Individual differences in supra-threshold auditory perception - mechanisms and objective correlates

Thesis (Ph.D.)--Boston UniversityTo extract content and meaning from a single source of sound in a quiet background, the auditory system can use a small subset of a very redundant set of spectral and temporal features. In stark contrast, communication in a complex, crowded scene places enormous demands on the auditory system. Spectrotemporal overlap between sounds reduces modulations in the signals at the ears and causes masking, with problems exacerbated by reverberation. Consistent with this idea, many patients seeking audiological treatment seek help precisely because they notice difficulties in environments requiring auditory selective attention. In the laboratory, even listeners with normal hearing thresholds exhibit vast differences in the ability to selectively attend to a target. Understanding the mechanisms causing these supra-threshold differences, the focus of this thesis, may enable research that leads to advances in treating communication disorders that affect an estimated one in five Americans. Converging evidence from human and animal studies points to one potential source of these individual differences: differences in the fidelity with which supra-threshold sound is encoded in the early portions of the auditory pathway. Electrophysiological measures of sound encoding by the auditory brainstem in humans and animals support the idea that the temporal precision of the early auditory neural representation can be poor even when hearing thresholds are normal. Concomitantly, animal studies show that noise exposure and early aging can cause a loss (cochlear neuropathy) of a large percentage of the afferent population of auditory nerve fibers innervating the cochlear hair cells without any significant change in measured audiograms. Using behavioral, otoacoustic and electrophysiological measures in conjunction with computational models of sound processing by the auditory periphery and brainstem, a detailed examination of temporal coding of supra-threshold sound is carried out, focusing on characterizing and understanding individual differences in listeners with normal hearing thresholds and normal cochlear mechanical function. Results support the hypothesis that cochlear neuropathy may reduce encoding precision of supra-threshold sound, and that this manifests as deficits both behaviorally and in subcortical electrophysiological measures in humans. Based on these results, electrophysiological measures are developed that may yield sensitive, fast, objective measures of supra-threshold coding deficits that arise as a result of cochlear neuropathy

Bayesian Modeling and Estimation Techniques for the Analysis of Neuroimaging Data

Brain function is hallmarked by its adaptivity and robustness, arising from underlying neural activity that admits well-structured representations in the temporal, spatial, or spectral domains. While neuroimaging techniques such as Electroencephalography (EEG) and magnetoencephalography (MEG) can record rapid neural dynamics at high temporal resolutions, they face several signal processing challenges that hinder their full utilization in capturing these characteristics of neural activity. The objective of this dissertation is to devise statistical modeling and estimation methodologies that account for the dynamic and structured representations of neural activity and to demonstrate their utility in application to experimentally-recorded data. The first part of this dissertation concerns spectral analysis of neural data. In order to capture the non-stationarities involved in neural oscillations, we integrate multitaper spectral analysis and state-space modeling in a Bayesian estimation setting. We also present a multitaper spectral analysis method tailored for spike trains that captures the non-linearities involved in neuronal spiking. We apply our proposed algorithms to both EEG and spike recordings, which reveal significant gains in spectral resolution and noise reduction. In the second part, we investigate cortical encoding of speech as manifested in MEG responses. These responses are often modeled via a linear filter, referred to as the temporal response function (TRF). While the TRFs estimated from the sensor-level MEG data have been widely studied, their cortical origins are not fully understood. We define the new notion of Neuro-Current Response Functions (NCRFs) for simultaneously determining the TRFs and their cortical distribution. We develop an efficient algorithm for NCRF estimation and apply it to MEG data, which provides new insights into the cortical dynamics underlying speech processing. Finally, in the third part, we consider the inference of Granger causal (GC) influences in high-dimensional time series models with sparse coupling. We consider a canonical sparse bivariate autoregressive model and define a new statistic for inferring GC influences, which we refer to as the LASSO-based Granger Causal (LGC) statistic. We establish non-asymptotic guarantees for robust identification of GC influences via the LGC statistic. Applications to simulated and real data demonstrate the utility of the LGC statistic in robust GC identification

Multi-Scale Mathematical Modelling of Brain Networks in Alzheimer's Disease

Perturbations to brain network dynamics on a range of spatial and temporal scales are believed to underpin neurological disorders such as Alzheimer’s disease (AD). This thesis combines quantitative data analysis with tools such as dynamical systems and graph theory to understand how the network dynamics of the brain are altered in AD and experimental models of related pathologies. Firstly, we use a biophysical neuron model to elucidate ionic mechanisms underpinning alterations to the dynamics of principal neurons in the brain’s spatial navigation systems in an animal model of tauopathy. To uncover how synaptic deficits result in alterations to brain dynamics, we subsequently study an animal model featuring local and long-range synaptic degeneration. Synchronous activity (functional connectivity; FC) between neurons within a region of the cortex is analysed using two-photon calcium imaging data. Long-range FC between regions of the brain is analysed using EEG data. Furthermore, a computational model is used to study relationships between networks on these different spatial scales. The latter half of this thesis studies EEG to characterize alterations to macro-scale brain dynamics in clinical AD. Spectral and FC measures are correlated with cognitive test scores to study the hypothesis that impaired integration of the brain’s processing systems underpin cognitive impairment in AD. Whole brain computational modelling is used to gain insight into the role of spectral slowing on FC, and elucidate potential synaptic mechanisms of FC differences in AD. On a finer temporal scale, microstate analyses are used to identify changes to the rapid transitioning behaviour of the brain’s resting state in AD. Finally, the electrophysiological signatures of AD identified throughout the thesis are combined into a predictive model which can accurately separate people with AD and healthy controls based on their EEG, results which are validated on an independent patient cohort. Furthermore, we demonstrate in a small preliminary cohort that this model is a promising tool for predicting future conversion to AD in patients with mild cognitive impairment