Multilevel Dynamic Generalized Structured Component Analysis for Brain Connectivity Analysis in Functional Neuroimaging Data

We extend dynamic generalized structured component analysis (GSCA) to enhance its data-analytic capability in structural equation modeling of multi-subject time series data. Time series data of multiple subjects are typically hierarchically structured, where time points are nested within subjects who are in turn nested within a group. The proposed approach, named multilevel dynamic GSCA, accommodates the nested structure in time series data. Explicitly taking the nested structure into account, the proposed method allows investigating subject-wise variability of the loadings and path coefficients by looking at the variance estimates of the corresponding random effects, as well as fixed loadings between observed and latent variables and fixed path coefficients between latent variables. We demonstrate the effectiveness of the proposed approach by applying the method to the multi-subject functional neuroimaging data for brain connectivity analysis, where time series data-level measurements are nested within subjects.     

An Introduction to Latent Class Growth Analysis and Growth Mixture Modeling

In recent years, there has been a growing interest among researchers in the use of latent class and growth mixture modeling techniques for applications in the social and psychological sciences, in part due to advances in and availability of computer software designed for this purpose (e.g., Mplus and SAS Proc Traj). Latent growth modeling approaches, such as latent class growth analysis (LCGA) and growth mixture modeling (GMM), have been increasingly recognized for their usefulness for identifying homogeneous subpopulations within the larger heterogeneous population and for the identification of meaningful groups or classes of individuals. The purpose of this paper is to provide an overview of LCGA and GMM, compare the different techniques of latent growth modeling, discuss current debates and issues, and provide readers with a practical guide for conducting LCGA and GMM using the Mplus software.     

The Impact of Specific Psychotherapist Beliefs Regarding Managed Care on Prospective Psychotherapy Clients

The purpose of this study was to investigate the separate impact of each of thirteen therapist beliefs that, presented collectively, were previously found to have a significantly negative impact on prospective clients' attitudes toward managed care psychotherapy (Pomerantz, 2000). Participants in this study initially completed a brief questionnaire measuring their willingness to enter psychotherapy and their expectations regarding psychotherapy under managed care. Participants subsequently completed the same brief questionnaire again after being instructed to imagine seeing a hypothetical psychologist and being presented with the psychologist's supposed beliefs regarding managed care (which were actually derived from survey data by Murphy et al., 1998). Results suggest that almost every discrete therapist belief had a significantly negative impact on participants' attitudes toward managed care psychotherapy. Several specific therapist beliefs produced particularly salient negative effects. Implications regarding ethics and informed consent are discussed.     

Gray matter correlates of fluid,crystallized, and spatial intelligence: Testing the P-FIT model

The parieto-frontal integration theory (P-FIT) nominates several areas distributed throughout the brain as relevant for intelligence. This theory was derived from previously published studies using a variety of both imaging methods and tests of cognitive ability. Here we test this theory in a new sample of young healthy adults (N = 100) using a psychometric battery tapping fluid, crystallized, and spatial intelligence factors. High resolution structural MRI scans (3T) were obtained and analyzed with Voxel-based Morphometry (VBM). The main findings are consistent with the P-FIT, supporting the view that general intelligence (g) involves multiple cortical areas throughout the brain. Key regions include the dorsolateral prefrontal cortex, Broca's and Wernicke's areas, the somato-sensory association cortex, and the visual association cortex. Further, estimates of crystallized and spatial intelligence with g statistically removed, still share several brain areas with general intelligence, but also show some degree of uniqueness.     

Information-processing modules and their relative modality specificity

This research uses fMRI to understand the role of eight cortical regions in a relatively complex information-processing task. Modality of input (visual versus auditory) and modality of output (manual versus vocal) are manipulated. Two perceptual regions (auditory cortex and fusiform gyrus) only reflected perceptual encoding. Two motor regions were involved in information rehearsal as well as programming of overt actions. Two cortical regions (parietal and prefrontal) performed processing (retrieval and representational change) independent of input and output modality. The final two regions (anterior cingulate and caudate) were involved in control of cognition independent of modality of input or output and content of the material. An information-processing model, based on the ACT-R theory, is described that predicts the BOLD response in these regions. Different modules in the theory vary in the degree to which they are modality-specific and the degree to which they are involved in central versus peripheral cognitive processes.     

The psychology of ongoing threat: relative risk appraisal, the September 11 attacks, and terrorism-related fears

There are now replicated findings that posttraumatic stress disorder (PTSD) symptoms related to the September 11, 2001, attacks occurred in large numbers of persons who did not fit the traditional definition of exposure to a traumatic event. These data are not explained by traditional epidemiologic "bull's eye" disaster models, which assume the psychological effects are narrowly, geographically circumscribed, or by existing models of PTSD onset. In this article, the authors develop a researchable model to explain these and other terrorism-related phenomena by synthesizing research and concepts from the cognitive science, risk appraisal, traumatic stress, and anxiety disorders literatures. They propose the new term relative risk appraisal to capture the psychological function that is the missing link between the event and subjective response in these and other terrorism-related studies to date. Relative risk appraisal highlights the core notion from cognitive science that human perception is an active, multidimensional process, such that for unpredictable societal threats, proximity to the event is only one of several factors that influence behavioral responses. Addressing distortions in relative risk appraisal effectively could reduce individual and societal vulnerability to a wide range of adverse economic and ethnopolitical consequences to terrorist attacks. The authors present ways in which these concepts and related techniques can be helpful in treating persons with September 11- or terrorism-related distress or psychopathology.