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Flexible representation of speech in human auditory cortex

人类听觉皮层中语音的灵活表示

基本信息

批准号：
10605606
负责人：
Vibha Viswanathan
金额：
$ 3.35万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-12-01 至 2023-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10605606
关键词：
Accent Acoustics Address Area Auditory Auditory area Behavior Control Behavioral Behavioral Research Brain Categories Clinical Code Cognitive Communication impairment Complement Cues Data Dimensions Doctor of Philosophy Effectiveness Electrocorticogram Electroencephalography Electrophysiology (science)Engineering Environment Frequencies General Population Goals Hearing Human Intervention Laboratories Language Linguistics Link Maps Measurable Measures Modeling Neurobiology Neurologic Neurosciences Noise Pear Population Psychophysics Research Research Training Scalp structure Schizophrenia Sensory Signal Transduction Source Speech Speech Acoustics Speech Perception Stimulus Structure of superior temporal sulcus Superior temporal gyrus Surface System Temporal Lobe Testing Theoretical model Time Training Training Support Translating Ursidae Family Variant Weight Work autism spectrum disorder career clear speech clinical application cognitive neuroscience cohort contextual factors flexibility gray matter hearing impairment improved innovation insight nervous system disorder neural information processing neuromechanism novel rehabilitation strategy relating to nervous system response speech in noise speech processing speech recognition translational health science

项目摘要

PROJECT SUMMARY/ABSTRACT The mapping from acoustics to linguistically relevant speech categories is not fixed; rather, speech recognition adapts to listening context. For example, the utterances bear and pear differ by as many as 16 spectrotemporal acoustic dimensions. The relative importance (i.e., the perceptual weight) of each of these acoustic dimensions in signaling bear vs. pear changes with contextual factors like whether the listening environment is quiet or noisy and whether the talker speaks in a native or unfamiliar accent. This flexibility or adaptive plasticity is an important component of how the brain maintains robust speech perception despite considerable variation in speech acoustics. Indeed, inflexible sensory processing is implicated as a source of perceptual deficits in both hearing impairment and neurological disorders. Yet, the neural mechanisms underlying adaptive plasticity in speech perception are poorly understood. To address this gap, the proposed research will leverage access deep within human auditory cortex obtained through intracerebral stereotactic electroencephalography (sEEG), by employing sEEG simultaneously with scalp electroencephalography (EEG) and well-established behavioral tasks for measuring adaptive plasticity. Aim 1 will invoke adaptive plasticity in speech perception with acoustic noise or a change in short-term input distributions mimicking an ‘accent’ and will relate behavioral changes in the perceptual weights of acoustic dimensions relative to baseline (i.e., clear speech) to changes in the neural response profile across the auditory cortical hierarchy. Aim 2 will quantify the relationships between sEEG and EEG to establish the extent to which intracerebral signatures of adaptive plasticity relate to scalp EEG signatures measurable in the general population; this will also facilitate region-specific interpretation of EEG. The work will provide novel insight into the cortical mechanisms of adaptive plasticity in speech perception, with implications for neurobiological models and clinical applications. Our innovative combination of simultaneous scalp EEG with spatially specific, high signal-to-noise ratio sEEG will create a highly informative link across noninvasive and intracranial electrophysiology. Project completion also will provide the applicant with training in cognitive neuroscience of speech perception, intracerebral electrophysiology, and approaches to effectively integrate intracerebral and scalp EEG. This training will complement her strong engineering/quantitative background and Ph.D. training in auditory temporal coding, scene analysis, and EEG. This will advance her goal of undertaking a successful independent academic research career in the neuroscience of human audition and speech perception.

项目总结/文摘