A challenge in clinical neuroscience is to identify systematic differences between patients’ resting state networks (RSNs) that may relate to phenotypes and/or disease subtypes. Recently, Durieux...Show moreA challenge in clinical neuroscience is to identify systematic differences between patients’ resting state networks (RSNs) that may relate to phenotypes and/or disease subtypes. Recently, Durieux et al. (2022) proposed a novel unsupervised learning method for multi-subject rs-fMRI data, called Clusterwise Independent Component Analysis (C-ICA), which enables automatic clustering of subjects based on differences and similarities in subjects’ RSNs. A drawback of C-ICA, however, is that the method assumes that subject clusters show large difference in the RSNs that characterizes them. This, however, is a quite restrictive assumption as it can be expected that there exists important RSNs that are common to all subjects (across clusters). Not accounting for these common RSNs may negatively affect C-ICA’s ability to identify the subject clustering and the RSNs for each cluster. Therefore, in order to tackle this problem, two extensions of C-ICA (CCD1 and CCD2) are presented and compared to each other that allow to distinguish between (and extract from the data) both common and distinctive components. Both extensions differ in the procedure to identify the common components, with CCD2 selecting the common components out of a wider pool of possible components than CCD1. In a simulation study, the two C-ICA extensions are compared to C-ICA while manipulating the following factors: number of clusters, number of common and distinctive components, degree of overlap among clusters and amount of noise. Performance was determined by computing cluster recovery and component recovery, where component recovery was studied separately for the common components, the distinctive components and all components. Both extensions clearly outperformed C-ICA, with CCD2 performing the best. A mixed analysis of variance (ANOVA) showed that, the degree of overlap and the amount of noise in the data were the most influential factors, with large amounts of overlap and noise decreasing cluster and component recovery. Moreover, both extensions showed a slower deterioration of cluster and component recovery -with increasing overlap and noise- than C-ICA. Specifically for intermediate amounts of noise and overlap the differences between both extensions and C-ICA were the largest.Show less
Meta-analytic tree models (meta-CART) can be widely used to identify possible (multiple) interaction effects and examine how study characteristics explain the heterogeneity in study effect sizes....Show moreMeta-analytic tree models (meta-CART) can be widely used to identify possible (multiple) interaction effects and examine how study characteristics explain the heterogeneity in study effect sizes. The algorithm partitions individual studies in more homogeneous groups (i.e. terminal nodes) and in each node a summary effect size is estimated with a naive standard error. Unfortunately, tree based method can be unstable. Since the terminal nodes are generated by an algorithm rather than being predetermined, the complex search strategy that generates these naive standard errors fails to distinguish between a constant (global) and a local optimum and can be over-optimistic. In order to acquire more attainable confidence intervals of the summary effect sizes, the current study introduces a new bootstrap calibration approach to meta-CART models. With bootstrapping, the standards errors are re-estimated in order to correct this underestimation of the naive confidence intervals. The present study was conducted to test the performance of the new bootstrap method extensively via a simulation study and aims to provide more knowledge whether the new approach showed better coverage of the established tree models. The results of the simulation study are very promising. The new method increases the mean coverage, creates wider confidence intervals and provides more accurate summary effect sizes compared to the current naive method.Show less
This thesis argues about the impact of premigratory factors, as those were shaped in post civil war Greece, to the migration and integration experience of Greek 'guest' workers'to the Netherlands,...Show moreThis thesis argues about the impact of premigratory factors, as those were shaped in post civil war Greece, to the migration and integration experience of Greek 'guest' workers'to the Netherlands, in the period 1955 to 1981.Moreover, it follows the migrants' organizational trajectories making comparisons between Rotterdam and Utrecht.Show less
