The impact of noise on temporal integration of speech in the human brain

PROJECT SUMMARY Understanding speech in real-world conditions is a complex process that requires the brain to integrate information about the incoming speech stream concurrently on multiple timescales, ranging from milliseconds to seconds. While previous work has characterized integration timescales across the auditory cortex, it remains unclear the extent to which these temporal integration windows are fixed or whether they vary depending on stimulus processing demands, such as the presence of background noise. Prior studies that have examined this question have been limited to measuring integration windows using linear modeling (e.g., spectrotemporal receptive fields), and much of the relevant research has either been conducted in animals or used coarse neuroimaging measures. As a consequence, much remains unknown about the human auditory cortex integrates information in speech during challenging listening conditions, which is thought to depend upon highly nonlinear computations. In this project, we examine the degree to which auditory cortical integration windows vary depending on the presence or absence of background noise using a novel method (the “temporal context invariance” or TCI paradigm) applied to both scalp EEG (Aim 1) and intracranial EEG recordings (Aim 2). The TCI paradigm makes it possible to measure integration windows from any sensory response, even if that response is a highly nonlinear function of its input. Scalp EEG recordings will allow me to test if there is any overall change in the integration window of auditory cortical responses in the presence of noise, while the unparalleled spatiotemporal resolution of intracranial recordings will enable me to examine the neuroanatomical basis of integration window flexibility. The proposed research will answer longstanding questions about the nature of temporal integration in the auditory cortex, and further our understanding of how the brain reckons with the extreme variability inherent in real-world communication settings in order to arrive at stable representations of speech despite interference from background sounds. This research is a critical first step in understanding the speech perception deficits in noise that are present in auditory neurodevelopmental and attentional disorders, many of which are hypothesized to also involve impairments in temporal processing. In the process of conducting this research, I will develop expertise in several valuable domains: (1) scalp EEG experiments, (2) intracranial EEG experiments, (3) the analysis of high-dimensional time-series data, (4) hypothesis-driven encoding models of speech. These skills complement my prior expertise in fMRI, music, and data-driven component modeling, thus equipping me with a unique and valuable set of experimental and computational skills that will facilitate my transition to an independent research career.