Extraversion modulates functional connectivity hubs of resting‐state brain networks

Personality dimension extraversion describes individual differences in social behaviour and socio‐emotional functioning. The intrinsic functional connectivity patterns of the brain are reportedly associated with extraversion. However, whether or not extraversion is associated with functional hubs warrants clarification. Functional hubs are involved in the rapid integration of neural processing, and their dysfunction contributes to the development of neuropsychiatric disorders. In this study, we employed the functional connectivity density (FCD) method for the first time to distinguish the energy‐efficient hubs associated with extraversion. The resting‐state functional magnetic resonance imaging data of 71 healthy subjects were used in the analysis. Short‐range FCD was positively correlated with extraversion in the left cuneus, revealing a link between the local functional activity of this region and extraversion in risk‐taking. Long‐range FCD was negatively correlated with extraversion in the right superior frontal gyrus and the inferior frontal gyrus. Seed‐based resting‐state functional connectivity (RSFC) analyses revealed that a decreased long‐range FCD in individuals with high extraversion scores showed a low long‐range functional connectivity pattern between the medial and dorsolateral prefrontal cortex, middle temporal gyrus, and anterior cingulate cortex. This result suggests that decreased RSFC patterns are responsible for self‐esteem, self‐evaluation, and inhibitory behaviour system that account for the modulation and shaping of extraversion. Overall, our results emphasize specific brain hubs, and reveal long‐range functional connections in relation to extraversion, thereby providing a neurobiological basis of extraversion.     

Neural substrates of the ‘low‐level’ system for speech articulation: Evidence from primary opercular syndrome

We describe a patient with progressive disorder of speech, without language impairment (opercular syndrome). Morphometric analysis confirmed asymmetric volume reduction of the precentral areas (>left). Diffusion imaging showed significant white matter changes in the left frontal lobe, with specific involvement of the left corticobulbar tract and connections between supplementary/pre‐supplementary motor areas and the frontal operculum (frontal aslant tract). We suggest that the organization of expressive language includes a ‘low level’ motor system principally distributed in the left hemisphere that shows specific susceptibility to neurodegeneration, distinct from neural systems subtending praxic, and cognitive aspects of language.     

Distance stability analysis in multidimensional scaling using the jackknife method

Stability or sensitivity analysis is an important topic in data analysis that has received little attention in the application of multidimensional scaling (MDS), for which the only available approaches are given in terms of a coordinate‐based analytical jackknife methodology. Although in MDS the prime interest is in assessing the stability of the points in the configuration, this methodology may be influenced by imprecisions resulting from the inherently necessary Procrustes method. This paper proposes an analytical distance‐based jackknife procedure to study stability and cross‐validation in MDS in terms of the jackknife distances, which is not influenced by the Procrustes method. For each object, the corresponding jackknife estimated points are considered as naturally clustered points, and stability and cross‐validation are analysed in terms of the MDS distances arising from the jackknife procedure, on the basis of a weighted cluster‐MDS algorithm. A jackknife‐relevant configuration is also proposed for cross‐validation in terms of coordinates, in a cluster‐MDS framework.     

Developing new online calibration methods for multidimensional computerized adaptive testing

Multidimensional computerized adaptive testing (MCAT) has received increasing attention over the past few years in educational measurement. Like all other formats of CAT, item replenishment is an essential part of MCAT for its item bank maintenance and management, which governs retiring overexposed or obsolete items over time and replacing them with new ones. Moreover, calibration precision of the new items will directly affect the estimation accuracy of examinees’ ability vectors. In unidimensional CAT (UCAT) and cognitive diagnostic CAT, online calibration techniques have been developed to effectively calibrate new items. However, there has been very little discussion of online calibration in MCAT in the literature. Thus, this paper proposes new online calibration methods for MCAT based upon some popular methods used in UCAT. Three representative methods, Method A, the ‘one EM cycle’ method and the ‘multiple EM cycles’ method, are generalized to MCAT. Three simulation studies were conducted to compare the three new methods by manipulating three factors (test length, item bank design, and level of correlation between coordinate dimensions). The results showed that all the new methods were able to recover the item parameters accurately, and the adaptive online calibration designs showed some improvements compared to the random design under most conditions.     

Analysis of categorical moderators in mixed‐effects meta‐analysis: Consequences of using pooled versus separate estimates of the residual between‐studies variances

Subgroup analyses allow us to examine the influence of a categorical moderator on the effect size in meta‐analysis. We conducted a simulation study using a dichotomous moderator, and compared the impact of pooled versus separate estimates of the residual between‐studies variance on the statistical performance of the Q _{B (P)} and Q _{B (S)} tests for subgroup analyses assuming a mixed‐effects model. Our results suggested that similar performance can be expected as long as there are at least 20 studies and these are approximately balanced across categories. Conversely, when subgroups were unbalanced, the practical consequences of having heterogeneous residual between‐studies variances were more evident, with both tests leading to the wrong statistical conclusion more often than in the conditions with balanced subgroups. A pooled estimate should be preferred for most scenarios, unless the residual between‐studies variances are clearly different and there are enough studies in each category to obtain precise separate estimates.     

Non‐ignorable missingness item response theory models for choice effects in examinee‐selected items

Examinee‐selected item (ESI) design, in which examinees are required to respond to a fixed number of items in a given set, always yields incomplete data (i.e., when only the selected items are answered, data are missing for the others) that are likely non‐ignorable in likelihood inference. Standard item response theory (IRT) models become infeasible when ESI data are missing not at random (MNAR). To solve this problem, the authors propose a two‐dimensional IRT model that posits one unidimensional IRT model for observed data and another for nominal selection patterns. The two latent variables are assumed to follow a bivariate normal distribution. In this study, the mirt freeware package was adopted to estimate parameters. The authors conduct an experiment to demonstrate that ESI data are often non‐ignorable and to determine how to apply the new model to the data collected. Two follow‐up simulation studies are conducted to assess the parameter recovery of the new model and the consequences for parameter estimation of ignoring MNAR data. The results of the two simulation studies indicate good parameter recovery of the new model and poor parameter recovery when non‐ignorable missing data were mistakenly treated as ignorable.