Distribution of important and word-cued autobiographical memories in 20-, 35-, and 70-year-old adults.

For word-cued autobiographical memories, older adults had an increase, or bump, from the ages 10 to 30. All age groups had fewer memories from childhood than from other years and a power-function retention for memories from the most recent 10 years. There were no consistent differences in reaction times and rating scale responses across decades. Concrete words cued older memories, but no property of the cues predicted which memories would come from the bump. The 5 most important memories given by 20- and 35-year-old participants were distributed similarly to their word-cued memories, but those given by 70-year-old participants came mostly from the single 20-to-30 decade. No theory fully accounts for the bump.

The distribution of autobiographical memories across the lifespan.

Words were used to cue autobiographical memories from 20- and 70-year-old subjects. Both groups showed a decrease in memories from the childhood years and a power-function retention function for their most recent 10 years. Older subjects also had an increase in the number of memories from the ages 10 to 30. These results held for individual subjects as well as grouped data and held when either 124 or 921 memories were cued. Reaction times to produce memories were constant across decades except for childhood where they were longer.

Global value Chains, rising power firms and economic and social upgrading

© Emerald Group Publishing Limited.Purpose – The purpose of this paper is to introduce the global value chain (GVC) approach to understand the relationship between multinational enterprises (MNEs) and the changing patterns of global trade, investment and production, and its impact on economic and social upgrading. It aims to illuminate how GVCs can advance our understanding about MNEs and rising power (RP) firms and their impact on economic and social upgrading in fragmented and dispersed global production systems. Design/methodology/approach – The paper reviews theGVCliterature focusing on two conceptual elements of the GVC approach, governance and upgrading, and highlights three key recent developments in GVCs: concentration, regionalization and synergistic governance. Findings – The paper underscores the complicated role of GVCs in shaping economic and social upgrading for emerging economies, RP firms and developing country firms in general. Rising geographic and organizational concentration in GVCs leads to the uneven distribution of upgrading opportunities in favor of RP firms, and yet economic upgrading may be elusive even for the most established suppliers because of power asymmetry with global buyers. Shifting end markets and the regionalization of value chains can benefit RP firms by presenting alternative markets for upgrading. Yet, without further upgrading, such benefits may be achieved at the expense of social downgrading. Finally, the ineffectiveness of private standards to achieve social upgrading has led to calls for synergistic governance through the cooperation of private, public and social actors, both global and local. Originality/value – The paper illuminates how the GVC approach and its key concepts can contribute to the critical international business and RP firms literature by examining the latest dynamics in GVCs and their impacts on economic and social development in developing countries.

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.