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Thesis
Peer Reviewed

Visualization and Non-parametric Statistical Testing Methods for Multivariate Time Series

UC Irvine Electronic Theses and Dissertations (2018)

Many model-based methods have been developed over the last several decades for analysis of multivariate time series, such as electroencephalograms (EEG) in order to understand electrical neural data. In this dissertation, we propose to use the functional boxplot to analyze log periodograms of EEG time series data in the spectral domain. The functional boxplot approach produces a median curve -- which is not equivalent to connecting medians obtained from frequency-specific boxplots. In addition, this approach identifies a functional median, summarizes variability and detects potential outliers. By extending functional boxplots analysis from one-dimensional curves to surfaces, surface boxplots are also used to explore the variation of the spectral power for the alpha ($8-12$ Hertz) and beta ($16-32$ Hertz) frequency bands across the brain cortical surface. By using rank-based nonparametric tests, we also investigate the stationarity of EEG traces across an exam acquired during resting-state by comparing the spectrum during the early vs. late phases of a single resting-state EEG exam.

Moreover, we present an exploratory data analysis tool for visualizing and testing the symmetric positive definite matrices (e.g., covariance, spectral and coherence matrices) in a multi--subject experimental setting. %derived from electroencephalogram (EEG) recordings from several clinical subjects. Our work is motivated by the clinician's interest to determine associations between brain functional connectivity (as measured by coherence) and patients' response to treatment. For each study participant, the geometric surface boxplot is developed to characterize the distribution of coherence matrices through the median matrix and the 50\% most central region of the data. The surface boxplot will also be used to detect the outlier coherence matrices as in the classical boxplot. To investigate the treatment effect, we develop a rank--based non--parametric approach to test for significant differences in coherence matrices between treatment and control groups. As an application, we demonstrate our proposed methods on coherence matrices, derived from an electroencephalograms, to determine potential associations treatment effect on patients with infantile spasms and hypsarrhythmia both before and after treatment.

Cover page: Visualization and Non-parametric Statistical Testing Methods for Multivariate Time Series

Article
Peer Reviewed

An exploratory data analysis of electroencephalograms using the functional boxplots approach

ICTS Publications (2015)

Creative Commons 'BY' version 4.0 license

Article
Peer Reviewed

A novel method for dynamically altering the surface area of intracranial EEG electrodes.

UC Irvine Previously Published Works (2023)

Objective.Intracranial electroencephalogram (iEEG) plays a critical role in the treatment of neurological diseases, such as epilepsy and Parkinsons disease, as well as the development of neural prostheses and brain computer interfaces. While electrode geometries vary widely across these applications, the impact of electrode size on iEEG features and morphology is not well understood. Some insight has been gained from computer simulations, as well as experiments in which signals are recorded using electrodes of different sizes concurrently in different brain regions. Here, we introduce a novel method to record from electrodes of different sizes in the exact same location by changing the size of iEEG electrodes after implantation in the brain.Approach.We first present a theoretical model and anin vitrovalidation of the method. We then report the results of anin vivoimplementation in three human subjects with refractory epilepsy. We recorded iEEG data from three different electrode sizes and compared the amplitudes, power spectra, inter-channel correlations, and signal-to-noise ratio (SNR) of interictal epileptiform discharges, i.e. epileptic spikes.Main Results.We found that iEEG amplitude and power decreased as electrode size increased, while inter-channel correlation did not change significantly with electrode size. The SNR of epileptic spikes was generally highest in the smallest electrodes, but 39% of spikes had maximal SNR in larger electrodes. This likely depends on the precise location and spatial spread of each spike.Significance.Overall, this new method enables multi-scale measurements of electrical activity in the human brain that can facilitate our understanding of neurophysiology, treatment of neurological disease, and development of novel technologies.

Cover page: A novel method for dynamically altering the surface area of intracranial EEG electrodes.

Article
Peer Reviewed

Supervised and Semi-Supervised Learning for MIMO Blind Detection With Low-Resolution ADCs

UC Irvine Previously Published Works (2020)

The use of low-resolution analog-to-digital converters (ADCs) is considered to be an effective technique to reduce the power consumption and hardware complexity of wireless transceivers. However, in systems with low-resolution ADCs, obtaining channel state information (CSI) is difficult due to significant distortions in the received signals. The primary motivation of this paper is to show that learning techniques can mitigate the impact of CSI unavailability. We study the blind detection problem in multiple-input-multiple-output (MIMO) systems with low-resolution ADCs using learning approaches. Two methods, which employ a sequence of pilot symbol vectors as the initial training data, are proposed. The first method exploits the use of a cyclic redundancy check (CRC) to obtain more training data, which helps improve the detection accuracy. The second method is based on the perspective that the to-be-decoded data can itself assist the learning process, so no further training information is required except the pilot sequence. For the case of 1-bit ADCs, we provide a performance analysis of the vector error rate for the proposed methods. Based on the analytical results, a criterion for designing transmitted signals is also presented. Simulation results show that the proposed methods outperform existing techniques and are also more robust.

Cover page: Supervised and Semi-Supervised Learning for MIMO Blind Detection With Low-Resolution ADCs

Article
Peer Reviewed

Common genomic elements promote transcriptional and DNA replication roadblocks.

UCLA Previously Published Works (2016)

RNA polymerase II (Pol II) transcription termination by the Nrd1p-Nab3p-Sen1p (NNS) pathway is critical for the production of stable noncoding RNAs and the control of pervasive transcription in Saccharomyces cerevisiae To uncover determinants of NNS termination, we mapped the 3'-ends of NNS-terminated transcripts genome-wide. We found that nucleosomes and specific DNA-binding proteins, including the general regulatory factors (GRFs) Reb1p, Rap1p, and Abf1p, and Pol III transcription factors enhance the efficiency of NNS termination by physically blocking Pol II progression. The same DNA-bound factors that promote NNS termination were shown previously to define the 3'-ends of Okazaki fragments synthesized by Pol δ during DNA replication. Reduced binding of these factors results in defective NNS termination and Pol II readthrough. Furthermore, inactivating NNS enables Pol II elongation through these roadblocks, demonstrating that effective Pol II termination depends on a synergy between the NNS machinery and obstacles in chromatin. Consistent with this finding, loci exhibiting Pol II readthrough at GRF binding sites are depleted for upstream NNS signals. Overall, these results underscore how RNA termination signals influence the behavior of Pol II at chromatin obstacles, and establish that common genomic elements define boundaries for both DNA and RNA synthesis machineries.

Article
Peer Reviewed

Common genomic elements promote transcriptional and DNA replication roadblocks

UCLA Previously Published Works (2016)

RNA polymerase II (Pol II) transcription termination by the Nrd1p-Nab3p-Sen1p (NNS) pathway is critical for the production of stable noncoding RNAs and the control of pervasive transcription in Saccharomyces cerevisiae To uncover determinants of NNS termination, we mapped the 3'-ends of NNS-terminated transcripts genome-wide. We found that nucleosomes and specific DNA-binding proteins, including the general regulatory factors (GRFs) Reb1p, Rap1p, and Abf1p, and Pol III transcription factors enhance the efficiency of NNS termination by physically blocking Pol II progression. The same DNA-bound factors that promote NNS termination were shown previously to define the 3'-ends of Okazaki fragments synthesized by Pol δ during DNA replication. Reduced binding of these factors results in defective NNS termination and Pol II readthrough. Furthermore, inactivating NNS enables Pol II elongation through these roadblocks, demonstrating that effective Pol II termination depends on a synergy between the NNS machinery and obstacles in chromatin. Consistent with this finding, loci exhibiting Pol II readthrough at GRF binding sites are depleted for upstream NNS signals. Overall, these results underscore how RNA termination signals influence the behavior of Pol II at chromatin obstacles, and establish that common genomic elements define boundaries for both DNA and RNA synthesis machineries.

Article
Peer Reviewed

A functional genomics screen identifying blood cell development genes in Drosophila by undergraduates participating in a course-based research experience

UCLA Previously Published Works (2021)

Undergraduate students participating in the UCLA Undergraduate Research Consortium for Functional Genomics (URCFG) have conducted a two-phased screen using RNA interference (RNAi) in combination with fluorescent reporter proteins to identify genes important for hematopoiesis in Drosophila. This screen disrupted the function of approximately 3500 genes and identified 137 candidate genes for which loss of function leads to observable changes in the hematopoietic development. Targeting RNAi to maturing, progenitor, and regulatory cell types identified key subsets that either limit or promote blood cell maturation. Bioinformatic analysis reveals gene enrichment in several previously uncharacterized areas, including RNA processing and export and vesicular trafficking. Lastly, the participation of students in this course-based undergraduate research experience (CURE) correlated with increased learning gains across several areas, as well as increased STEM retention, indicating that authentic, student-driven research in the form of a CURE represents an impactful and enriching pedagogical approach.

Cover page: A functional genomics screen identifying blood cell development genes in Drosophila by undergraduates participating in a course-based research experience