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.
