SpEx: a German-language dataset of speech and executive function performance

This work presents data from 148 German native speakers (20-55 years of age), who completed several speaking tasks, ranging from formal tests such as word production tests to more ecologically valid spontaneous tasks that were designed to mimic natural speech. This speech data is supplemented by performance measures on several standardised, computer-based executive functioning (EF) tests covering domains of working-memory, cognitive flexibility, inhibition, and attention. The speech and EF data are further complemented by a rich collection of demographic data that documents education level, family status, and physical and psychological well-being. Additionally, the dataset includes information of the participants' hormone levels (cortisol, progesterone, oestradiol, and testosterone) at the time of testing. This dataset is thus a carefully curated, expansive collection of data that spans over different EF domains and includes both formal speaking tests as well as spontaneous speaking tasks, supplemented by valuable phenotypical information. This will thus provide the unique opportunity to perform a variety of analyses in the context of speech, EF, and inter-individual differences, and to our knowledge is the first of its kind in the German language. We refer to this dataset as SpEx since it combines speech and executive functioning data. Researchers interested in conducting exploratory or hypothesis-driven analyses in the field of individual differences in language and executive functioning, are encouraged to request access to this resource. Applicants will then be provided with an encrypted version of the data which can be downloaded.

Sex classification from functional brain connectivity: Generalization to multiple datasets

Machine learning (ML) approaches are increasingly being applied to neuroimaging data. Studies in neuroscience typically have to rely on a limited set of training data which may impair the generalizability of ML models. However, it is still unclear which kind of training sample is best suited to optimize generalization performance. In the present study, we systematically investigated the generalization performance of sex classification models trained on the parcelwise connectivity profile of either single samples or compound samples of two different sizes. Generalization performance was quantified in terms of mean across-sample classification accuracy and spatial consistency of accurately classifying parcels. Our results indicate that the generalization performance of parcelwise classifiers (pwCs) trained on single dataset samples is dependent on the specific test samples. Certain datasets seem to "match" in the sense that classifiers trained on a sample from one dataset achieved a high accuracy when tested on the respected other one and vice versa. The pwCs trained on the compound samples demonstrated overall highest generalization performance for all test samples, including one derived from a dataset not included in building the training samples. Thus, our results indicate that both a large sample size and a heterogeneous data composition of a training sample have a central role in achieving generalizable results.

Sex classification from functional brain connectivity: Generalization to multiple datasets Generalizability of sex classifiers

Machine learning (ML) approaches are increasingly being applied to neuroimaging data. Studies in neuroscience typically have to rely on a limited set of training data which may impair the generalizability of ML models. However, it is still unclear which kind of training sample is best suited to optimize generalization performance. In the present study, we systematically investigated the generalization performance of sex classification models trained on the parcelwise connectivity profile of either single samples or a compound sample containing data from four different datasets. Generalization performance was quantified in terms of mean across-sample classification accuracy and spatial consistency of accurately classifying parcels. Our results indicate that generalization performance of pwCs trained on single dataset samples is dependent on the specific test samples. Certain datasets seem to "match" in the sense that classifiers trained on a sample from one dataset achieved a high accuracy when tested on the respected other one and vice versa. The pwC trained on the compound sample demonstrated overall highest generalization performance for all test samples, including one derived from a dataset not included in building the training samples. Thus, our results indicate that a big and heterogenous training sample comprising data of multiple datasets is best suited to achieve generalizable results.

Sex differences in intrinsic functional cortical organization reflect differences in network topology rather than cortical morphometry

Brain size robustly differs between sexes. However, the consequences of this anatomical dimorphism on sex differences in intrinsic brain function remain unclear. We investigated the extent to which sex differences in intrinsic cortical functional organization may be explained by differences in cortical morphometry, namely brain size, microstructure, and the geodesic distances of connectivity profiles. For this, we computed a low dimensional representation of functional cortical organization, the sensory-association axis, and identified widespread sex differences. Contrary to our expectations, observed sex differences in functional organization were not fundamentally associated with differences in brain size, microstructural organization, or geodesic distances, despite these morphometric properties being per se associated with functional organization and differing between sexes. Instead, functional sex differences in the sensory-association axis were associated with differences in functional connectivity profiles and network topology. Collectively, our findings suggest that sex differences in functional cortical organization extend beyond sex differences in cortical morphometry.

Teaser

Investigating sex differences in functional cortical organization and their association to differences in cortical morphometry.

Accurate sex prediction of cisgender and transgender individuals without brain size bias

The increasing use of machine learning approaches on neuroimaging data comes with the important concern of confounding variables which might lead to biased predictions and in turn spurious conclusions about the relationship between the features and the target. A prominent example is the brain size difference between women and men. This difference in total intracranial volume (TIV) can cause bias when employing machine learning approaches for the investigation of sex differences in brain morphology. A TIV-biased model will not capture qualitative sex differences in brain organization but rather learn to classify an individual's sex based on brain size differences, thus leading to spurious and misleading conclusions, for example when comparing brain morphology between cisgender- and transgender individuals. In this study, TIV bias in sex classification models applied to cis- and transgender individuals was systematically investigated by controlling for TIV either through featurewise confound removal or by matching the training samples for TIV. Our results provide strong evidence that models not biased by TIV can classify the sex of both cis- and transgender individuals with high accuracy, highlighting the importance of appropriate modeling to avoid bias in automated decision making.

A topography-based predictive framework for naturalistic viewing fMRI

Functional magnetic resonance imaging (fMRI) during naturalistic viewing (NV) provides exciting opportunities for studying brain functions in more ecologically valid settings. Understanding individual differences in brain functions during NV and their behavioural relevance has recently become an important goal. However, methods specifically designed for this purpose remain limited. Here, we propose a topography-based predictive framework (TOPF) to fill this methodological gap. TOPF identifies individual-specific evoked activity topographies in a data-driven manner and examines their behavioural relevance using a machine learning-based predictive framework. We validate TOPF on both NV and task-based fMRI data from multiple conditions. Our results show that TOPF effectively and stably captures individual differences in evoked brain activity and successfully predicts phenotypes across cognition, emotion and personality on unseen subjects from their activity topographies. Moreover, TOPF compares favourably with functional connectivity-based approaches in prediction performance, with the identified predictive brain regions being neurobiologically interpretable. Crucially, we highlight the importance of examining individual evoked brain activity topographies in advancing our understanding of the brain-behaviour relationship. We believe that the TOPF approach provides a simple but powerful tool for understanding brain-behaviour relationships on an individual level with a strong potential for clinical applications.

Deep learning-assisted radiomics facilitates multimodal prognostication for personalized treatment strategies in low-grade glioma

Determining the optimal course of treatment for low grade glioma (LGG) patients is challenging and frequently reliant on subjective judgment and limited scientific evidence. Our objective was to develop a comprehensive deep learning assisted radiomics model for assessing not only overall survival in LGG, but also the likelihood of future malignancy and glioma growth velocity. Thus, we retrospectively included 349 LGG patients to develop a prediction model using clinical, anatomical, and preoperative MRI data. Before performing radiomics analysis, a U2-model for glioma segmentation was utilized to prevent bias, yielding a mean whole tumor Dice score of 0.837. Overall survival and time to malignancy were estimated using Cox proportional hazard models. In a postoperative model, we derived a C-index of 0.82 (CI 0.79-0.86) for the training cohort over 10 years and 0.74 (Cl 0.64-0.84) for the test cohort. Preoperative models showed a C-index of 0.77 (Cl 0.73-0.82) for training and 0.67 (Cl 0.57-0.80) test sets. Our findings suggest that we can reliably predict the survival of a heterogeneous population of glioma patients in both preoperative and postoperative scenarios. Further, we demonstrate the utility of radiomics in predicting biological tumor activity, such as the time to malignancy and the LGG growth rate.

Naturalistic viewing increases individual identifiability based on connectivity within functional brain networks

Naturalistic viewing (NV) is currently considered a promising paradigm for studying individual differences in functional brain organization. While whole brain functional connectivity (FC) under NV has been relatively well characterized, so far little work has been done on a network level. Here, we extend current knowledge by characterizing the influence of NV on FC in fourteen meta-analytically derived brain networks considering three different movie stimuli in comparison to resting-state (RS). We show that NV increases identifiability of individuals over RS based on functional connectivity in certain, but not all networks. Furthermore, movie stimuli including a narrative appear more distinct from RS. In addition, we assess individual variability in network FC by comparing within- and between-subject similarity during NV and RS. We show that NV can evoke individually distinct NFC patterns by increasing inter-subject variability while retaining within-subject similarity. Crucially, our results highlight that this effect is not observable across all networks, but rather dependent on the network-stimulus combination. Our results confirm that NV can improve the detection of individual differences over RS and underline the importance of selecting the appropriate combination of movie and cognitive network for the research question at hand.

Confound-leakage: confound removal in machine learning leads to leakage

BACKGROUND

Machine learning (ML) approaches are a crucial component of modern data analysis in many fields, including epidemiology and medicine. Nonlinear ML methods often achieve accurate predictions, for instance, in personalized medicine, as they are capable of modeling complex relationships between features and the target. Problematically, ML models and their predictions can be biased by confounding information present in the features. To remove this spurious signal, researchers often employ featurewise linear confound regression (CR). While this is considered a standard approach for dealing with confounding, possible pitfalls of using CR in ML pipelines are not fully understood.

RESULTS

We provide new evidence that, contrary to general expectations, linear confound regression can increase the risk of confounding when combined with nonlinear ML approaches. Using a simple framework that uses the target as a confound, we show that information leaked via CR can increase null or moderate effects to near-perfect prediction. By shuffling the features, we provide evidence that this increase is indeed due to confound-leakage and not due to revealing of information. We then demonstrate the danger of confound-leakage in a real-world clinical application where the accuracy of predicting attention-deficit/hyperactivity disorder is overestimated using speech-derived features when using depression as a confound.

CONCLUSIONS

Mishandling or even amplifying confounding effects when building ML models due to confound-leakage, as shown, can lead to untrustworthy, biased, and unfair predictions. Our expose of the confound-leakage pitfall and provided guidelines for dealing with it can help create more robust and trustworthy ML models.

P11.27.B Whole genome DNA methylation as predictive biomarker in CNS WHO grade 2 and 3 oligodendroglioma patients receiving early postoperative treatment

Background

Oligodendrogliomas are glial tumors with a relatively favorable survival prognosis of >10 years. While immediate postoperative treatment prolongs survival, long-term toxicities of adjuvant radio-chemotherapy remain a concern. Predictive biomarkers guiding postoperative treatment decisions are limited.

Material and Methods

In this retrospective study, we included patients treated for a newly diagnosed oligodendroglioma (isocitrate dehydrogenase (IDH)-mutated, 1p/19q-codeleted, CNS WHO grades 2 and 3) in 1992 - 2019 at the Medical University of Vienna or the Kepler University Hospital Linz (Austria). Early treatment was defined as radiotherapy, chemotherapy, or both within 6 months after resection, whereas benefit from early treatment was defined as progression-free survival (PFS) above the median in the overall cohort. DNA methylation analysis was performed using Illumina MethylationEPIC 850k microarrays.

Results

Of all 201 eligible patients, sufficient tumor tissue for DNA methylation analysis was available in 46 patients. Of these, 25/46 (54.3%) were diagnosed with CNS WHO grade 2 and 21/46 (45.6%) with grade 3 oligodendroglioma. Median age at diagnosis was 41 years (range: 23-70). In total, 21/46 (45.6%) patients received early treatment, of whom 13/21 (61.9%) received radio-chemotherapy, 6/21 (28.6%) radiotherapy only and 2/21 (9.5%) chemotherapy only. Median PFS was 134.0 months (95%CI: 78.3 - not reached) in patients receiving early treatment versus 87.2 months (95%CI: 66.8 - 150) in patients who did not. In patients receiving early treatment, differences in DNA methylation profiles could be detected between patients who drew benefit from postoperative treatment (group 1) versus those who did not (group 2). Based on the top 1000 differentially methylated CpG sites between both groups, two clusters were detected which comprised either patients of group 1 or 2. Clustering was independent from gender, WHO grade, extent of resection, type of postoperative treatment, treating center, and O6-methylguanine-methyltransferease (MGMT) promoter methylation status. Gene set enrichment analysis of the top 1000 differentially methylated gene sites mapped to 694 genes showed differential methylation in genes involved in fibroblast growth receptor 1 (FGFR1) signaling, Wnt signaling, integrin signaling, and actin cytoskeleton regulation. Conversely, in patients not receiving early treatment, PFS did neither correlate with DNA methylation clustering nor with MGMT promoter methylation.

Conclusion

In our cohort, whole genome DNA methylation was associated with PFS in patients who received early postoperative treatment, suggesting a predictive but not prognostic role. As the predictive value of MGMT promoter methylation is limited in oligodendroglioma, whole genome DNA methylation should be considered in future clinical trials.

Lehramt-Studienwahlmotivation sagt Zielorientierungen vorher, pädagogisches Wissen und selbst eingeschätzte Kompetenz aber nur teilweise

Zusammenfassung. In Studie 1 wurde die Validität des Fragebogens FEMOLA (Fragebogen zur Erfassung der Motivation für die Wahl des Lehramtsstudiums) durch Überprüfung der internen Struktur untersucht. Hierzu wurden anhand von Daten von N = 1467 Lehramtsstudierenden zwei in der Literatur vorgeschlagene Faktorenstrukturen verglichen, wobei sich zeigte, dass eine Lösung mit sieben Faktoren besser zu den Daten passte als die ursprünglich vorgeschlagene Lösung mit sechs Faktoren. Anschließend wurde die Messinvarianz über nach angestrebter Schulform gebildete Gruppen von Lehramtsstudierenden (Grundschule, Förderschule, Sekundarstufe I und Gymnasium) untersucht; hier konnte schwache Invarianz (gleiche Faktorladungen) festgestellt werden. In Studie 2 wurde anhand von Längsschnittdaten ( N = 442) untersucht, ob pädagogisches Wissen und selbst eingeschätzte Unterrichtskompetenz bei Lehramtsstudierenden anhand der Studienwahlmotivation und Zielorientierungen (Lernziele, Annäherungs- und Vermeidungs-Leistungsziele sowie Arbeitsvermeidung) vorhergesagt werden können. Dabei wurde mittels eines Strukturgleichungsmodells u.a. die Annahme geprüft, dass die Effekte der Studienwahlmotivation durch die Zielorientierungen vermittelt werden; der Einfluss von Intelligenz wurde kontrolliert. Erwartungskonform war die Studienwahl aus intrinsischer Motivation mit einer höheren Lernzielorientierung und einer niedrigeren Tendenz zur Arbeitsvermeidung verbunden; die Studienwahl aus extrinsischer Motivation hingegen mit höheren Ausprägungen von Annäherungs- und Vermeidungs-Leistungszielen sowie einer stärkeren Tendenz zur Arbeitsvermeidung. Die durch die Zielorientierungen vermittelten Pfade von den Studienwahlmotivations-Faktoren zu den Kriterien erwiesen sich jedoch weitgehend als nicht signifikant. Beide Kriterien waren zudem nur schwach miteinander korreliert und für Intelligenz ergab sich nur mit dem pädagogischen Wissen ein signifikanter Zusammenhang. Ursachen für diese Befunde und Abweichungen von vorliegenden Studien werden diskutiert.

Is it left or is it right? A classification approach for investigating hemispheric differences in low and high dimensionality

Hemispheric asymmetries, i.e., differences between the two halves of the brain, have extensively been studied with respect to both structure and function. Commonly employed pairwise comparisons between left and right are suitable for finding differences between the hemispheres, but they come with several caveats when assessing multiple asymmetries. What is more, they are not designed for identifying the characterizing features of each hemisphere. Here, we present a novel data-driven framework—based on machine learning-based classification—for identifying the characterizing features that underlie hemispheric differences. Using voxel-based morphometry data from two different samples (n = 226, n = 216), we separated the hemispheres along the midline and used two different pipelines: First, for investigating global differences, we embedded the hemispheres into a two-dimensional space and applied a classifier to assess if the hemispheres are distinguishable in their low-dimensional representation. Second, to investigate which voxels show systematic hemispheric differences, we employed two classification approaches promoting feature selection in high dimensions. The two hemispheres were accurately classifiable in both their low-dimensional (accuracies: dataset 1 = 0.838; dataset 2 = 0.850) and high-dimensional (accuracies: dataset 1 = 0.966; dataset 2 = 0.959) representations. In low dimensions, classification of the right hemisphere showed higher precision (dataset 1 = 0.862; dataset 2 = 0.894) compared to the left hemisphere (dataset 1 = 0.818; dataset 2 = 0.816). A feature selection algorithm in the high-dimensional analysis identified voxels that most contribute to accurate classification. In addition, the map of contributing voxels showed a better overlap with moderate to highly lateralized voxels, whereas conventional t test with threshold-free cluster enhancement best resembled the LQ map at lower thresholds. Both the low- and high-dimensional classifiers were capable of identifying the hemispheres in subsamples of the datasets, such as males, females, right-handed, or non-right-handed participants. Our study indicates that hemisphere classification is capable of identifying the hemisphere in their low- and high-dimensional representation as well as delineating brain asymmetries. The concept of hemisphere classifiability thus allows a change in perspective, from asking what differs between the hemispheres towards focusing on the features needed to identify the left and right hemispheres. Taking this perspective on hemispheric differences may contribute to our understanding of what makes each hemisphere special.

P14.14 Adjuvant treatment versus initial observation in newly diagnosed WHO grade II and grade III oligodendroglioma: real-life data from two academic, tertiary care centers in Austria

BACKGROUND

Oligodendrogliomas are rare, slow-growing brain tumors with a survival prognosis of >10 years. Although adjuvant radio-chemotherapy has been shown to prolong survival, aggressive treatment comes at the cost of increased toxicity. Systematic data on the optimal timing of adjuvant treatment in oligodendroglioma are lacking.

MATERIAL AND METHODS

Patients treated for a newly diagnosed IDH-mutated, 1p/19q-codeleted oligodendroglioma (WHO grades II/III) in 2000 - 2018 at the Medical University of Vienna or the Kepler University Hospital Linz (Austria) were included in this retrospective study. Adjuvant treatment was defined as radiotherapy (RT), chemotherapy (CHT) or radio-chemotherapy (R-CHT) within 6 months after resection in the absence of progression. “Wait and see” was defined as regular follow up with magnetic resonance imaging and treatment at progression.

RESULTS

185 patients were identified, comprising 123/185 (66.5%) WHO grade II and 62/185 (33.5%) WHO grade III oligodendrogliomas. Median age at diagnosis was 42 years (range: 20–82). Gross total resection (GTR) could be achieved in 77/178 (42.3%) evaluable patients. Adjuvant treatment was applied in 63/185 (38.2%) patients, of whom 43/63 (68.3%) underwent R-CHT, 9/63 (14.3%) CHT only and 11/63 (17.5%) RT only. 43/52 (82.7%) received temozolomide-based treatment, 1/52 (1.9%) procarbazine, lomustine and vincristine (PCV), 1/52 dacarbazine/fotemustine and in 7/52 (13.5%) patients, no data on used regimens was available. Adjuvant treatment was more frequently applied in WHO grade 3 tumors (p<0.001), while there was no association of adjuvant treatment with extent of resection (p=0.24). Patients after GTR who underwent adjuvant therapy presented with longer progression-free survival (PFS) compared to patients initially managed with observation (median: 150 months, 95%CI: 100 - not reached (n.r.) vs. median: 101 months, 95%CI: 73.2–115; p=0.053). In non-GTR tumors, patients with adjuvant therapy presented with a significantly longer median PFS of 107.5 months (95%CI: 62.8-n.r.) as compared to patients initially managed with observation (45.3 months, 95%CI: 41.2–78.8; p=0.025).

CONCLUSION

The application of adjuvant therapy was associated with favorable PFS in patients who underwent resection of newly diagnosed oligodendroglioma in this retrospective study. Prospective clinical trials should investigate the risks and benefits of adjuvant treatment versus initial observation in patients with oligodendroglioma.

OS08.4.A Retrospective analysis of in vivo 1H-magnetic resonance spectroscopy based on a machine learning approach enables reliable prediction of IDH mutation in patients with glioma

BACKGROUND

Mutation of isocitrate dehydrogenase (IDH) is not only an important landmark in the development of low-grade gliomas, but also has prognostic significance and is a potential therapeutic target. There is a high need to determinate IDH mutation status at diagnosis and during the course of therapy in a non-invasive and reliable manner. We established a machine learning approach based on a support vector machine to detect IDH mutation status in in vivo standard 1H-magnetic resonance spectroscopy (1H-MRS) at 3T with an accuracy of 88.2%, a sensitivity of 95.5% (95% CI, 77.2–99.9%), and a specificity of 75% (95% CI, 42.85–94.5%) in a prospective monocentric clinical trial. Here, the same method is applied in a retrospective cohort at 1.5T and tested for transferability.

MATERIAL AND METHODS

Validation cohort. The validation cohort comprised 100 patients with glioma for which standard in vivo 1H-MRS spectra had been acquired between 2002 and 2007. Standard single voxel spectroscopy had been measured at 1.5T using a PRESS sequence with a TR of 1500ms and a TE of 30ms. One sample had to be excluded due to non-malignant histology and for 15 samples the IDH mutation status was not available. Therefore, the validation cohort comprised 84 samples, of which 35 were bearing an IDH mutation in immunohistochemistry (sequencing for confirmation is outstanding). Machine learning. To transfer our method to an independent validation cohort our previously established machine learning approach was first trained on all samples of the 3T group. The trained algorithm was then applied to the data of the validation cohort. Here, among other factors the different field strengths, with which the spectra were acquired (3T vs. 1.5T) had to be considered.

RESULTS

27 samples of the validation cohort had to be excluded due to poor spectra quality. Our approach correctly detected IDH mutation status in 47 of 62 patients (75.8%), although the technical conditions were significantly different from our published prospective cohort. 17 of 30 patients bearing an IDH mutation were correctly identified, while 30 of 32 wild type patients were determined successfully.

CONCLUSION

Our approach to detect IDH mutation status has promising application in an unselected retrospective cohort, demonstrating transferability across different technical conditions. Further investigations to improve our technique and an advanced neuropathological processing of the samples are planned.

A machine learning approach for the factorization of psychometric data with application to the Delis Kaplan Executive Function System

While a replicability crisis has shaken psychological sciences, the replicability of multivariate approaches for psychometric data factorization has received little attention. In particular, Exploratory Factor Analysis (EFA) is frequently promoted as the gold standard in psychological sciences. However, the application of EFA to executive functioning, a core concept in psychology and cognitive neuroscience, has led to divergent conceptual models. This heterogeneity severely limits the generalizability and replicability of findings. To tackle this issue, in this study, we propose to capitalize on a machine learning approach, OPNMF (Orthonormal Projective Non-Negative Factorization), and leverage internal cross-validation to promote generalizability to an independent dataset. We examined its application on the scores of 334 adults at the Delis-Kaplan Executive Function System (D-KEFS), while comparing to standard EFA and Principal Component Analysis (PCA). We further evaluated the replicability of the derived factorization across specific gender and age subsamples. Overall, OPNMF and PCA both converge towards a two-factor model as the best data-fit model. The derived factorization suggests a division between low-level and high-level executive functioning measures, a model further supported in subsamples. In contrast, EFA, highlighted a five-factor model which reflects the segregation of the D-KEFS battery into its main tasks while still clustering higher-level tasks together. However, this model was poorly supported in the subsamples. Thus, the parsimonious two-factors model revealed by OPNMF encompasses the more complex factorization yielded by EFA while enjoying higher generalizability. Hence, OPNMF provides a conceptually meaningful, technically robust, and generalizable factorization for psychometric tools.

Comprehensive verbal fluency features predict executive function performance

Semantic verbal fluency (sVF) tasks are commonly used in clinical diagnostic batteries as well as in a research context. When performing sVF tasks to assess executive functions (EFs) the sum of correctly produced words is the main measure. Although previous research indicates potentially better insights into EF performance by the use of finer grained sVF information, this has not yet been objectively evaluated. To investigate the potential of employing a finer grained sVF feature set to predict EF performance, healthy monolingual German speaking participants (n = 230) were tested with a comprehensive EF test battery and sVF tasks, from which features including sum scores, error types, speech breaks and semantic relatedness were extracted. A machine learning method was applied to predict EF scores from sVF features in previously unseen subjects. To investigate the predictive power of the advanced sVF feature set, we compared it to the commonly used sum score analysis. Results revealed that 8 / 14 EF tests were predicted significantly using the comprehensive sVF feature set, which outperformed sum scores particularly in predicting cognitive flexibility and inhibitory processes. These findings highlight the predictive potential of a comprehensive evaluation of sVF tasks which might be used as diagnostic screening of EFs.

Imaging evolution of the primate brain: the next frontier?

Evolution, as we currently understand it, strikes a delicate balance between animals' ancestral history and adaptations to their current niche. Similarities between species are generally considered inherited from a common ancestor whereas observed differences are considered as more recent evolution. Hence comparing species can provide insights into the evolutionary history. Comparative neuroimaging has recently emerged as a novel subdiscipline, which uses magnetic resonance imaging (MRI) to identify similarities and differences in brain structure and function across species. Whereas invasive histological and molecular techniques are superior in spatial resolution, they are laborious, post-mortem, and oftentimes limited to specific species. Neuroimaging, by comparison, has the advantages of being applicable across species and allows for fast, whole-brain, repeatable, and multi-modal measurements of the structure and function in living brains and post-mortem tissue. In this review, we summarise the current state of the art in comparative anatomy and function of the brain and gather together the main scientific questions to be explored in the future of the fascinating new field of brain evolution derived from comparative neuroimaging.

Sex differences in the brain: More than just male or female

Sex differences in the brain are widely studied, but results are often inconsistent and it is assumed that many negative findings are not even being reported. The lack of consistent findings might be based on the highly questionable assumption of a clear-cut sexual dimorphism in brain structure and function, that underlies commonly used group comparisons between males and females. Without having to rely on this assumption, state of the art statistical learning methods based on large neuroimaging data sets might offer the tools necessary to disentangle the complex pattern of sex-related variations in brain structure and organization.

Clonal evolution in diffuse large B-cell lymphoma with central nervous system recurrence

BACKGROUND

The prognosis of patients with secondary central nervous system lymphoma (SCNSL) is poor and despite massive advances in understanding the mutational landscape of primary diffuse large B-cell lymphoma (DLBCL), the genetic comparison to SCNSL is still lacking. We therefore collected paired samples from six patients with DLBCL with available biopsies from a lymph node (LN) at primary diagnosis and the central nervous system (CNS) at recurrence.

PATIENTS AND METHODS

A targeted, massively parallel sequencing approach was used to analyze 216 genes recurrently mutated in DLBCL. Healthy tissue from each patient was also sequenced in order to exclude germline mutations. The results of the primary biopsies were compared with those of the CNS recurrences to depict the genetic background of SCNSL and evaluate clonal evolution.

RESULTS

Sequencing was successful in five patients, all of whom had at least one discordant mutation that was not detected in one of their samples. Four patients had mutations that were found in the CNS but not in the primary LN. Discordant mutations were found in genes known to be important in lymphoma biology such as MYC, CARD11, EP300 and CCND3. Two patients had a Jaccard similarity coefficient below 0.5 indicating substantial genetic differences between the primary LN and the CNS recurrence.

CONCLUSIONS

This analysis gives an insight into the genetic landscape of SCNSL and confirms the results of our previous study on patients with systemic recurrence of DLBCL with evidence of substantial clonal diversification at relapse in some patients, which might be one of the mechanisms of treatment resistance.

Executive functions predict verbal fluency scores in healthy participants

While there is a clear link between impairments of executive functions (EFs), i.e. cognitive control mechanisms that facilitate goal-directed behavior, and speech problems, it is so far unclear exactly which of the complex subdomains of EFs most strongly contribute to speech performance, as measured by verbal fluency (VF) tasks. Furthermore, the impact of intra-individual variability is largely unknown. This study on healthy participants (n = 235) shows that the use of a relevance vector machine approach allows for the prediction of VF performance from EF scores. Based on a comprehensive set of EF scores, results identified cognitive flexibility and inhibition as well as processing speed as strongest predictors for VF performance, but also highlighted a modulatory influence of fluctuating hormone levels. These findings demonstrate that speech production performance is strongly linked to specific EF subdomains, but they also suggest that inter-individual differences should be taken into account.

The relationship between language ability and brain activity across language processes and modalities

Existing neuroimaging studies on the relationship between language ability and brain activity have found contradictory evidence: On the one hand, increased activity with higher language ability has been interpreted as deeper or more adaptive language processing. On the other hand, decreased activity with higher language ability has been interpreted as more efficient language processing. In contrast to previous studies, the current study investigated the relationship between language ability and neural activity across different language processes and modalities while keeping non-linguistic cognitive task demands to a minimum. fMRI data were collected from 22 healthy adults performing a sentence listening task, a sentence reading task and a phonological production task. Outside the MRI scanner, language ability was assessed with the verbal scale of the Wechsler Abbreviated Scale of Intelligence (WASI-II) and a verbal fluency task. As expected, sentence comprehension activated the left anterior temporal lobe while phonological processing activated the left inferior frontal gyrus. Higher language ability was associated with increased activity in the left temporal lobe during auditory sentence processing and with increased activity in the left frontal lobe during phonological processing, reflected in both, higher intensity and greater extent of activations. Evidence for decreased activity with higher language ability was less consistent and restricted to verbal fluency. Together, the results predominantly support the hypothesis of deeper language processing in individuals with higher language ability. The consistency of results across language processes, modalities, and brain regions suggests a general positive link between language abilities and brain activity within the core language network. However, a negative relationship seems to exist for non-linguistic cognitive functions located outside the language network.

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.

Semantic and attentional networks in bilingual processing: fMRI connectivity signatures of translation directionality

Comparisons between backward and forward translation (BT, FT) have long illuminated the organization of bilingual memory, with neuroscientific evidence indicating that FT would involve greater linguistic and attentional demands. However, no study has directly assessed the functional interaction between relevant mechanisms. Against this background, we conducted the first fMRI investigation of functional connectivity (FC) differences between BT and FT. In addition to yielding lower behavioral outcomes, FT was characterized by increased FC between a core semantic hub (the left anterior temporal lobe, ATL) and key nodes of attentional and vigilance networks (left inferior frontal, left orbitofrontal, and bilateral parietal clusters). Instead, distinct FC patterns for BT emerged only between the left ATL and the right thalamus, a region implicated in automatic relaying of sensory information to cortical regions. Therefore, FT seems to involve enhanced coupling between semantic and attentional mechanisms, suggesting that asymmetries in cross-language processing reflect dynamic interactions between linguistic and domain-general systems.

Sex Classification by Resting State Brain Connectivity

A large amount of brain imaging research has focused on group studies delineating differences between males and females with respect to both cognitive performance as well as structural and functional brain organization. To supplement existing findings, the present study employed a machine learning approach to assess how accurately participants' sex can be classified based on spatially specific resting state (RS) brain connectivity, using 2 samples from the Human Connectome Project (n1 = 434, n2 = 310) and 1 fully independent sample from the 1000BRAINS study (n = 941). The classifier, which was trained on 1 sample and tested on the other 2, was able to reliably classify sex, both within sample and across independent samples, differing both with respect to imaging parameters and sample characteristics. Brain regions displaying highest sex classification accuracies were mainly located along the cingulate cortex, medial and lateral frontal cortex, temporoparietal regions, insula, and precuneus. These areas were stable across samples and match well with previously described sex differences in functional brain organization. While our data show a clear link between sex and regionally specific brain connectivity, they do not support a clear-cut dimorphism in functional brain organization that is driven by sex alone.

Neurobiological Divergence of the Positive and Negative Schizophrenia Subtypes Identified on a New Factor Structure of Psychopathology Using Non-negative Factorization: An International Machine Learning Study

BACKGROUND

Disentangling psychopathological heterogeneity in schizophrenia is challenging, and previous results remain inconclusive. We employed advanced machine learning to identify a stable and generalizable factorization of the Positive and Negative Syndrome Scale and used it to identify psychopathological subtypes as well as their neurobiological differentiations.

METHODS

Positive and Negative Syndrome Scale data from the Pharmacotherapy Monitoring and Outcome Survey cohort (1545 patients; 586 followed up after 1.35 ± 0.70 years) were used for learning the factor structure by an orthonormal projective non-negative factorization. An international sample, pooled from 9 medical centers across Europe, the United States, and Asia (490 patients), was used for validation. Patients were clustered into psychopathological subtypes based on the identified factor structure, and the neurobiological divergence between the subtypes was assessed by classification analysis on functional magnetic resonance imaging connectivity patterns.

RESULTS

A 4-factor structure representing negative, positive, affective, and cognitive symptoms was identified as the most stable and generalizable representation of psychopathology. It showed higher internal consistency than the original Positive and Negative Syndrome Scale subscales and previously proposed factor models. Based on this representation, the positive-negative dichotomy was confirmed as the (only) robust psychopathological subtypes, and these subtypes were longitudinally stable in about 80% of the repeatedly assessed patients. Finally, the individual subtype could be predicted with good accuracy from functional connectivity profiles of the ventromedial frontal cortex, temporoparietal junction, and precuneus.

CONCLUSIONS

Machine learning applied to multisite data with cross-validation yielded a factorization generalizable across populations and medical systems. Together with subtyping and the demonstrated ability to predict subtype membership from neuroimaging data, this work further disentangles the heterogeneity in schizophrenia.

Cefazolin versus anti-staphylococcal penicillins for the treatment of patients with Staphylococcus aureus bacteraemia

BACKGROUND

For patients with bacteraemia caused by methicillin-sensitive Staphylococcus aureus anti-staphylococcal penicillins (ASPs) or cefazolin are agents of choice. While ASPs are potentially nephrotoxic, cefazolin may be less effective in some S. aureus strains due to an inoculum effect.

OBJECTIVES

To perform a systematic literature review and meta-analysis assessing current evidence comparing cefazolin with ASPs for patients with S. aureus bacteraemia (SAB).

METHODS

We searched MEDLINE, ISI Web of Science (Science Citation Index Expanded) and the Cochrane Database as well as clinicaltrials.gov from inception to 26 June 2018. All studies investigating the effects of cefazolin versus ASP in patients with methicillin-sensitive SAB were eligible for inclusion regardless of study design, publication status or language. Additional information was requested by direct author contact. A meta-analysis to estimate relative risks (RRs) with the corresponding 95% confidence intervals (CIs) was performed. Statistical heterogeneity was estimated using I². The primary endpoint was 90-day all-cause mortality. The Newcastle-Ottawa Scale (NOS) and Grading of Recommendations Assessment, Development and Evaluation (GRADE) were used for study and data quality assessment.

RESULTS

Fourteen non-randomized studies were included. Seven reported the primary endpoint (RR 0.71 (0.50, 1.02), low quality of evidence). Cefazolin treatment may be associated with lower 30-day mortality rates (RR 0.70 (0.54, 0.91), low quality of evidence) and less nephrotoxicity (RR 0.36 (0.21, 0.59), (low quality of evidence)). We are uncertain whether cefazolin and ASP differ regarding treatment failure/relapse as the quality of the evidence has been assessed as very low (RR of 0.84 (0.59, 1.18)). For patients with endocarditis (RR 0.71 (0.12, 4.05)) or abscesses (RR 1.17 (0.30, 4.63)), cefazolin treatment may be associated with equal 30-day and 90-day mortality (low quality of evidence).

CONCLUSIONS

Cefazolin seemed to be at least equally as effective as ASPs while being associated with less nephrotoxicity.

MicroRNAs 143 and 150 in whole blood enable detection of T-cell immunoparalysis in sepsis

Background

Currently, no suitable clinical marker for detection of septic immunosuppression is available. We aimed at identifying microRNAs that could serve as biomarkers of T-cell mediated immunoparalysis in sepsis.

Methods

RNA was isolated from purified T-cells or from whole blood cells obtained from septic patients and healthy volunteers. Differentially regulated miRNAs were identified by miRNA Microarray (n = 7). Validation was performed via qPCR (n = 31).

Results

T-cells of septic patients revealed characteristics of immunosuppression: Pro-inflammatory miR-150 and miR-342 were downregulated, whereas anti-inflammatory miR-15a, miR-16, miR-93, miR-143, miR-223 and miR-424 were upregulated. Assessment of T-cell effector status showed significantly reduced mRNA-levels of IL2, IL7R and ICOS, and increased levels of IL4, IL10 and TGF-β. The individual extent of immunosuppression differed markedly. MicroRNA-143, − 150 and − 223 independently indicated T-cell immunoparalysis and significantly correlated with patient’s IL7R-/ICOS-expression and SOFA-scores. In whole blood, composed of innate and adaptive immune cells, both traits of immunosuppression and hyperinflammation were detected. Importantly, miR-143 and miR-150 – both predominantly expressed in T-cells – retained strong power of discrimination also in whole blood samples.

Conclusions

These findings suggest miR-143 and miR-150 as promising markers for detection of T-cell immunosuppression in whole blood and may help to develop new approaches for miRNA-based diagnostic in sepsis.

Electronic supplementary material

The online version of this article (10.1186/s10020-018-0056-z) contains supplementary material, which is available to authorized users.

CXCL13 as a diagnostic marker of neuroborreliosis and other neuroinflammatory disorders in an unselected group of patients

BACKGROUND

The C-X-C motif chemokine ligand 13 (CXCL13) and its receptor CXCR5 play an important role in the homing of B-lymphocytes. As a biomarker in the cerebrospinal fluid (CSF), CXCL13 has increasingly been used for the diagnosis of neuroborreliosis (NB). We evaluated the diagnostic and prognostic potential of CXCL13 for NB and other neuroinflammatory diseases in an unselected cohort, paying attention to those patients particularly who might benefit from newly emerging CXCL13-directed therapies.

METHODS

We report the CSF CXCL13 concentrations and other relevant baseline characteristics for an unselected cohort of 459 patients. We compare different diagnostic groups and analyse the sensitivity and specificity of CSF CXCL13 as a marker of NB. The course of the CXCL13 concentrations is reported in a subgroup of 19 patients.

RESULTS

We confirm the high diagnostic yield of CXCL13 for NB in this unselected cohort. The optimal cut-off for the reliable diagnosis of NB was 93.83 pg/ml, resulting in a sensitivity and specificity of 95 and 97%, respectively (positive predictive value 55.9%, negative predictive value 99.8%), surpassing the sensitivity of both serological testing and PCR. CSF CXCL13 concentration showed a swift response to therapy. Non-NB patients with high CSF CXCL13 concentrations suffered from meningeosis neoplastica or infectious encephalitis.

CONCLUSIONS

CXCL13 is a valuable tool for the diagnosis and assessment of therapeutic response in NB. Furthermore, our data point towards an emerging role of CXCL13 in the diagnosis and prognosis of viral encephalitis and meningeosis neoplastica. These results are of particular interest in the light of recently developed approaches to CXCL13-directed therapeutic interventions.

Sex differences and menstrual cycle effects in cognitive and sensory resting state networks

It has not yet been established if resting state (RS) connectivity reflects stable characteristics of the brain, or if it is modulated by the psychological and/or physiological state of the participant. Based on research demonstrating sex hormonal effects in task-related brain activity, the present study aimed to investigate corresponding differences in RS networks. RS functional Magnetic Resonance Imaging (RS fMRI) was conducted in women during three different menstrual cycle phases, while men underwent three repeated RS fMRI testing sessions. Independent component analysis was used to identify the default mode network (DMN) and an auditory RS network. For the DMN, RS connectivity was stable across testing sessions in men, but varied across the menstrual cycle in women. For the auditory network (AN), retest reliable sex difference was found. Although RS activity in the DMN has been interpreted as trait characteristic of functional brain organization, these findings suggest that RS activity in networks involving frontal areas might be less stable than in sensory-based networks and can dynamically fluctuate. This also implies that some of the previously reported effects of sex hormones on task-related activity might to some extent be mediated by cycle-related fluctuations in RS activity, especially when frontal areas are involved.

Study on the utility of a statewide counselling programme for improving mortality outcomes of patients with Staphylococcus aureus bacteraemia in Thuringia (SUPPORT): a study protocol of a cluster-randomised crossover trial

INTRODUCTION

Staphylococcus aureus bacteraemia (SAB) is a frequent infection with high mortality rates. It requires specific diagnostic and therapeutic management such as prolonged intravenous administration of antibiotics and aggressive search for and control of infectious sources. Underestimation of disease severity frequently results in delayed or inappropriate management of patients with SAB leading to increased mortality rates. According to observational studies, patient counselling by infectious disease consultants (IDC) improves survival and reduces the length of hospital stay as well as complication rates. In many countries, IDC are available only in some tertiary hospitals. In this trial, we aim to demonstrate that the outcome of patients with SAB in small and medium size hospitals that do not employ IDC can be improved by unsolicited ID phone counselling. The SUPPORT trial will be the first cluster-randomised controlled multicentre trial addressing this question.

METHODS AND ANALYSIS

SUPPORT is a single-blinded, multicentre interventional, cluster-randomised, controlled crossover trial with a minimum of 15 centres that will include 250 patients with SAB who will receive unsolicited IDC counselling and 250 who will receive standard of care. Reporting of SAB will be conducted by an electronic real-time blood culture registry established for the German Federal state of Thuringia (ALERTSNet) or directly by participating centres in order to minimise time delay before counselling. Mortality, disease course and complications will be monitored for 90 days with 30-day all-cause mortality rates as the primary outcome. Generalised linear mixed modelling will be used to detect the difference between the intervention sequences. We expect improved outcome of patients with SAB after IDC.

ETHICS AND DISSEMINATION

We obtained ethics approval from the Ethics committee of the Jena University Hospital and from the Ethics committee of the State Chamber of Physicians of Thuringia. Results will be published in a peer-reviewed journal and additionally disseminated through public media.

TRIAL REGISTRATION NUMBER

DRKS00010135.

[Antibiotic stewardship and Staphylococcus aureus Bacteremia]

Rates of antibiotic resistance are increasing worldwide and impact on the treatment of patients with bacterial infections. A broad and uncritical application in inpatient and outpatient settings as well as in agriculture has been recognized as the main driving force. Antibiotic stewardship (ABS) programs aim at countering this worrisome development using various direct interventions such as infectious disease counseling. Blood stream infections caused by Staphylococcus (S.) aureus are severe infections associated with high mortality rates. ABS interventions such as de-eskalation of the antibiotic regimen or application of narrow-spectrum beta-lactam antibiotics can significantly reduce mortality rates. In this review, we discuss the importance of ABS programs and infectious disease counseling for the treatment of S. aureus blood stream infection.

Estradiol-related variations in top-down and bottom-up processes of cerebral lateralization

OBJECTIVE

Natural fluctuations of sex hormones have been shown to modulate cerebral lateralization in dichotic listening tasks. Two recent studies presented contradictory notions regarding the mechanism of this effect. Specifically, whereas Hjelmervik et al. (2012) suggested that estradiol affects lateralization by enhancing top-down processes, such as cognitive control, Hodgetts, Weis, and Hausmann, (2015) suggested that the effect was attributable to estradiol-related variations in bottom-up aspects of lateralization.

METHOD

The present study used 2 well-established left- and right-lateralized dichotic listening tasks (Grimshaw, Kwasny, Covell, & John, 2003; Grimshaw, Séguin, & Godfrey, 2009; Hugdahl, 1995, 2003), with forced-attention conditions to differentiate between these 2 ideas. Fifty-two naturally cycling women underwent both tasks, during either the menstrual, follicular, or luteal cycle phase. Saliva estradiol and progesterone levels were determined by luminescence immunoassays.

RESULTS

The results showed that sex hormones did not affect language lateralization, which may be attributable to the larger degree of lateralization yielded by the task, compared with that shown by Hodgetts et al. (2015). In the emotional prosody task, high levels of estradiol were marginally associated with a reduction in cognitive control, whereas the language task yielded no cycle effects for either top-down or bottom-up processes.

CONCLUSIONS

In sum, the current study revealed weak support for the idea that estradiol affects top-down control of lateralization, as measured with dichotic listening tasks. Given that the task employed in the present study seemed less cognitively demanding than that used previously, it is suggested that estradiol-related inter- and intraindividual variations in lateralization are small when task demands are low. (PsycINFO Database Record

High estradiol levels improve false memory rates and meta-memory in highly schizotypal women

Overconfidence in false memories is often found in patients with schizophrenia and healthy participants with high levels of schizotypy, indicating an impairment of meta-cognition within the memory domain. In general, cognitive control is suggested to be modulated by natural fluctuations in oestrogen. However, whether oestrogen exerts beneficial effects on meta-memory has not yet been investigated. The present study sought to provide evidence that high levels of schizotypy are associated with increased false memory rates and overconfidence in false memories, and that these processes may be modulated by natural differences in estradiol levels. Using the Deese-Roediger-McDermott paradigm, it was found that highly schizotypal participants with high estradiol produced significantly fewer false memories than those with low estradiol. No such difference was found within the low schizotypy participants. Highly schizotypal participants with high estradiol were also less confident in their false memories than those with low estradiol; low schizotypy participants with high estradiol were more confident. However, these differences only approached significance. These findings suggest that the beneficial effect of estradiol on memory and meta-memory observed in healthy participants is specific to highly schizotypal individuals and might be related to individual differences in baseline dopaminergic activity.

Sex hormones affect language lateralisation but not cognitive control in normally cycling women

This article is part of a Special Issue "Estradiol and Cognition". Natural fluctuations of sex hormones during the menstrual cycle have been shown to modulate language lateralisation. Using the dichotic listening (DL) paradigm, a well-established measurement of language lateralisation, several studies revealed that the left hemispheric language dominance was stronger when levels of estradiol were high. A recent study (Hjelmervik et al., 2012) showed, however, that high levels of follicular estradiol increased lateralisation only in a condition that required participants to cognitively control (top-down) the stimulus-driven (bottom-up) response. This finding suggested that sex hormones modulate lateralisation only if cognitive control demands are high. The present study investigated language lateralisation in 73 normally cycling women under three attention conditions that differed in cognitive control demands. Saliva estradiol and progesterone levels were determined by luminescence immunoassays. Women were allocated to a high or low estradiol group. The results showed a reduced language lateralisation when estradiol and progesterone levels were high. The effect was independent of the attention condition indicating that estradiol marginally affected cognitive control. The findings might suggest that high levels of estradiol especially reduce the stimulus-driven (bottom-up) aspect of lateralisation rather than top-down cognitive control.

The brain's conversation with itself: neural substrates of dialogic inner speech

Inner speech has been implicated in important aspects of normal and atypical cognition, including the development of auditory hallucinations. Studies to date have focused on covert speech elicited by simple word or sentence repetition, while ignoring richer and arguably more psychologically significant varieties of inner speech. This study compared neural activation for inner speech involving conversations (‘dialogic inner speech’) with single-speaker scenarios (‘monologic inner speech’). Inner speech-related activation differences were then compared with activations relating to Theory-of-Mind (ToM) reasoning and visual perspective-taking in a conjunction design. Generation of dialogic (compared with monologic) scenarios was associated with a widespread bilateral network including left and right superior temporal gyri, precuneus, posterior cingulate and left inferior and medial frontal gyri. Activation associated with dialogic scenarios and ToM reasoning overlapped in areas of right posterior temporal cortex previously linked to mental state representation. Implications for understanding verbal cognition in typical and atypical populations are discussed.

Zur Validität von studentischen Lehrveranstaltungsevaluationen

Die Studie untersucht verschiedene Aspekte der Validität von studentischen Lehrveranstaltungsevaluationen (LVE). Anhand einer adaptierten Version des Trierer Inventars zur Lehrevaluation (TRIL) überprüften wir sowohl faktorielle Validität, Messinvarianz über Veranstaltungsarten hinweg als auch Beurteilerübereinstimmung unter Studierenden (Konsistenz vs. Methodenspezifität). Hierzu wurden konfirmatorische Faktorenanalysen unter Berücksichtigung der Multilevel-Struktur der Daten modelliert. Außerdem wurde die Heterogenität der Urteile innerhalb von Veranstaltungen durch Studierendenvariablen (Interesse am Thema, Sympathie für die Lehrperson, wahrgenommene Schwierigkeit der Inhalte) erklärt. In einer Stichprobe von 1 823 Studierendenurteilen, geschachtelt in 101 Veranstaltungen, konnte die angenommene Struktur der Items bestätigt werden, die Items waren strikt messinvariant über Vorlesungen (n = 51) und Seminare/Übungen (n = 50) hinweg. Die Konsistenz der Studierendenurteile fiel moderat aus. Etwa 50 % der Variabilität innerhalb von Veranstaltungen ließen sich durch die Studierendenvariablen erklären. Hinweise auf die diskriminante Validität der Lehrqualitätsdimensionen ergaben sich durch differentielle Vorhersagebeiträge.

The Cue Is the Key

In contrast to former tests, the Magdeburg Test of Social Intelligence measures social understanding with a scenario approach. Each scenario is based on one real target person and includes both social cues and contextual information about this person in different realistic situations. The subjects’ task is to understand the given social cues and to judge the target persons’ cognitions, emotions, and relationships to other people. However, subjects can potentially use only contextual information instead of social cues or base their judgments equally on both. The present study was aimed at examining the relative contribution of social cues and contextual information. In an experiment (N = 126), we manipulated the following conditions: Participants were given (a) only social cues, (b) only contextual information, or (c) both components. Results showed that social cues played a more important role in this social understanding task than the contextual information.

[Drug therapy of infectious diarrhea. Part 2: Chronic diarrhea]

Diarrheal diseases are among the most common diseases worldwide. In this review the current treatment recommendations for acute (Part 1) and chronic (Part 2) infectious diarrhea are summarized and typical enteropathogens are discussed. The second part of the article describes chronic diarrhea, its related pathogens and treatment. In contrast to acute diarrhea which is mainly caused by viral and typical bacterial pathogens, chronic diarrhea has mainly non-infectious origins. Protozoal pathogens, such as Giardia lamblia and Entamoeba histolytica in particular are found and more rarely bacterial pathogens, such as Tropheryma whipplei. Opportunistic pathogens cause diarrhea in immunocompromised patients, such as in HIV patients. In these patients cytomegalovirus (CMV) colitis or infections with Cryptosporidium spp., Cyclospora cayetanensis, Isospora belli or microsporidia have to be considered. Besides targeted specific antimicrobial therapy, anti-retroviral drugs improving the underlying immunosuppression and thus the reconstitution of the adaptive immune response remain a cornerstone of the treatment in HIV-positive patients.

Functional interaction between right parietal and bilateral frontal cortices during visual search tasks revealed using functional magnetic imaging and transcranial direct current stimulation

The existence of a network of brain regions which are activated when one undertakes a difficult visual search task is well established. Two primary nodes on this network are right posterior parietal cortex (rPPC) and right frontal eye fields. Both have been shown to be involved in the orientation of attention, but the contingency that the activity of one of these areas has on the other is less clear. We sought to investigate this question by using transcranial direct current stimulation (tDCS) to selectively decrease activity in rPPC and then asking participants to perform a visual search task whilst undergoing functional magnetic resonance imaging. Comparison with a condition in which sham tDCS was applied revealed that cathodal tDCS over rPPC causes a selective bilateral decrease in frontal activity when performing a visual search task. This result demonstrates for the first time that premotor regions within the frontal lobe and rPPC are not only necessary to carry out a visual search task, but that they work together to bring about normal function.

Relative survival of patients with non-malignant central nervous system tumours: a descriptive study by the Austrian Brain Tumour Registry

BACKGROUND

Unlike malignant primary central nervous system (CNS) tumours outcome data on non-malignant CNS tumours are scarce. For patients diagnosed from 1996 to 2002 5-year relative survival of only 85.0% has been reported. We investigated this rate in a contemporary patient cohort to update information on survival.

METHODS

We followed a cohort of 3983 cases within the Austrian Brain Tumour Registry. All patients were newly diagnosed from 2005 to 2010 with a histologically confirmed non-malignant CNS tumour. Vital status, cause of death, and population life tables were obtained by 31 December 2011 to calculate relative survival.

RESULTS

Overall 5-year relative survival was 96.1% (95% CI 95.1-97.1%), being significantly lower in tumours of borderline (90.2%, 87.2-92.7%) than benign behaviour (97.4%, 96.3-98.3%). Benign tumour survival ranged from 86.8 for neurofibroma to 99.7% for Schwannoma; for borderline tumours survival rates varied from 83.2 for haemangiopericytoma to 98.4% for myxopapillary ependymoma. Cause of death was directly attributed to the CNS tumour in 39.6%, followed by other cancer (20.4%) and cardiovascular disease (15.8%).

CONCLUSION

The overall excess mortality in patients with non-malignant CNS tumours is 5.5%, indicating a significant improvement in survival over the last decade. Still, the remaining adverse impact on survival underpins the importance of systematic registration of these tumours.