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.

Phosphorylation/dephosphorylation of the beta-adrenergic receptor regulates its functional coupling to adenylate cyclase and subcellular distribution.

Prolonged exposure of cells or tissues to drugs or hormones such as catecholamines leads to a state of refractoriness to further stimulation by that agent, known as homologous desensitization. In the case of the beta-adrenergic receptor coupled to adenylate cyclase, this process has been shown to be intimately associated with the sequestration of the receptors from the cell surface through a cAMP-independent process. Recently, we have shown that homologous desensitization in the frog erythrocyte model system is also associated with increased phosphorylation of the beta-adrenergic receptor. We now provide evidence that the phosphorylation state of the beta-adrenergic receptor regulates its functional coupling to adenylate cyclase, subcellular translocation, and recycling to the cell surface during the process of agonist-induced homologous desensitization. Moreover, we show that the receptor phosphorylation is reversed by a phosphatase specifically associated with the sequestered subcellular compartment. At 23 degrees C, the time courses of beta-adrenergic receptor phosphorylation, sequestration, and adenylate cyclase desensitization are identical, occurring without a lag, exhibiting a t1/2 of 30 min, and reaching a maximum at approximately 3 hr. Upon cell lysis, the sequestered beta-adrenergic receptors can be partially recovered in a light membrane vesicle fraction that is separable from the plasma membranes by differential centrifugation. The increased beta-adrenergic receptor phosphorylation is apparently reversed in the sequestered vesicle fraction as the sequestered receptors exhibit a phosphate/receptor stoichiometry that is similar to that observed under basal conditions. High levels of a beta-adrenergic receptor phosphatase activity appear to be associated with the sequestered vesicle membranes. The functional activity of the phosphorylated beta-adrenergic receptor was examined by reconstituting purified receptor with its biochemical effector the guanine nucleotide regulatory protein (Ns) in phospholipid vesicles and assessing the receptor-stimulated GTPase activity of Ns. Compared to controls, phosphorylated beta-adrenergic receptors, purified from desensitized cells, were less efficacious in activating the Ns GTPase activity. These results suggest that phosphorylation of the beta-adrenergic receptor leads to its functional uncoupling and physical translocation away from the cell surface into a sequestered membrane domain. In the sequestered compartment, the phosphorylation is reversed thus enabling the receptor to recycle back to the cell surface and recouple with adenylate cyclase.

A Molecular-scale Programmable Stochastic Process Based On Resonance Energy Transfer Networks: Modeling And Applications

While molecular and cellular processes are often modeled as stochastic processes, such as Brownian motion, chemical reaction networks and gene regulatory networks, there are few attempts to program a molecular-scale process to physically implement stochastic processes. DNA has been used as a substrate for programming molecular interactions, but its applications are restricted to deterministic functions and unfavorable properties such as slow processing, thermal annealing, aqueous solvents and difficult readout limit them to proof-of-concept purposes. To date, whether there exists a molecular process that can be programmed to implement stochastic processes for practical applications remains unknown.

In this dissertation, a fully specified Resonance Energy Transfer (RET) network between chromophores is accurately fabricated via DNA self-assembly, and the exciton dynamics in the RET network physically implement a stochastic process, specifically a continuous-time Markov chain (CTMC), which has a direct mapping to the physical geometry of the chromophore network. Excited by a light source, a RET network generates random samples in the temporal domain in the form of fluorescence photons which can be detected by a photon detector. The intrinsic sampling distribution of a RET network is derived as a phase-type distribution configured by its CTMC model. The conclusion is that the exciton dynamics in a RET network implement a general and important class of stochastic processes that can be directly and accurately programmed and used for practical applications of photonics and optoelectronics. Different approaches to using RET networks exist with vast potential applications. As an entropy source that can directly generate samples from virtually arbitrary distributions, RET networks can benefit applications that rely on generating random samples such as 1) fluorescent taggants and 2) stochastic computing.

By using RET networks between chromophores to implement fluorescent taggants with temporally coded signatures, the taggant design is not constrained by resolvable dyes and has a significantly larger coding capacity than spectrally or lifetime coded fluorescent taggants. Meanwhile, the taggant detection process becomes highly efficient, and the Maximum Likelihood Estimation (MLE) based taggant identification guarantees high accuracy even with only a few hundred detected photons.

Meanwhile, RET-based sampling units (RSU) can be constructed to accelerate probabilistic algorithms for wide applications in machine learning and data analytics. Because probabilistic algorithms often rely on iteratively sampling from parameterized distributions, they can be inefficient in practice on the deterministic hardware traditional computers use, especially for high-dimensional and complex problems. As an efficient universal sampling unit, the proposed RSU can be integrated into a processor / GPU as specialized functional units or organized as a discrete accelerator to bring substantial speedups and power savings.

Economic and social upgrading in global production networks: A new paradigm for a changing world

A key challenge in promoting decent work worldwide is how to improve the position of both firms and workers in value chains and global production networks driven by lead firms. This article develops a framework for analysing the linkages between the economic upgrading of firms and the social upgrading of workers. Drawing on studies which indicate that firm upgrading does not necessarily lead to improvements for workers, with a particular focus on the Moroccan garment industry, it outlines different trajectories and scenarios to provide a better understanding of the relationship between economic and social upgrading. The authors 2011 Journal compilation © International Labour Organization 2011.

NAFTA and the apparel commodity chain: Corporate strategies, interfirm networks, and industrial upgrading

The apparel industry is one of the oldest and largest export industries in the world, with global trade and production networks that connect firms and workers in countries at all levels of economic development. This chapter examines the impact of the North American Free Trade Agreement (NAFTA) as one of the most recent and significant developments to affect patterns of international trade and production in the apparel and textile industries. Tr ade policies are changing the institutional environment in which firms in this industry operate, and companies are responding to these changes with new strategies designed to increase their profitability and strengthen their control over the apparel commodity chain. Our hypothesis is that lead firms are establishing qualitatively different kinds of regional production networks in North America from those that existed prior to NAFTA, and that these networks have important consequences for industrial upgrading in the Mexican textile and apparel industries. Post-NAFTA crossborder production arrangements include full-package networks that link lead firms in the United States with apparel and textile manufacturers, contractors, and suppliers in Mexico. Full-package production is increasing the local value added provided by the apparel commodity chain in Mexico and creating new opportunities for Mexican firms and workers. The chapter is divided into four main sections. The first section uses trade and production data to analyze shifts in global apparel flows, highlighting the emergence and consolidation of a regional trade bloc in North America. The second section discusses the process of industrial upgrading in the apparel industry and introduces a distinction between assembly and full-package production networks. The third section includes case studies based on published industry sources and strategic interviews with several lead companies whose strategies are largely responsible for the shifting trade patterns and NAFTA-inspired cross-border production networks discussed in the previous section. The fourth section considers the implications of these changes for employment in the North American apparel industry. © 2009 by Temple University Press. All rights reserved.