Adrian KC Lee - US grants

The research proposed herein aims at developing and utilizing a multimodal imaging approach to examine the underlying brain mechanism involved in auditory attention. The proposed approach is to conduct the same experiment using functional magnetic resonance imaging (fMRI) and magneto/electroencephalography (M/EEG). This powerful combination utilizes the fine spatial precision of fMRI with the high temporal resolution of M/EEG to map the spatiotemporal dynamics of the cortical networks associated with auditory attention and scene analysis. Currently, however, the acoustical noise associated with fMRI presents a technical hurdle to all auditory studies. Initial steps to characterize the acoustical noise associated with different fMRI pulse sequences are underway. During the mentored phase, the candidate will draw on his signal processing expertise to develop a noise masking protocol that will psychoacoustically control forthe auditory environment during fMRI scanning while mitigating the technical challenges in MR image reconstruction associated with the proposed auditory-amicable fMRI pulse sequence. In later stages, two experiments will be carried out examining how the prefrontal cortex is differentially involved when subjects are directed to attend different cues of the auditory stimulus. We will determine what "biomarkers" can be extracted from the M/EEG signals that are associated with the listener's attentional states. The project fits the candidate's long-term career goal of establishing a high-quality independent research program that combines engineering and neuroscience approaches in a synergistic manner to characterize the "biomarkers" that are associated with auditory scene analysis. This work will facilitate the candidate's immediate goals of becoming an expert in multimodal imaging while bringing to the field his knowledge in speech and hearing sciences, particularly his quantitative psychophysics training. The independent phase of the NIH Pathway to Independence Career Development award will be carried out at the LIniversity of Washington, Seattle, where the candidate will begin his career as assistant professor on January 1, 2011.

DESCRIPTION (provided by applicant): In order to dynamically follow different conversations in a crowded environment, we must constantly direct attention to the auditory signal of interest and segregate sounds that originated from other sources. Normal-hearing listeners can achieve this task seamlessly, but hearing-impaired listeners, cochlear implant users, and individuals with (central) auditory processing disorders often find communicating in this everyday acoustic environment challenging. The long-term objective of this research is to characterize the cortical dynamics associated with different aspects of auditory attention and to incorporate these brain signals in a next-generation hearing assistive device that helps account for the listener's attentional focus. The current project is built on the hypothesis that there are distributed corticl regions that coordinate top-down and bottom-up auditory attention, and that these regions are functionally coupled to the auditory sensory areas differently depending on the task at hand. The brain dynamics associated with auditory attention are currently not well understood, and thus a necessary first step to achieve our long-term objective is to study the attentional network in normal-hearing listeners. The specific aims of this project seek to identify differences between the cortical regions recruited for attention based on spatial and non-spatial features (Aim 1), as well as how the rest of the cortex compensates when the peripheral auditory signal is degraded by simulating the reduction in spectrotemporal acuity experienced by listeners with hearing impairments and cochlear implant users (Aim 2). Furthermore, we propose to take a systems-level approach and investigate how other cortical regions communicate with the auditory sensory areas in order to coordinate switching and maintenance of attention in a crowded acoustical scene (Aim 3). Our proposal emphasizes designing behavioral paradigms that bridge the gap between psychoacoustics and neuroimaging research, thereby addressing how different regions of the cortex act in concert to aid us in communicating in everyday settings.

Despite receiving normal audiological assessments, some listeners still complain to clinicians that they struggle to hear, particularly in noisy or crowded environments. However, systematic investigations into how the brain processes sound (and how it can go wrong) are lacking. The goal of this funded project is to apply novel statistical approaches to study patterns of activity in the brain while it processes sound in complex situations like when listening in a multi-talker environment.

A wide variety of behavioral and electrophysiological responses will be collected under several different types of auditory stimulation. Behavioral data and physiological measures of brainstem response will be used to characterize individuals' hearing health in both monaural and binaural pathways, providing complementary information to their cortical magneto- and electroencephalography (M-EEG) responses during similar auditory tasks. Auditory attentional network connectivity will also be analyzed, to account for the neural underpinnings of aspects of auditory dysfunction such as the inability to maintain or switch attention between speakers. Using computationally-driven statistical approaches, flexible graphical model-based representations of high-dimensional time series will be learned, in order to characterize the auditory attentional network based on collected M-EEG data. Specifically, two computational aims will be tackled: 1) Construct Bayesian models to characterize dynamical cortical interactions at different spatial resolutions and 2) Develop models that infer connectivity structure at different canonical cortical rhythmic bands. This research program leverages the complementary expertise of the two investigators, bringing together auditory behavioral and systems neuroscience, with flexible and scalable statistical time series modeling approaches. Temporal structure is often ignored in big data analyses as well as in systems neuroscience, and this funded research will directly address this shortcoming by using a high-dimensional set of temporally continuous neural data. The cortical network discovered by this approach will enable neuroscientists to better understand the variability inherent in the auditory attentional network across both task types and individual differences in listening abilities.