Reduced neuronal efficacy in progressive mild cognitive impairment: a prospective fMRI study on visuospatial processing

Mild cognitive impairment (MCI) often refers to the preclinical stage of dementia, where the majority develop Alzheimer's disease (AD). Given that neurodegenerative burden and compensatory mechanisms might exist before accepted clinical symptoms of AD are noticeable, the current prospective study aimed to investigate the functioning of brain regions in the visuospatial networks responsible for preclinical symptoms in AD using event-related functional magnetic resonance imaging (fMRI). Eighteen MCI patients were evaluated and clinically followed for approximately 3 years. Five progressed to AD (PMCI) and eight remained stable (SMCI). Thirteen age-, gender- and education-matched controls also participated. An angle discrimination task with varying task demands was used. Brain activation patterns as well as task demand-dependent and -independent signal changes between the groups were investigated by using an extended general linear model including individual performance (reaction time [RT]) of each single trial. Similar behavioral (RT and accuracy) responses were observed between MCI patients and controls. A network of bilateral activations, e.g. dorsal pathway, which increased linearly with increasing task demand, was engaged in all subjects. Compared with SMCI patients and controls, PMCI patients showed a stronger relation between task demand and brain activity in left superior parietal lobules (SPL) as well as a general task demand-independent increased activation in left precuneus. Altered brain function can be detected at a group level in individuals that progress to AD before changes occur at the behavioral level. Increased parietal activation in PMCI could reflect a reduced neuronal efficacy due to accumulating AD pathology and might predict future clinical decline in patients with MCI.

Hierarchical Markov Random Fields Applied to Model Soft Tissue Deformations on Graphics Hardware

Many methodologies dealing with prediction or simulation of soft tissue deformations on medical image data require preprocessing of the data in order to produce a different shape representation that complies with standard methodologies, such as mass–spring networks, finite element method s (FEM). On the other hand, methodologies working directly on the image space normally do not take into account mechanical behavior of tissues and tend to lack physics foundations driving soft tissue deformations. This chapter presents a method to simulate soft tissue deformations based on coupled concepts from image analysis and mechanics theory. The proposed methodology is based on a robust stochastic approach that takes into account material properties retrieved directly from the image, concepts from continuum mechanics and FEM. The optimization framework is solved within a hierarchical Markov random field (HMRF) which is implemented on the graphics processor unit (GPU See Graphics processing unit ).

The gender typicality of faces and its impact on visual processing and on hiring decisions

Past research has shown that the gender typicality of applicants’ faces affects leadership selection irrespective of a candidate’s gender: A masculine facial appearance is congruent with masculine-typed leadership roles, thus masculine-looking applicants are hired more certainly than feminine-looking ones. In the present study, we extended this line of research by investigating hiring decisions for both masculine- and feminine-typed professional roles. Furthermore, we used eye tracking to examine the visual exploration of applicants’ portraits. Our results indicate that masculine-looking applicants were favored for the masculine-typed role (leader) and feminine-looking applicants for the feminine-typed role (team member). Eye movement patterns showed that information about gender category and facial appearance was integrated during first fixations of the portraits. Hiring decisions, however, were not based on this initial analysis, but occurred at a second stage, when the portrait was viewed in the context of considering the applicant for a specific job.

Special Tax Treatment as Trade Policy: A Database on Export Processing and Special Economic Zones

Many countries treat income generated via exports favourably, especially when production takes places in special zones known as export processing zones (EPZs). EPZs can be defined as specific, geographically defined zones or areas that are subject to special administration and that generally offer tax incentives, such as duty‐free imports when producing for export, exemption from other regulatory constraints linked to import for the domestic market, sometimes favourable treatment in terms of industrial regulation, and the streamlining of border clearing procedures. We describe a database of WTO Members that employ special economic zones as part of their industrial policy mix. This is based on WTO notification and monitoring through the WTO’s trade policy review mechanism (TPRM), supplemented with information from the ILO, World Bank, and primary sources. We also provide some rough analysis of the relationship between use of EPZs and the carbon intensity of exports, and relative levels of investment across countries with and without special zones.

Non-rigid Free-Form 2D-3D Registration Using Statistical Deformation Model

This paper presents a non-rigid free-from 2D-3D registration approach using statistical deformation model (SDM). In our approach the SDM is first constructed from a set of training data using a non-rigid registration algorithm based on b-spline free-form deformation to encode a priori information about the underlying anatomy. A novel intensity-based non-rigid 2D-3D registration algorithm is then presented to iteratively fit the 3D b-spline-based SDM to the 2D X-ray images of an unseen subject, which requires a computationally expensive inversion of the instantiated deformation in each iteration. In this paper, we propose to solve this challenge with a fast B-spline pseudo-inversion algorithm that is implemented on graphics processing unit (GPU). Experiments conducted on C-arm and X-ray images of cadaveric femurs demonstrate the efficacy of the present approach.

Emotional processing in a ten-session general psychiatric treatment for borderline personality disorder: a case study

This study examines the effects of a borderline-specific treatment, called general psychiatric management, on emotional change, outcome and therapeutic alliance of an outpatient presenting with borderline personality disorder. Based on the sequential model of emotional processing, emotional states were assessed in a 10-session setting. The case showed an increase in expressions of distress and no change in therapeutic alliance and tended towards general deterioration. Results suggest emotional processing may play a lesser role in general psychiatric management in early phase treatment than previously hypothezised.

Survey estimation and marginal effects in Stata

Stata is a general purpose software package that has become popular among various disciplines such as epidemiology, economics, or social sciences. Users like Stata for its scientific approach, its robustness and reliability, and the ease with which its functionality can be extended by user written programs. In this talk I will first give a brief overview of the functionality of Stata and then discuss two specific features: survey estimation and predictive margins/marginal effects. Most surveys are based on complex samples that contain multiple sampling stages, are stratified or clustered, and feature unequal selection probabilities. Standard estimators can produce misleading results in such samples unless the peculiarities of the sampling plan are taken into account. Stata offers survey statistics for complex samples for a wide variety of estimators and supports several variance estimation procedures such as linearization, jackknife, and balanced repeated replication (see Kreuter and Valliant, 2007, Stata Journal 7: 1-21). In the talk I will illustrate these features using applied examples and I will also show how user written commands can be adapted to support complex samples. Complex can also be the models we fit to our data, making it difficult to interpret them, especially in case of nonlinear or non-additive models (Mood, 2010, European Sociological Review 26: 67-82). Stata provides a number of highly useful commands to make results of such models accessible by computing and displaying predictive margins and marginal effects. In my talk I will discuss these commands provide various examples demonstrating their use.