A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item Changes in Cortical Directional Connectivity during Difficult Listening in Younger and Older Adults(Wiley, 2023-05) Soleimani, Behrad; Dushyanthi Karunathilake, I. M.; Das, Proloy; Kuchinsky, Stephanie E.; Babadi, Behtash; Simon, Jonathan E.One way to investigate the mechanisms that underlie speech comprehension under difficult listening conditions is via cortical connectivity. The innovative Network Localized Granger Causality (NLGC) framework was applied to magnetoencephalography (MEG) data, obtained from older and younger subjects performing a speech listening task in noisy conditions, in delta and theta frequency bands. Directional connectivity between frontal, temporal, and parietal lobes was analyzed. Both aging- and condition-related changes were found, particularly in theta. In younger adults, as background noise increased, there was a transition from predominantly temporal-to-frontal (bottom-up) connections, to predominantly frontal-to-temporal (top-down). In contrast, older adults showed bidirectional information flow between frontal and temporal cortices even for speech in quiet, not changing substantially with increased noise. Additionally, younger listeners did not show changes in the nature of their cortical links for different listening conditions, whereas older listeners exhibited a switch from predominantly facilitative links to predominantly sharpening, when noise increased.Item Bayesian Modeling and Estimation Techniques for the Analysis of Neuroimaging Data(2020) Das, Proloy; Babadi, Behtash; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)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.