Assessment of the Spatial and Temporal Distribution of Functional Connectivity in Resting-State BOLD fMRI

Recent research into resting-state functional magnetic resonance imaging (fMRI) has shown that the brain is very active during rest. This thesis work utilizes blood oxygenation level dependent (BOLD) signals to investigate the spatial and temporal functional network information found within resting-state data, and aims to investigate the feasibility of extracting functional connectivity networks using different methods as well as the dynamic variability within some of the methods. Furthermore, this work looks into producing valid networks using a sparsely-sampled sub-set of the original data.

In this work we utilize four main methods: independent component analysis (ICA), principal component analysis (PCA), correlation, and a point-processing technique. Each method comes with unique assumptions, as well as strengths and limitations into exploring how the resting state components interact in space and time.

Correlation is perhaps the simplest technique. Using this technique, resting-state patterns can be identified based on how similar the time profile is to a seed region’s time profile. However, this method requires a seed region and can only identify one resting state network at a time. This simple correlation technique is able to reproduce the resting state network using subject data from one subject’s scan session as well as with 16 subjects.

Independent component analysis, the second technique, has established software programs that can be used to implement this technique. ICA can extract multiple components from a data set in a single analysis. The disadvantage is that the resting state networks it produces are all independent of each other, making the assumption that the spatial pattern of functional connectivity is the same across all the time points. ICA is successfully able to reproduce resting state connectivity patterns for both one subject and a 16 subject concatenated data set.

Using principal component analysis, the dimensionality of the data is compressed to find the directions in which the variance of the data is most significant. This method utilizes the same basic matrix math as ICA with a few important differences that will be outlined later in this text. Using this method, sometimes different functional connectivity patterns are identifiable but with a large amount of noise and variability.

To begin to investigate the dynamics of the functional connectivity, the correlation technique is used to compare the first and second halves of a scan session. Minor differences are discernable between the correlation results of the scan session halves. Further, a sliding window technique is implemented to study the correlation coefficients through different sizes of correlation windows throughout time. From this technique it is apparent that the correlation level with the seed region is not static throughout the scan length.

The last method introduced, a point processing method, is one of the more novel techniques because it does not require analysis of the continuous time points. Here, network information is extracted based on brief occurrences of high or low amplitude signals within a seed region. Because point processing utilizes less time points from the data, the statistical power of the results is lower. There are also larger variations in DMN patterns between subjects. In addition to boosted computational efficiency, the benefit of using a point-process method is that the patterns produced for different seed regions do not have to be independent of one another.

This work compares four unique methods of identifying functional connectivity patterns. ICA is a technique that is currently used by many scientists studying functional connectivity patterns. The PCA technique is not optimal for the level of noise and the distribution of the data sets. The correlation technique is simple and obtains good results, however a seed region is needed and the method assumes that the DMN regions is correlated throughout the entire scan. Looking at the more dynamic aspects of correlation changing patterns of correlation were evident. The last point-processing method produces a promising results of identifying functional connectivity networks using only low and high amplitude BOLD signals.

Inequality, the Welfare State, and Demographic Change

This dissertation is a three-part analysis examining how the welfare state in advanced Western democracies has responded to recent demographic changes. Specifically, this dissertation investigates two primary relationships, beginning with the influence of government spending on poverty. I analyze two at-risk populations in particular: immigrants and children of single mothers. Next, attention is turned to the influence of individual and environmental traits on preferences for social spending. I focus specifically on religiosity, religious beliefs and religious identity. I pool data from a number of international macro- and micro-data sources including the Luxembourg Income Study (LIS), International Social Survey Program (ISSP), the World Bank Databank, and the OECD Databank. Analyses highlight the power of the welfare state to reduce poverty, but also the effectiveness of specific areas of spending focused on addressing new social risks. While previous research has touted the strength of the welfare state, my analyses highlight the need to consider new social risks and encourage closer attention to how social position affects preferences for the welfare state.

Development of Plasmonic Nanoplatforms for Diagnostics, Therapy, and Sensing

Recent advances in nanotechnology have led to the application of nanoparticles in a wide variety of fields. In the field of nanomedicine, there is great emphasis on combining diagnostic and therapeutic modalities into a single nanoparticle construct (theranostics). In particular, anisotropic nanoparticles have shown great potential for surface-enhanced Raman scattering (SERS) detection due to their unique optical properties. Gold nanostars are a type of anisotropic nanoparticle with one of the highest SERS enhancement factors in a non-aggregated state. By utilizing the distinct characteristics of gold nanostars, new plasmonic materials for diagnostics, therapy, and sensing can be synthesized. The work described herein is divided into two main themes. The first half presents a novel, theranostic nanoplatform that can be used for both SERS detection and photodynamic therapy (PDT). The second half involves the rational design of silver-coated gold nanostars for increasing SERS signal intensity and improving reproducibility and quantification in SERS measurements.

The theranostic nanoplatforms consist of Raman-labeled gold nanostars coated with a silica shell. Photosensitizer molecules for PDT can be loaded into the silica matrix, while retaining the SERS signal of the gold nanostar core. SERS detection and PDT are performed at different wavelengths, so there is no interference between the diagnostic and therapeutic modalities. Singlet oxygen generation (a measure of PDT effectiveness) was demonstrated from the drug-loaded nanocomposites. In vitro testing with breast cancer cells showed that the nanoplatform could be successfully used for PDT. When further conjugating the nanoplatform with a cell-penetrating peptide (CPP), efficacy of both SERS detection and PDT is enhanced.

The rational design of plasmonic nanoparticles for SERS sensing involved the synthesis of silver-coated gold nanostars. Investigation of the silver coating process revealed that preservation of the gold nanostar tips was necessary to achieve the increased SERS intensity. At the optimal amount of silver coating, the SERS intensity is increased by over an order of magnitude. It was determined that a majority of the increased SERS signal can be attributed to reducing the inner filter effect, as the silver coating process moves the extinction of the particles far away from the laser excitation line. To improve reproducibility and quantitative SERS detection, an internal standard was incorporated into the particles. By embedding a small-molecule dye between the gold and silver surfaces, SERS signal was obtained both from the internal dye and external analyte on the particle surface. By normalizing the external analyte signal to the internal reference signal, reproducibility and quantitative analysis are improved in a variety of experimental conditions.