Kognitive Basisfunktionen und motorisch-koordinative Kompetenzen in Abhängigkeit des Peerstatus bei Kindern zu Beginn der Schulzeit

Zusammenfassung. In der vorliegenden Studie wird der Frage nachgegangen, ob sich zwischen beliebten, durchschnittlichen, unbeachteten und zurückgewiesenen Kindern Unterschiede in spezifischen kognitiven und motorischen Fähigkeiten finden lassen. Zu drei verschiedenen Zeitpunkten wurden mit 177 regulär eingeschulten 7-jährigen Kindern Peernominationen und Peerratings erhoben, um reliable soziometrische Daten zu erhalten und ein Vergleich der beiden Methoden vorzunehmen. Außerdem wurde eine umfassende Testbatterie von insgesamt 20 Aufgaben in den Bereichen Informationsverarbeitungsgeschwindigkeit, Kurzzeit- und Arbeitsgedächtnis, Inhibition, Sprache und Motorik durchgeführt. Mit Ausnahme der Kurzzeitgedächtniskapazität wurden für alle Funktionsbereiche signifikante Unterschiede zwischen den Statusgruppen gefunden. Die kontinuierlichen soziometrischen Werte der Ratingmethode zeigten sich etwas sensitiver als die der Nominationsmethode. Korrelativ wurde mit beiden Methoden ersichtlich, dass bessere Leistungen im kognitiven und motorischen Bereich nicht nur mit mehr Beliebtheit zusammenhingen, sondern auch, dass schlechte Leistungen in Verbindung standen mit sozialer Zurückweisung. Abstract. In the present study, it was investigated whether popular, average, neglected, and rejected children differ with respect to specific cognitive and motor skills. Peer nomination and peer rating methods were used at three different points in time to obtain reliable sociometric data of 177 regularly enrolled 7-year-old children and to compare the two methods. Furthermore, a battery comprising 21 tasks was used to assess speed of information processing, short-term and working memory, inhibition, language, and motor skills. Significant differences were found between children of different status groups with respect to all studied abilities with the exception of short-term memory. The continuous sociometric scores of the rating method resulted to be slightly more sensitive than those obtained with the nomination method. However, correlative analyses with both methods showed that better performance on the cognitive and motor tasks was associated with popularity, whereas worse performance was related to social rejection.

Widespread Signals of Convergent Adaptation to High Altitude in Asia and America

Living at high altitude is one of the most difficult challenges that humans had to cope with during their evolution. Whereas several genomic studies have revealed some of the genetic bases of adaptations in Tibetan, Andean, and Ethiopian populations, relatively little evidence of convergent evolution to altitude in different continents has accumulated. This lack of evidence can be due to truly different evolutionary responses, but it can also be due to the low power of former studies that have mainly focused on populations from a single geographical region or performed separate analyses on multiple pairs of populations to avoid problems linked to shared histories between some populations. We introduce here a hierarchical Bayesian method to detect local adaptation that can deal with complex demographic histories. Our method can identify selection occurring at different scales, as well as convergent adaptation in different regions. We apply our approach to the analysis of a large SNP data set from low- and high-altitude human populations from America and Asia. The simultaneous analysis of these two geographic areas allows us to identify several candidate genome regions for altitudinal selection, and we show that convergent evolution among continents has been quite common. In addition to identifying several genes and biological processes involved in high-altitude adaptation, we identify two specific biological pathways that could have evolved in both continents to counter toxic effects induced by hypoxia.

Determination of Pelvic Orientation from Ultrasound Images Using Patch-SSMs and a Hierarchical Speed of Sound Compensation Strategy

In the field of computer assisted orthopedic surgery (CAOS) the anterior pelvic plane (APP) is a common concept to determine the pelvic orientation by digitizing distinct pelvic landmarks. As percutaneous palpation is - especially for obese patients - known to be error-prone, B-mode ultrasound (US) imaging could provide an alternative means. Several concepts of using ultrasound imaging to determine the APP landmarks have been introduced. In this paper we present a novel technique, which uses local patch statistical shape models (SSMs) and a hierarchical speed of sound compensation strategy for an accurate determination of the APP. These patches are independently matched and instantiated with respect to associated point clouds derived from the acquired ultrasound images. Potential inaccuracies due to the assumption of a constant speed of sound are compensated by an extended reconstruction scheme. We validated our method with in-vitro studies using a plastic bone covered with a soft-tissue simulation phantom and with a preliminary cadaver trial.

Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

German Validation of the Conners Adult ADHD Rating Scale-Self-Report: Confirmation of Factor Structure in a Large Sample of Participants With ADHD

Objective: The Conners Adult ADHD Rating Scales (CAARS) assess symptoms specific to adults that are frequently used and have been translated into German. The current study tests the factor structure of the CAARS in a large sample of German adults with ADHD and compares the means of the CAARS subscales with those of healthy German controls. Method: CAARS were completed by 466 participants with ADHD and 851 healthy control participants. Confirmatory factor analysis was used to establish model fit with the American original. Comparisons between participants with ADHD and healthy controls and influences of gender, age, and degree of education were analyzed. Results: Confirmatory factor analysis showed a very good fit with the model for the American original. Differences between ADHD participants and healthy controls on all Conners Adult ADHD Rating Scales-Self-Report (CAARS-S) subscales were substantial and significant. Conclusion: The factor structure of the original American model was successfully replicated in this sample of adult German ADHD participants. (J. of Att. Dis. 2012; XX(X) 1-XX).

An empirical comparison of methods for meta-analysis of diagnostic accuracy showed hierarchical models are necessary

OBJECTIVE: Meta-analysis of studies of the accuracy of diagnostic tests currently uses a variety of methods. Statistically rigorous hierarchical models require expertise and sophisticated software. We assessed whether any of the simpler methods can in practice give adequately accurate and reliable results. STUDY DESIGN AND SETTING: We reviewed six methods for meta-analysis of diagnostic accuracy: four simple commonly used methods (simple pooling, separate random-effects meta-analyses of sensitivity and specificity, separate meta-analyses of positive and negative likelihood ratios, and the Littenberg-Moses summary receiver operating characteristic [ROC] curve) and two more statistically rigorous approaches using hierarchical models (bivariate random-effects meta-analysis and hierarchical summary ROC curve analysis). We applied the methods to data from a sample of eight systematic reviews chosen to illustrate a variety of patterns of results. RESULTS: In each meta-analysis, there was substantial heterogeneity between the results of different studies. Simple pooling of results gave misleading summary estimates of sensitivity and specificity in some meta-analyses, and the Littenberg-Moses method produced summary ROC curves that diverged from those produced by more rigorous methods in some situations. CONCLUSION: The closely related hierarchical summary ROC curve or bivariate models should be used as the standard method for meta-analysis of diagnostic accuracy.

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 ).

Reference photographs for nasolabial appearance rating in unilateral cleft lip and palate

A popular method for nasolabial rating in unilateral cleft lip and palate (UCLP) is the Asher-McDade system consisting of a 5-point ordinal scale assessing nasal form, nasal symmetry, nasal profile, and vermilion border. The aim of the current study was to identify reference photographs illustrating this scale to facilitate its use.Four observers assessed nasolabial appearance on frontal and profile photographs of the nasolabial area of 42 children of Caucasian origin with a repaired UCLP at age 9 years. Cronbachs alpha, based on the individual scores of the 4 observers, ranged from 0.73 to 0.82 for the 4 nasolabial ratings, indicating a good reliability. The reliability of the overall score (mean of the 4 component scores) was also high (Cronbachs alpha, 0.83). Both for the nasolabial component ratings and for the overall score, duplicate measurement errors were small. The reliability for the mean of the 4 observers' scores was good, Spearman rank correlation coefficients ranging from 0.56 to 0.96.Subsequently, photographs were selected that showed the highest agreement among observers. For each of the 4 components (eg, nasal form, nasal deviation, nasal profile, and shape of the vermilion border), 5 photographs were selected to illustrate the whole range of the scale (score, 1-5), resulting in the selection of 20 pictures.It was concluded that nasolabial appearance rating can be performed reliably using a panel of judges and averaging the scores of all observers. Reference photographs, as developed from this study, may facilitate the rating task.

Hierarchical segmentation-assisted multimodal registration for MR brain images

Information theory-based metric such as mutual information (MI) is widely used as similarity measurement for multimodal registration. Nevertheless, this metric may lead to matching ambiguity for non-rigid registration. Moreover, maximization of MI alone does not necessarily produce an optimal solution. In this paper, we propose a segmentation-assisted similarity metric based on point-wise mutual information (PMI). This similarity metric, termed SPMI, enhances the registration accuracy by considering tissue classification probabilities as prior information, which is generated from an expectation maximization (EM) algorithm. Diffeomorphic demons is then adopted as the registration model and is optimized in a hierarchical framework (H-SPMI) based on different levels of anatomical structure as prior knowledge. The proposed method is evaluated using Brainweb synthetic data and clinical fMRI images. Both qualitative and quantitative assessment were performed as well as a sensitivity analysis to the segmentation error. Compared to the pure intensity-based approaches which only maximize mutual information, we show that the proposed algorithm provides significantly better accuracy on both synthetic and clinical data.