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影像方法（功能磁共振成像（fMRI）和 皮质电图（ECoG）与严格的行为方法相结合，我们将检查 多个皮质纹状体和感觉皮质网络在新颖的非 我们测试了双重学习的科学前提。 系统（DLS）模型，通过在依赖反馈的过程中使用 fMRI 或 ECoG 探测神经功能 与流行的单一学习系统（SLS）方法相比，DLS 定位了两种神经网络： 可分离的皮质纹状体系统对于言语学习至关重要：一个明确的、声音规则的皮质纹状体 系统，将声音映射到规则，以及隐式的声音奖励皮质纹状体系统，隐式地 根据 DLS，这两个系统将声音与可立即获得奖励的行为联系起来。 通过闭环，高度可塑性的皮质纹状体系统可以“训练”无钥匙的学习者的新兴专业知识。 一旦类别确定，不稳定的颞叶网络就可以通过经过验证的规则或奖励对信息进行分类。 学习到自动化程度后，皮质纹状体网络不再需要调解行为。 相反，颞皮层内的抽象分类信息驱动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受实验者限制，要么依赖于规则（规则 基于，RB），或多维线索的隐式整合（信息整合，II）。 声音规则网络区域、前额皮质 (PFC)、海马体和尾状核显示 相对于 II 学习，RB 期间的激活程度更高；相比之下，声音奖励网络中的关键区域， 相对于目标 1.2 和 RB 学习，壳核和腹侧纹状体在 II 期间表现出更大的激活。 1.3，我们利用高密度网格在时间、PFC 和 海马区域检查 RB 学习期间颞叶表征变化的程度 是 PFC 和海马体中错误监控过程的结果。在目标 2 中，我们探索神经元。 使用 fMRI 或 ECoG 的功能来评估在获取非 我们预测，早期的超细分和细分类别将达到类似本地人的绩效水平。 “新手”的语音习得涉及到声音到规则的映射；后来的“经验丰富”的则涉及到声音到奖励的映射。 相比之下，只有皮质网络在分类中处于“类本地自动化”状态时才处于活跃状态。 使用创新的单试验分类和 ECoG 数据的网络级解码分析，我们检查 学习引起的颞叶内言语表征的变化进一步，我们检查了其程度。 PFC 和海马内的哪些错误监控过程驱动突发颞叶 新语音类别的表示。