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.     

Functional Generalized Structured Component Analysis

An extension of Generalized Structured Component Analysis (GSCA), called Functional GSCA, is proposed to analyze functional data that are considered to arise from an underlying smooth curve varying over time or other continua. GSCA has been geared for the analysis of multivariate data. Accordingly, it cannot deal with functional data that often involve different measurement occasions across participants and a large number of measurement occasions that exceed the number of participants. Functional GSCA addresses these issues by integrating GSCA with spline basis function expansions that represent infinite-dimensional curves onto a finite-dimensional space. For parameter estimation, functional GSCA minimizes a penalized least squares criterion by using an alternating penalized least squares estimation algorithm. The usefulness of functional GSCA is illustrated with gait data.     

Generalized Structured Component Analysis with Latent Interactions

Generalized structured component analysis (GSCA) is a component-based approach to structural equation modeling. In practice, researchers may often be interested in examining the interaction effects of latent variables. However, GSCA has been geared only for the specification and testing of the main effects of variables. Thus, an extension of GSCA is proposed to effectively deal with various types of interactions among latent variables. In the proposed method, a latent interaction is defined as a product of interacting latent variables. As a result, this method does not require the construction of additional indicators for latent interactions. Moreover, it can easily accommodate both exogenous and endogenous latent interactions. An alternating least-squares algorithm is developed to minimize a single optimization criterion for parameter estimation. A Monte Carlo simulation study is conducted to investigate the parameter recovery capability of the proposed method. An application is also presented to demonstrate the empirical usefulness of the proposed method.     

Contributions of Functional Neuroimaging to the Study of Social Cognition

ABSTRACT— Increasingly, researchers have been applying the methods of cognitive neuroscience—especially functional neuroimaging—to address questions about how humans make inferences about the mental states of others. At the same time, a number of critics have warned against the use of these new techniques by suggesting that functional neuroimaging has been unable to provide novel insights into the nature of social cognition. Addressing these critiques, this article briefly describes some of the ways in which functional neuroimaging has indeed redirected the study of the social mind, reviewing not only the novel data these techniques have provided but also the ways in which cognitive neuroscience has prompted researchers to consider entirely new questions about the organization of human social cognition. Such questions include whether or not there are cognitive processes dedicated for social thought; what the constituent parts of our social-cognitive system might be; how social cognition overlaps with other mental processes in previously unanticipated ways; and whether social cognition might play a privileged role in the human cognitive repertoire.     

Effects of Aging on Functional Connectivity of the Amygdala for Subsequent Memory of Negative Pictures: A Network Analysis of Functional Magnetic Resonance Imaging Data

ABSTRACT— Aging is associated with preserved enhancement of emotional memory, as well as with age-related reductions in memory for negative stimuli, but the neural networks underlying such alterations are not clear. We used a subsequent-memory paradigm to identify brain activity predicting enhanced emotional memory in young and older adults. Activity in the amygdala predicted enhanced emotional memory, with subsequent-memory activity greater for negative stimuli than for neutral stimuli, across age groups, a finding consistent with an overall enhancement of emotional memory. However, older adults recruited greater activity in anterior regions and less activity in posterior regions in general for negative stimuli that were subsequently remembered. Functional connectivity of the amygdala with the rest of the brain was consistent with age-related reductions in memory for negative stimuli: Older adults showed decreased functional connectivity between the amygdala and the hippocampus, but increased functional connectivity between the amygdala and dorsolateral prefrontal cortices. These findings suggest that age-related differences in the enhancement of emotional memory might reflect decreased connectivity between the amygdala and typical subsequent-memory regions, as well as the engagement of regulatory processes that inhibit emotional responses.     

Functional Neuroimaging in the Examination of Effects of Prenatal Alcohol Exposure

Functional neuroimaging offers the opportunity to understand the effect of prenatal alcohol exposure on the activities of the brain as well as providing a window into the relationship between neural activation and the behavioral outcomes that have been described in affected individuals. Several different methodologies have been used to examine the neurophysiological signal changes associated with different brain functions in prenatally exposed individuals and those diagnosed with fetal alcohol syndrome (FAS) or other fetal alcohol spectrum disorders (FASD). These include electroencephalography (EEG), positron emission tomography (PET), single-photon emission computed tomography (SPECT), and functional magnetic resonance imaging (fMRI). These studies demonstrate that it is feasible to use these technologies with this clinical population and that the damage to the central nervous system associated with prenatal alcohol exposure has widespread functional implications; however, currently, the literature in these areas is limited and unsystematic. Functional MRI with this clinical population has just begun to explore the implications of prenatal alcohol exposure with the first paper published in 2005. Other methodologies are similarly limited in scope. Nonetheless, these functional neuroimaging studies suggest that prenatal alcohol exposure, or a diagnosis of FAS, may lead to restrictions in neural efficiency or a global decrement in processing resources.     

Comparing patterns of component loadings: Principal Component Analysis (PCA) versus Independent Component Analysis (ICA) in analyzing multivariate non-normal data

Principal component analysis identifies uncorrelated components from correlated variables, and a few of these uncorrelated components usually account for most of the information in the input variables. Researchers interpret each component as a separate entity representing a latent trait or profile in a population. However, the components are guaranteed to be independent and uncorrelated only when the multivariate normality of the variables is assumed. If the normality assumption does not hold, components are guaranteed to be uncorrelated, but not independent. If the independence assumption is violated, each component cannot be uniquely interpreted because of contamination by other components. Therefore, in the present study, we introduced independent component analysis, whose components are uncorrelated and independent even when the multivariate normality assumption is violated, and each component carries unique information.     

Maximum Marginal Likelihood Estimation of a Monotonic Polynomial Generalized Partial Credit Model with Applications to Multiple Group Analysis

We present a semi-parametric approach to estimating item response functions (IRF) useful when the true IRF does not strictly follow commonly used functions. Our approach replaces the linear predictor of the generalized partial credit model with a monotonic polynomial. The model includes the regular generalized partial credit model at the lowest order polynomial. Our approach extends Liang’s (A semi-parametric approach to estimate IRFs, Unpublished doctoral dissertation, 2007) method for dichotomous item responses to the case of polytomous data. Furthermore, item parameter estimation is implemented with maximum marginal likelihood using the Bock–Aitkin EM algorithm, thereby facilitating multiple group analyses useful in operational settings. Our approach is demonstrated on both educational and psychological data. We present simulation results comparing our approach to more standard IRF estimation approaches and other non-parametric and semi-parametric alternatives.     

Principal Component Analysis of the College Date Rape Attitude Survey (CDRAS)

ABSTRACT

The College Date Rape Attitude Survey (CDRAS), a measure intended to assess attitudes related to risk for committing rape in adolescents and young adults, was examined to determine the principal component structure of rape-related attitudes in data collected on an undergraduate college sample. Results provide evidence that responses to the instrument are reliable with regard to internal consistency, and that the CDRAS measures four rape-related attitudes: Entitlement, Blame Shifting, Traditional Roles, and Overwhelming Sexual Arousal. The CDRAS could be used to elicit information about student's attitudes, which could then be used to develop, implement, and evaluate an intervention specific to the needs of that population.     

Modeling Differences in the Dimensionality of Multiblock Data by Means of Clusterwise Simultaneous Component Analysis

Given multivariate multiblock data (e.g., subjects nested in groups are measured on multiple variables), one may be interested in the nature and number of dimensions that underlie the variables, and in differences in dimensional structure across data blocks. To this end, clusterwise simultaneous component analysis (SCA) was proposed which simultaneously clusters blocks with a similar structure and performs an SCA per cluster. However, the number of components was restricted to be the same across clusters, which is often unrealistic. In this paper, this restriction is removed. The resulting challenges with respect to model estimation and selection are resolved.     

SLIPATM通气道临床应用的回顾性分析

回顾性分析SLIPATM通气道(喉罩)临床应用的优缺点及相关并发症.330例ASA1-3级各类全麻手术患者,快速诱导麻醉后插入SLIPATM通气道.记录脉搏血氧饱和度,呼气末二氧化碳分压,插入的难易程度及是否有反流、误吸和术后咽痛等相关并发症.结果为96%的患者容易插入SLIPATM通气道,10.9%的患者发生了一定程度胃胀气,血液痕迹残留SLIPATM通气道表面发生率为11.5%,术后咽痛的发生率为3.3%,无一例发生术后肺内感染.SLIPATM通气道操作方便,通气可靠,围术期并发症少,在临床的应用安全有效.     

Applications of rational-emotive therapy (RET) to love problems

Rational-emotive therapy (RET) can be particularly applied to individuals with serious love problems, such as people possessed by super-romantic love, limerents who are mired in obsessive-compulsive feelings, insensately jealous and possessive lovers, people who needlessly interfere with their encountering suitable partners, and those who suffer anguish and depression when they lose love. It also has important things to teach about keeping alive and enhancing normal love feelings. This paper outlines the RET theory of love and applies it to various amative difficulties.This article is to appear as a chapter inClinical applications of Rational-Emotive Therapy, edited by Albert Ellis and Michael Bernard, New York: Plenum, 1985.     

Second-Order Quantifier Elimination in Higher-Order Contexts with Applications to the Semantical Analysis of Conditionals

Second-order quantifier elimination in the context of classical logic emerged as a powerful technique in many applications, including the correspondence theory, relational databases, deductive and knowledge databases, knowledge representation, commonsense reasoning and approximate reasoning. In the current paper we first generalize the result of Nonnengart and Szałas [17] by allowing second-order variables to appear within higher-order contexts. Then we focus on a semantical analysis of conditionals, using the introduced technique and Gabbay’s semantics provided in [10] and substantially using a third-order accessibility relation. The analysis is done via finding correspondences between axioms involving conditionals and properties of the underlying third-order relation. Presented by Wojciech Buszkowski     

A Meta-Analysis of Structural and Functional Brain Imaging in Dementia of the Alzheimer's Type: A Neuroimaging Profile

We conducted a quantitative review of the imaging literature using meta-analytic methodology to characterize further the magnitude of hippocampal deficit in probable Alzheimer's disease (AD) and to determine whether other neuroanatomic structures in AD can better discriminate the disease from normal aging. Additionally, we parceled the discriminability of neuroanatomic structures by duration of disease to determine those structures most sensitive to AD in its early and late stages. One hundred twenty-one studies published between 1984 and 2000 met criteria for inclusion in the present analysis. In total, structural (i.e., CT and MRI) and functional (i.e., SPECT and PET) neuroimaging results from 3511 patients with AD, and 1632 normal healthy controls were recorded across meta-analyses. Our results include neuroimaging profiles for both early onset and longer duration patients with AD. In sum, these profiles yield a signature of diagnostic markers in both cortical and subcortical neuroanatomic areas. This signature is consistent with the clinical phenomenology of Alzheimer's dementia and should aid in the positive identification of AD.     

Heuristic Implementation of Dynamic Programming for Matrix Permutation Problems in Combinatorial Data Analysis

Dynamic programming methods for matrix permutation problems in combinatorial data analysis can produce globally-optimal solutions for matrices up to size 30×30, but are computationally infeasible for larger matrices because of enormous computer memory requirements. Branch-and-bound methods also guarantee globally-optimal solutions, but computation time considerations generally limit their applicability to matrix sizes no greater than 35×35. Accordingly, a variety of heuristic methods have been proposed for larger matrices, including iterative quadratic assignment, tabu search, simulated annealing, and variable neighborhood search. Although these heuristics can produce exceptional results, they are prone to converge to local optima where the permutation is difficult to dislodge via traditional neighborhood moves (e.g., pairwise interchanges, object-block relocations, object-block reversals, etc.). We show that a heuristic implementation of dynamic programming yields an efficient procedure for escaping local optima. Specifically, we propose applying dynamic programming to reasonably-sized subsequences of consecutive objects in the locally-optimal permutation, identified by simulated annealing, to further improve the value of the objective function. Experimental results are provided for three classic matrix permutation problems in the combinatorial data analysis literature: (a) maximizing a dominance index for an asymmetric proximity matrix; (b) least-squares unidimensional scaling of a symmetric dissimilarity matrix; and (c) approximating an anti-Robinson structure for a symmetric dissimilarity matrix. We are extremely grateful to the Associate Editor and two anonymous reviewers for helpful suggestions and corrections.     

广泛性焦虑障碍的治疗进展

广泛性焦虑障碍心理治疗、药物治疗均可选择.心理治疗主要是CBT.药物治疗主要是抗抑郁药物,SSRI、SNRI是一线选择,NICE推荐舍曲林作为初始治疗药物.对于难治性的患者,可以试用第二代抗精神病药物,如喹硫平.如果药物治疗有效,最佳治疗时间为一年.     

A Cautionary Note on Using G2(dif) to Assess Relative Model Fit in Categorical Data Analysis

The likelihood ratio test statistic G²(dif) is widely used for comparing the fit of nested models in categorical data analysis. In large samples, this statistic is distributed as a chi-square with degrees of freedom equal to the difference in degrees of freedom between the tested models, but only if the least restrictive model is correctly specified. Yet, this statistic is often used in applications without assessing the adequacy of the least restrictive model. This may result in incorrect substantive conclusions as the above large sample reference distribution for G²(dif) is no longer appropriate. Rather, its large sample distribution will depend on the degree of model misspecification of the least restrictive model. To illustrate this, a simulation study is performed where this statistic is used to compare nested item response theory models under various degrees of misspecification of the least restrictive model. G²(dif) was found to be robust only under small model misspecification of the least restrictive model. Consequently, we argue that some indication of the absolute goodness of fit of the least restrictive model is needed before employing G²(dif) to assess relative model fit.