mapping. In contrast, only cortical networks are active at the point of ‘native-like automaticity’ in categorization. Using innovative single-trial classification and network-level decoding analyses on ECoG data, we examine learning-induced changes in speech representation within the temporal lobe. Further, we examine the extent to which error monitoring processes within the PFC and the hippocampus drive emergent temporal lobe representations of novel speech categories.

使用互补的多模态神经成像方法（功能磁共振成像（fMRI）和 皮质电图（ECoG））结合严格的行为方法，我们将研究的作用， 多个皮质-纹状体和感觉皮质网络在新的非- 成熟成人大脑中的言语和言语类别。我们测试了双重学习的科学前提 DLS模型通过使用fMRI或ECoG探测反馈依赖过程中的神经功能来建立 类别学习与流行的单一学习系统（SLS）方法相比，DLS假设两个神经- 可分离的皮质-纹状体系统对言语学习至关重要：一个外显的，声音规则的皮质-纹状体系统 系统，将声音映射到规则上，以及一个隐含的，声音奖励皮质纹状体系统， 将声音与导致立即奖励的行为联系起来。根据DLS，这两个系统有助于 学习者的新兴专业知识。通过闭合回路，高度可塑性的皮质-纹状体系统“训练”键较少 不稳定的颞叶网络通过有效的规则或奖励对信息进行分类。一旦类别 学习到自动化的程度，皮质-纹状体网络不再需要介导行为。 相反，颞叶皮层中的抽象分类信息驱动着高度准确的语音分类。 在目标1.1中，我们使用功能磁共振成像来检查两个皮质-纹状体网络在学习中的相对优势 多维非语音类别结构，其被实验者约束为依赖于规则（规则- 基于，RB），或隐含的多维线索整合（信息整合，II）。我们预测这把钥匙 声音规则网络的区域，前额叶皮层（PFC），海马和尾状核显示 相对于II学习，RB期间的激活更大;相反，声音-奖励网络中的关键区域， 相对于RB学习，壳核和腹侧纹状体在II期间显示出更大的激活。在目标1.2和 1.3，我们利用时间，PFC和高密度网格中ECoG测量的时间精度 海马区，以检查RB学习期间颞叶表征变化的程度 是前额叶皮层和海马内错误监控过程的结果。在目标2中，我们探测神经 功能使用功能磁共振成像或ECoG评估网络和代表性的变化，在收购的非- 本土超音段和音段类别到本土般的表现水平。我们预测， “新手”的言语习得涉及到声音到规则的映射;后来的“经验丰富”涉及到声音到奖励 映射.与此相反，只有皮层网络是活跃的“类天然自动性”的分类点。 使用创新的单次试验分类和网络级解码分析ECoG数据，我们研究 颞叶内语言表达的学习诱发变化。此外，我们还研究了 前额叶皮层和海马体中的错误监控过程驱动颞叶的出现， 新的言语类别的表征。