The neurophysiological mechanisms underlying the integration of perception and action are an important topic in cognitive neuroscience. Yet, connections between neurophysiology and cognitive theoretical frameworks have rarely been established. The theory of event coding (TEC) details how perceptions and actions are associated (bound) in a common representational domain (the “event file”), but the neurophysiological mechanisms underlying these processes are hardly understood. We used complementary neurophysiological methods to examine the neurophysiology of event file processing (i.e., event‐related potentials [ERPs], temporal EEG signal decomposition, EEG source localization, time‐frequency decomposition, EEG network analysis). We show that the P3 ERP component and activity modulations in inferior parietal regions (BA40) reflect event file binding processes. The relevance of this parietal region is corroborated by source localization of temporally decomposed EEG data. We also show that temporal EEG signal decomposition reveals a pattern of results suggesting that event file processes can be dissociated from pure stimulus and response‐related processes in the EEG signal. Importantly, it is also documented that event file binding processes are reflected by modulations in the network architecture of theta frequency band activity. That is, when stimulus–response bindings in event files hamper response selection this was associated with a less efficient theta network organization. A more efficient organization was evident when stimulus–response binding in event files facilitated response selection. Small‐world network measures seem to reflect event file processing. The results show how cognitive‐theoretical assumptions of TEC can directly be mapped to the neurophysiology of response selection.

The Autism Diagnostic Observation Schedule is a semi-structured, standardized assessment tool for individuals with suspected autism spectrum disorders (ASD) and is deemed to be part of the gold standard for diagnostic evaluation. Good diagnostic accuracy and interpersonal objectivity have been demonstrated for the ADOS in research setting. The question arises whether this is also true for daily clinical practice and whether diagnostic accuracy depends on specialized experience in the diagnostic evaluation. The present study explores the diagnostic accuracy of the original and the revised version of the ADOS for Modules 1 through 4. Thus, seven cases of ADOS executions were recorded and coded by a group of experts of specialized outpatient clinics for ASD. In an extensive consensus process, including video analysis of every minute of the ADOS executions, a "gold standard" coding for every case was defined. The videos of the ADOS administration were presented to a large group of clinicians (from daily clinical routine care) and their codings (n = 189) were obtained and analysed. Variance of coding and congruence with the expert coding were determined. High variance was found in the codings. The accuracy of the coding depends on the experience of the coder with the ADOS as well as on characteristics of the cases and the quality of the administration of the ADOS. Specialization in the diagnostic of ASD has to be claimed. Specialized outpatient clinics for ASD are required which guarantee a qualified diagnostic/differential diagnostic and case management with the aim of demand-oriented supply of individual cases.

Task switching processes reflect a faculty of cognitive flexibility. The underlying neural mechanisms and functional cortical networks have frequently been investigated using neurophysiological (EEG) or functional imaging methods. However, task switching processes are subject to strong intra-individual variability, especially when tested under varying levels of working memory demands. This intra-individual variability compromises the reliable estimation of neurophysiological processes and related functional neuroanatomical networks. In this study, we combine residue iteration decomposition (RIDE) of event-related potentials (ERPs) and source localization methods to circumvent this problem. Due to strong intra-individual variability, behavioral effects between memory-based and cue-based task switching were not reflected by classical ERPs, but were so after applying RIDE. Using RIDE, modulations paralleling the behavioral data were specifically reflected by processes related to the updating of internal representations for response selection (reflected by the C-cluster in the P3-component time range) rather than by stimulus and motor-related processes (reflected by the S-cluster and R-cluster). The C-cluster-processes were associated with activation differences in the inferior parietal cortex, including the temporo-parietal junction (TPJ, BA40) and likely reflect mechanisms related to the updating of internal representations and task sets for response selection. The results underline the necessity to use temporal decomposition methods to control the problem of intra-individual signal variability to decipher the neurophysiology and functional neuroanatomy of cognitive processes.

It is well established that memory is more accurate for own-relative to other-race faces (own-race bias), which has been suggested to result from larger perceptual expertise for own-race faces. Previous studies also demonstrated better memory for own-relative to other-gender faces, which is less likely to result from differences in perceptual expertise, and rather may be related to social in-group vs out-group categorization. We examined neural correlates of the own-gender bias using event-related potentials (ERP). In a recognition memory experiment, both female and male participants remembered faces of their respective own gender more accurately compared with other-gender faces. ERPs during learning yielded significant differences between the subsequent memory effects (subsequently remembered - subsequently forgotten) for own-gender compared with other-gender faces in the occipito-temporal P2 and the central N200, whereas neither later subsequent memory effects nor ERP old/new effects at test reflected a neural correlate of the own-gender bias. We conclude that the own-gender bias is mainly related to study phase processes, which is in line with sociocognitive accounts.

BackgroundEarly identification of autism spectrum disorders (ASD) is a prerequisite for access to early interventions. Although parents often note developmental atypicalities during the first 2 years of life, many children with ASD are not diagnosed until school age. For parents, the long period between first parental concerns and diagnosis is often frustrating and accompanied by uncertainty and worry.MethodsThis study retrospectively explored the trajectories of children with a confirmed ASD diagnosis during the diagnostic process, from first parental concerns about their child’s development until the definite diagnosis. A survey concerning the diagnostic process was distributed to parents or legal guardians of children with ASD from three specialized ASD outpatient clinics in Germany.ResultsThe response rate was 36.9%, and the final sample consisted of carers of 207 affected children (83.6% male, mean age 12.9 years). The children had been diagnosed with childhood autism (55.6%), Asperger syndrome (24.2%), or atypical autism (20.3%). On average, parents had first concerns when their child was 23.4 months old, and an ASD diagnosis was established at a mean age of 78.5 months. Children with atypical autism or Asperger syndrome were diagnosed significantly later (83.9 and 98.1 months, respectively) than children with childhood autism (68.1 months). Children with an IQ < 85 were diagnosed much earlier than those with an IQ ≥ 85. On average, parents visited 3.4 different health professionals (SD = 2.4, range 1–20, median: 3.0) until their child received a definite ASD diagnosis. Overall, 38.5% of carers were satisfied with the diagnostic process.ConclusionsIn this sample of children with ASD in Germany, the time to diagnosis was higher than in the majority of other comparable studies. These results flag the need for improved forms of service provision and delivery for suspected cases of ASD in Germany.Electronic supplementary materialThe online version of this article (10.1186/s13034-019-0276-1) contains supplementary material, which is available to authorized users.

Young adult participants are more accurate at remembering young as compared with old faces (own-age bias). This study investigated behavioral and event-related potential (ERP) correlates of recognition memory in 4 consecutive age categories (ranging from 19-29, 30-44, 45-59, and 60-80 years), both with respect to face and participant age. Young and young middle-aged participants yielded more accurate recognition memory for both young and young middle-aged as compared to old middle-aged and old faces, suggesting that the own-age bias in adults is not exclusively directed toward age-congruent "in-group" faces. No own-age bias was observed in old middle-aged and elderly participants. Analysis of ERPs revealed significant positive correlations of both N170 latency and amplitude with participant age, thus, suggesting an age-related delay of processing speed and an increase in processing demands at early perceptual stages of face processing. Furthermore, an ERP old-new effect (400-700 ms), with more positive amplitudes for hits as compared with correct rejections, was detected in young and young middle-aged participants but not in the 2 older groups. Because these older groups did not demonstrate enhanced memory performance for own-age faces, we suggest that detailed recollection of study-episode information, as reflected in the ERP old-new effect, may be a necessary prerequisite for the own-age bias.

Diagnosing autism spectrum disorders (ASD) is a complicated, time-consuming process which is particularly challenging in older individuals. one of the most widely used behavioral diagnostic tools is the Autism Diagnostic observation Schedule (ADoS). previous work using machine learning techniques suggested that ASD detection in children can be achieved with substantially fewer items than the original ADOS. Here, we expand on this work with a specific focus on adolescents and adults as assessed with the ADOS Module 4. We used a machine learning algorithm (support vector machine) to examine whether ASD detection can be improved by identifying a subset of behavioral features from the ADOS Module 4 in a routine clinical sample of N = 673 high-functioning adolescents and adults with ASD (n = 385) and individuals with suspected ASD but other best-estimate or no psychiatric diagnoses (n = 288). We identified reduced subsets of 5 behavioral features for the whole sample as well as age subgroups (adolescents vs. adults) that showed good specificity and sensitivity and reached performance close to that of the existing ADOS algorithm and the full ADOS, with no significant differences in overall performance. These results may help to improve the complicated diagnostic process of ASD by encouraging future efforts to develop novel diagnostic instruments for ASD detection based on the identified constructs as well as aiding clinicians in the difficult question of differential diagnosis. Autism Spectrum Disorders (ASD) comprise a range of pervasive neurodevelopmental disorders with a population prevalence of approximately 1% 1. They are characterized by early-onset persistent impairments in social communication and interaction as well as the presence of restricted, repetitive behaviors or interests 2,3. Diagnosing ASD is a complicated, lengthy and time-consuming process, which requires outstanding and specific clinical expertise 4,5. Although research makes constant progress in understanding the underlying genetic and neurobiological factors associated with ASD, there are currently no reliable biological markers for ASD and the diagnosis remains based on behavioral symptoms 1,6,7. The current so-called "gold standard" of ASD diagnosis comprises the use of various standardized diagnostic instruments that assist clinicians in reaching a best-estimate clinical diagnosis 7-9. Two of the most widely used diagnostic instruments are the Autism Diagnostic Observation Schedule (ADOS respectively ADOS-2 for the revised second edition) 10,11 and the Autism Diagnostic Interview

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