Diminished neural resources allocation to time processing in Autism Spectrum Disorders

Background: Interval timing, the ability to judge the duration of short events, has been shown to be compromised in Autism Spectrum Disorders (ASD). Timing abilities are ubiquitous and underlie behaviours as varied as sensory integration, motor coordination or communication. It has been suggested that atypical temporal processing in ASD could contribute to some of the disorder's symptoms, in particular motor clumsiness and difficulties in social interaction and communication. Recent behavioural investigations have suggested that interval timing in ASD is characterised by intact sensitivity but reduced precision in duration judgements. Methods: In this study we investigated the processing of duration as compared to pitch in a group of high-functioning individuals with ASD using magnetoencephalography (MEG). 18 adolescents and adults with ASD and 18 age- and IQ-matched typically-developing control (TDC) individuals compared two consecutive tones according to their duration or pitch in separate experimental blocks. The analysis was carried out exclusively on physically identical stimuli (500 Hz tones lasting 600 ms), which served, according to instruction, as standard or probe in a Duration or Pitch task respectively. Results: Our results suggest that compared to TDC individuals, individuals with ASD are less able to predict the duration of the standard tone accurately, affecting the sensitivity of the comparison process. In addition, contrary to TDC individuals who allocate resources at different times depending on the nature of the task (pitch or duration discrimination), individuals with ASD seem to engage less resources for the Duration task than for the Pitch task regardless of the context. Although individuals with ASD showed top-down adaptation to the context of the task, this neuronal strategy reflects a bias in the readiness to perform different types of tasks, and in particular a diminished allocation of resources to duration processing which could have cascading effect on learning and development of other cognitiv

Automated extraction of speech and turn-taking parameters in autism allows for diagnostic classification using a multivariable prediction model

Autism spectrum disorder (ASD) is diagnosed on the basis of speech and communication differences, amongst other symptoms. Since conversations are essential for building connections with others, it is important to understand the exact nature of differences between autistic and non-autistic verbal behaviour and evaluate the potential of these differences for diagnostics. In this study, we recorded dyadic conversations and used automated extraction of speech and interactional turn-taking features of 54 non-autistic and 26 autistic participants. The extracted speech and turn-taking parameters showed high potential as a diagnostic marker. A linear support vector machine was able to predict the dyad type with 76.2% balanced accuracy (sensitivity: 73.8%, specificity: 78.6%), suggesting that digitally assisted diagnostics could significantly enhance the current clinical diagnostic process due to their objectivity and scalability. In group comparisons on the individual and dyadic level, we found that autistic interaction partners talked slower and in a more monotonous manner than non-autistic interaction partners and that mixed dyads consisting of an autistic and a non-autistic participant had increased periods of silence, and the intensity, i.e. loudness, of their speech was more synchronous

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Brief Report: Social Anxiety in Autism Spectrum Disorder is Based on Deficits in Social Competence

This study differentially examined the relation between two clinical constructs: social anxiety and social competence in autism spectrum disorder (ASD). Employing two questionnaires (SASKO; IU), individuals with ASD (n = 23) showed increased scores of SOCIAL ANXIETY (SASKO) and of INTOLERANCE OF UNCERTAINTY (IU), compared to a non-clinical comparison group (NC; n = 25). SOCIAL ANXIETY scores were equally increased for ASD and a reference population of individuals with social anxiety disorder (SAD; n = 68). However, results showed increased SOCIAL COMPETENCE DEFICITS in ASD compared to SAD and NC groups. This study allows drawing the conclusion that social anxiety symptoms in ASD can be traced back to autism-specific deficits in social skills and are therefore putatively based on different, substantially deeper implemented cognitive mechanisms

Reduced nonverbal interpersonal synchrony in autism spectrum disorder independent of partner diagnosis: a motion energy study

BackgroundOne of the main diagnostic features of individuals with autism spectrum disorders is nonverbal behaviour difficulties during naturalistic social interactions. The ‘Interactional Heterogeneity Hypothesis’ of ASD proposes that the degree to which individuals share a common ground substantially influences their ability to achieve smooth social interactions.MethodsTo test this hypothesis, we filmed 29 autistic and 29 matched typically developed adults engaged in several conversational tasks. Windowed cross-lagged correlations were computed using the time series of motion energy of both individuals in a dyad. These coefficients were then compared across the three dyad types that were homo- or heterogenous with respect to diagnosis: pairs of two autistic individuals, two typically developed individuals or pairs of one autistic and one typically developed person.ResultsWe found that all dyad types achieved above-chance interpersonal synchrony, but that synchrony was more expressed in typical dyads compared to both autistic and mixed dyads.LimitationsThe method presented here provides only one, albeit objective and robust, approach to explore synchrony. The methodological choices as well as the lack of consideration for other communication modalities may limit our interpretation of the findings. Moreover, the sample size is small with respect to exploring associations between synchrony and various outcome and social skill measures.ConclusionsThe present results do not provide support for the Interactional Heterogeneity Hypothesis given that autistic individuals do not coordinate better when interacting with another autistic individual, compared to when interacting with a typical individual

Distance to the border as an indictor for the success of the AfD at the 2017 Bundestag election in Bavaria

Welche Faktoren erklären den Wahlerfolg der AfD bei der Bundestagswahl 2017? Zur Beantwortung dieser Frage wird auf Grundlage der Theorie der Wahlgeographie das Hauptaugenmerk auf einen bislang nicht beachteten Faktor gelenkt: die Nähe zur Grenze. Am Beispiel Bayerns, wo die AfD im westdeutschen Vergleich relativ stark abschnitt, wird getestet, ob Gemeinden, die näher an der Süd- und Ostgrenze des Freistaats liegen einen höheren AfD-Zweitstimmenanteil aufweisen. Hierfür wird mithilfe geographischer Informationssysteme die Distanz aller 2556 bayerischen Gemeinden zur Grenze ermittelt und dieser Faktor im Anschluss in einer Mehrebenenregression getestet, in der zudem auf bekannte Erklärungsfaktoren für die Wahl rechtspopulistischer Parteien wie die Modernisierungsverlierer- oder die Kontakthypothese kontrolliert wird. Die Ergebnisse zeigen, dass - auch wenn nicht abschließend geklärt werden kann, welche kausalen Faktoren hinter dem starken Abschneiden der AfD bei der Bundestagswahl 2017 im Osten Bayerns standen - der Faktor "Grenznähe" über bekannte Einflüsse hinaus erklärungskräftig ist. Zudem zeigt eine zweite Analyse, dass deutschlandweit die AfD in Wahlkreisen, die an der Grenze zu Polen, Tschechien oder Österreich liegen, stärker abschnitt. Diese Erkenntnisse, gepaart mit der generellen Einsicht, dass geographische Variation und Clustering in der Politikwissenschaft noch zu wenig beachtete Faktoren sind, die aber zumindest das Potential besitzen auf bislang unentdeckte kausale Mechanismen hinzuweisen, dürften sich auch gewinnbringend auf die Analyse anderer (rechtspopulistischer) Wahlergebnisse übertragen lassen.This article investigates the electoral success of the Alternative for Germany (AfD) at the general elections in 2017. Drawing on theoretical arguments from electoral geography it focuses on a factor that had not been explored in existing studies: proximity to the border. Using the example of Bavaria, where the AfD scored relatively well compared to other West German states, the article tests whether communities which are situated closer to the southern and eastern border of Bavaria exhibit a higher share of votes for the AfD. For this purpose we calculate the distances of all 2,556 communities in Bavaria to the border and test this variable in a multi-level-regression additionally controlling for established patterns of explanation for the vote of right-wing populist parties, such as the theory of modernization losers or the contact hypothesis. The results confirm - even if it cannot be finally resolved which factors are causal for the electoral success of the AfD in eastern Bavaria - that proximity to the border indeed has a significant and substantial impact in excess of well-known predictors. An additional analysis for all Germany shows furthermore that the AfD is stronger in electoral districts at the border to Poland, the Czech Republik and Austria. These findings, together with the general insight that geographical variation or clustering are factors that are often given not enough attention in political science although they have the potential to point out so far undetected causal mechanisms, could be fruitfully transferred to the analysis of other (right-wing populist) election results