Theses and Dissertations from UMD
Permanent URI for this communityhttp://hdl.handle.net/1903/2
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
Browse
3 results
Search Results
Item Computing with Trajectories: Characterizing Dynamics and Connectivity in Spatiotemporal Neuroimaging Data(2020) Venkatesh, Manasij; Pessoa, Luiz; JaJa, Joseph F; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Human functional Magnetic Resonance Imaging (fMRI) data are acquired while participants engage in diverse perceptual, motor, cognitive, and emotional tasks. Although data are acquired temporally, they are most often treated in a quasi-static manner. Yet, a fuller understanding of the mechanisms that support mental functions necessitates the characterization of dynamic properties. Firstly, we describe an approach employing a class of recurrent neural networks called reservoir computing, and show their feasibility and potential for the analysis of temporal properties of brain data. We show that reservoirs can be used effectively both for condition classification and for characterizing lower-dimensional "trajectories" of temporal data. Classification accuracy was approximately 90% for short clips of "social interactions" and around 70% for clips extracted from movie segments. Data representations with 12 or fewer dimensions (from an original space with over 300) attained classification accuracy within 5% of the full data. We hypothesize that such low-dimensional trajectories may provide "signatures" that can be associated with tasks and/or mental states. The approach was applied across participants (that is, training in one set of participants, and testing in a separate group), showing that representations generalized well to unseen participants. In the second part, we use fully-trained recurrent neural networks to capture and characterize spatiotemporal properties of brain events. We propose an architecture based on long short-term memory (LSTM) networks to uncover distributed spatiotemporal signatures during dynamic experimental conditions. We demonstrate the potential of the approach using naturalistic movie-watching fMRI data. We show that movie clips result in complex but distinct spatiotemporal patterns in brain data that can be classified using LSTMs (≈90% for 15-way classification), demonstrating that learned representations generalized to unseen participants. LSTMs were also superior to existing methods in predicting behavior and personality traits of individuals. We propose a dimensionality reduction approach that uncovers low-dimensional trajectories and captures essential informational properties of brain dynamics. Finally, we employed saliency maps to characterize spatiotemporally-varying brain-region importance. The spatiotemporal saliency maps revealed dynamic but consistent changes in fMRI activation data. Taken together, we believe the above approaches provide a powerful framework for visualizing, analyzing, and discovering dynamic spatially distributed brain representations during naturalistic conditions. Finally, we address the problem of comparing functional connectivity matrices obtained from temporal fMRI data. Understanding the correlation structure associated with multiple brain measurements informs about potential "functional groupings" and network organization. The correlation structure can be conveniently captured in a matrix format that summarizes the relationships among a set of brain measurements involving two regions, for example. Such functional connectivity matrix is an important component of many types of investigation focusing on network-level properties of the brain, including clustering brain states, characterizing dynamic functional states, performing participant identification (so-called "fingerprinting"), understanding how tasks reconfigure brain networks, and inter-subject correlation analysis. In these investigations, some notion of proximity or similarity of functional connectivity matrices is employed, such as their Euclidean distance or Pearson correlation (by correlating the matrix entries). We propose the use of a geodesic distance metric that reflects the underlying non-Euclidean geometry of functional correlation matrices. The approach is evaluated in the context of participant identification (fingerprinting): given a participant's functional connectivity matrix based on resting-state or task data, how effectively can the participant be identified? Using geodesic distance, identification accuracy was over 95% on resting-state data and exceeded the Pearson correlation approach by 20%. For whole-cortex regions, accuracy improved on a range of tasks by between 2% and as much as 20%. We also investigated identification using pairs of subnetworks (say, dorsal attention plus default mode), and particular combinations improved accuracy over whole-cortex participant identification by over 10%. The geodesic distance also outperformed Pearson correlation when the former employed a fourth of the data as the latter. Finally, we suggest that low-dimensional distance visualizations based on the geodesic approach help uncover the geometry of task functional connectivity in relation to that during resting-state. We propose that the use of the geodesic distance is an effective way to compare the correlation structure of the brain across a broad range of studies.Item Stochastic processes on graphs: learning representations and applications(2019) Bohannon, Addison Woodford; Balan, Radu V; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this work, we are motivated by discriminating multivariate time-series with an underlying graph topology. Graph signal processing has developed various tools for the analysis of scalar signals on graphs. Here, we extend the existing techniques to design filters for multivariate time-series that have non-trivial spatiotemporal graph topologies. We show that such a filtering approach can discriminate signals that cannot otherwise be discriminated by competing approaches. Then, we consider how to identify spatiotemporal graph topology from signal observations. Specifically, we consider a generative model that yields a bilinear inverse problem with an observation-dependent left multiplication. We propose two algorithms for solving the inverse problem and provide probabilistic guarantees on recovery. We apply the technique to identify spatiotemporal graph components in electroencephalogram (EEG) recordings. The identified components are shown to discriminate between various cognitive task conditions in the data.Item THE IMPACT OF ACUTE EXERCISE AND SLEEP QUALITY ON EXECUTIVE FUNCTION: THE POTENTIAL MEDIATING EFFECTS OF FUNCTIONAL CONNECTIVITY IN OLDER ADULTS(2017) Alfini, Alfonso J.; Smith, J. Carson; Kinesiology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Background: Although, improved longevity is a major public health accomplishment, the prevalence of chronic disease, including cognitive impairment, increases with age. Insufficient sleep and physical inactivity exacerbate chronic disease and may accelerate the onset of dementia. While a cure remains elusive, a growing body of evidence demonstrates that exercise training facilitates better sleep and enhanced cognition. Exercise-altered patterns of neural activity, including resting state functional connectivity (rsFC) and task-based functional activation, likely coincide with and may facilitate cognitive improvements in the aging brain. Purpose: This study sought to examine the joint impact of acute exercise and sleep quality on executive function in older adults. We also aimed to determine the degree to which exercise-induced changes in prefrontal rsFC influence the relationship between sleep and executive function performance/functional activation. Methods: Using a within subjects counter-balanced design, 21 participants (aged 55-85) underwent at least three days of objective sleep monitoring (actigraphy), followed by two experimental visits on separate days. During each visit, participants engaged in 30-minutes of rest or exercise followed immediately by resting state and task-based functional MRI. After the MRI scanning session, participants completed several executive function assessments. Neuroimaging and behavioral data were processed using AFNI (version 17.1.06) and SPSS (version 23), respectively. Results: Repeated measures ANOVA and multivariate linear regression revealed two significant voxel-wise interactions in the (L) precuneus. Our findings demonstrated that acute exercise increased prefrontal rsFC and functional activation in long sleepers (> 7.5 hours/night), while decreasing these parameters for individuals with less total sleep time. Moreover, these results correspond to behavioral data demonstrating that acute exercise and adequate sleep improved select aspects of executive function performance, while decreasing inhibitory control in short sleepers alone (< 7.5 hours). Conclusion: These findings suggest that the effects of acute exercise on prefrontal rsFC are similar, or even related, to the effects of acute exercise on conflict-dependent functional activation, and that this relationship may depend on sleep duration. Moreover, our results imply that although acute exercise elicited improved executive function for those with adequate sleep, it may weaken already vulnerable, and perhaps fatigued, executive function networks among short sleepers.