Intracranial Electrophysiology & Anatomical Connectivity of Voice-Selective Auditory Cortex

缩略： 识别声音的能力是人类听力的一项复杂壮举。对于听者来说，大脑能够 从高度可变的声学输入中无缝地提取复杂的语言和非语言线索。 神经成像研究提出了听觉皮质专门负责声音感知的区域， 包括颞上回(STG)和颞上沟(STS)，统称为 地区“。功能神经成像研究也表明，这些区域对发声反应最强烈。 与其他灵长类发声和自然发音相比，进一步表明 听觉皮质对声学刺激的专门化。目前尚不清楚这些区域是否会 对语音的真正选择性，或者更一般的功能是处理复杂的频谱时间特征 听觉刺激，如声音。神经成像部分描述了语音感知网络。 研究表明，颞区表现出与额叶下回和中央前回的连接， 然而，这些研究局限于它们在生理时间尺度上表征发音区域的能力，并且 主要集中在表征额叶-颞叶白质通路的言语感知和 制作。这项拟议的研究旨在表征老年人对语音的局部电生理反应。 时间语音区，并将描述语音的额时区结构连接 感知网络。我将利用来自神经群体的颅内脑电(IEEG) 15例癫痫患者的人类听觉皮质--接受癫痫手术评估的受试者 声音的神经表示。当参与者听到已发表的声音时，将获得神经记录 针对iEEG研究优化的定位器刺激集，以及来自 模拟语音和其他自然声音的频谱时间特征的调制噪声，称为高斯 声音模式(GSP)。语音选择性听觉皮质的额颞部连通性将通过 在所有使用语音定位器的患者中使用临床获得的扩散张量成像(DTI)的患者招募到 日期(n=11)，并列入本提案(n=15)。连接性分析将揭示额叶皮质的区域 显示与STG和STS沿线神经元群体的连接，具有最大的语音选择性 回应。总之，这项建议将利用与局部皮质iEEG相结合的多模式数据集 生理时间的录音和DTI结构连通性分析以批判性地检查声音 选择性听觉皮质。

ABTRACT: The ability to recognize voice is an intricate feat of human audition. For the listener, the brain is able to seamlessly extract complex linguistic and non-linguistic cues from highly variable vocal acoustic input. Neuroimaging studies have proposed specialized regions of auditory cortex dedicated to voice perception, including superior temporal gyrus (STG) and superior temporal sulcus (STS), referred to as “temporal voice areas”. Functional neuroimaging studies also demonstrate these areas respond most strongly to vocalizations of the same-species compared to other primate vocalizations and natural sounds, further suggesting specialization of auditory cortex for vocal acoustic stimuli. It remains unknown if these regions demonstrate true selectivity for voice, or more generally function to process the spectrotemporal features of complex auditory stimuli, such as voice. The voice perception network has been partially described by neuroimaging studies and suggests temporal voice areas exhibit connectivity to inferior frontal gyrus and precentral gyrus, however these studies are limited in their ability to characterize voice areas at physiologic timescales and have largely focused on characterizing frontotemporal white matter pathways underlying speech perception and production. The proposed research aims to characterize local electrophysiologic responses to voice in temporal voice areas and will describe the frontotemporal structural connectivity of the voice perception network. I will leverage intracranial electroencephalography (iEEG) from neural populations across human auditory cortex in 15 patient-participants undergoing epilepsy surgery evaluation to examine the neural representation of voice. Neural recordings will be acquired while participants listen to a published Voice Localizer stimulus set optimized for iEEG research, as well as an engineered acoustic stimulus set from modulated noise that mimick the spectrotemporal features of voice and other natural sounds, called Gaussian Sound Patterns (GSPs). Frontotemporal connectivity of voice-selective auditory cortex will be examined across patients using clinically-acquired diffusion tensor imaging (DTI) in all patients with Voice Localizer recruited to date (n=11) and included in this proposal (n=15). Connectivity analyses will reveal regions of frontal cortex demonstrating connectivity to neuronal populations along STG and STS with the greatest voice-selective responses. Together this proposal will leverage a multimodal dataset that marries local cortical iEEG recordings at physiologic timescales and DTI structural connectivity analysis to critically examine voice selective auditory cortex.