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The past decade has evinced a boom of computer-based approaches to aid movement assessment in early infancy. Increasing interests have been dedicated to develop AI driven approaches to complement the classic Prechtl general movements assessment (GMA). This study proposes a novel machine learning algorithm to detect an age-specific movement pattern, the fidgety movements (FMs), in a prospectively collected sample of typically developing infants. Participants were recorded using a passive, single camera RGB video stream. The dataset of 2800 five-second snippets was annotated by two well-trained and experienced GMA assessors, with excellent inter- and intra-rater reliabilities. Using OpenPose, the infant full pose was recovered from the video stream in the form of a 25-points skeleton. This skeleton was used as input vector for a shallow multilayer neural network (SMNN). An ablation study was performed to justify the network’s architecture and hyperparameters. We show for the first time that the SMNN is sufficient to discriminate fidgety from non-fidgety movements in a sample of age-specific typical movements with a classification accuracy of 88%. The computer-based solutions will complement original GMA to consistently perform accurate and efficient screening and diagnosis that may become universally accessible in daily clinical practice in the future.

Chronic peer victimization has long-term impacts on mental health; however, the biological mediators of this adverse relationship are unknown. We sought to determine whether adolescent brain development is involved in mediating the effect of peer victimization on psychopathology. We included participants (n = 682) from the longitudinal IMAGEN study with both peer victimization and neuroimaging data. Latent profile analysis identified groups of adolescents with different experiential patterns of victimization. We then associated the victimization trajectories and brain volume changes with depression, generalized anxiety, and hyperactivity symptoms at age 19. Repeated measures ANOVA revealed time-by-victimization interactions on left putamen volume (F = 4.38, p = 0.037). Changes in left putamen volume were negatively associated with generalized anxiety (t = −2.32, p = 0.020). Notably, peer victimization was indirectly associated with generalized anxiety via decreases in putamen volume (95% CI = 0.004–0.109). This was also true for the left caudate (95% CI = 0.002–0.099). These data suggest that the experience of chronic peer victimization during adolescence might induce psychopathology-relevant deviations from normative brain development. Early peer victimization interventions could prevent such pathological changes.

Autism Spectrum Disorder (ASD) is often characterized by deficits in emotion regulation and empathic abilities, potentially linked to alterations in prefrontal brain regions. This randomized, controlled clinical trial examines the efficacy of slow cortical potential neurofeedback training, specifically targeting these prefrontal areas, in improving emotion regulation and empathy among children and adolescents with ASD. The study involved 41 participants, with 21 undergoing slow cortical potential training and 20 receiving treatment as usual. All participants were allowed to continue usual care in progress, if it was kept stable. Emotional processing was evaluated using an adapted and extended version of the Multifaceted Empathy Test, alongside electroencephalography assessments focusing on event-related potentials, including N170, LPP, and P300 components. The main findings indicate a significant group × time interaction in P300 latency, with shorter latencies in the SCP neurofeedback group and longer latencies in controls, though post hoc tests were not significant. A trend toward reduced P300 amplitude in the experimental group suggests possible modulation of attentional processing. Additionally, changes in a late component of LPP amplitude were linked to reaction time in processing positive emotions, with increases associated with slower responses and decreases with faster responses. These results suggest slow cortical potential neurofeedback training may influence cognitive efficiency and emotional processing in autistic individuals. While promising, further research is needed to confirm these findings and optimize neurofeedback protocols for this population.

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.

Environmental adversities constitute potent risk factors for psychiatric disorders. Evidence suggests the brain adapts to adversity, possibly in an adversity-type and region-specific manner. However, the long-term effects of adversity on brain structure and the association of individual neurobiological heterogeneity with behavior have yet to be elucidated. Here we estimated normative models of structural brain development based on a lifespan adversity profile in a longitudinal at-risk cohort aged 25 years ( n  = 169). This revealed widespread morphometric changes in the brain, with partially adversity-specific features. This pattern was replicated at the age of 33 years ( n  = 114) and in an independent sample at 22 years ( n  = 115). At the individual level, greater volume contractions relative to the model were predictive of future anxiety. We show a stable neurobiological signature of adversity that persists into adulthood and emphasize the importance of considering individual-level rather than group-level predictions to explain emerging psychopathology. In a birth cohort, Holz et al. found widespread structural brain changes at the age of 25 years as a function of adversity. This pattern was replicated at the age of 33 years and in another cohort. Individual-level volume reductions on top of this pattern predicted anxiety.

Imaging genetics offers the possibility of detecting associations between genotype and brain structure as well as function, with effect sizes potentially exceeding correlations between genotype and behavior. However, study results are often limited due to small sample sizes and methodological differences, thus reducing the reliability of findings. The IMAGEN cohort with 2000 young adolescents assessed from the age of 14 onwards tries to eliminate some of these limitations by offering a longitudinal approach and sufficient sample size for analyzing gene-environment interactions on brain structure and function. Here, we give a systematic review of IMAGEN publications since the start of the consortium. We then focus on the specific phenotype ‘drug use’ to illustrate the potential of the IMAGEN approach. We describe findings with respect to frontocortical, limbic and striatal brain volume, functional activation elicited by reward anticipation, behavioral inhibition, and affective faces, and their respective associations with drug intake. In addition to describing its strengths, we also discuss limitations of the IMAGEN study. Because of the longitudinal design and related attrition, analyses are underpowered for (epi-) genome-wide approaches due to the limited sample size. Estimating the generalizability of results requires replications in independent samples. However, such densely phenotyped longitudinal studies are still rare and alternative internal cross-validation methods (e.g., leave-one out, split-half) are also warranted. In conclusion, the IMAGEN cohort is a unique, very well characterized longitudinal sample, which helped to elucidate neurobiological mechanisms involved in complex behavior and offers the possibility to further disentangle genotype × phenotype interactions.

In this study we compare the performance of available generic- and specialized infant-pose estimators for a video-based automated general movement assessment (GMA), and the choice of viewing angle for optimal recordings, i.e., conventional diagonal view used in GMA vs. top-down view. We used 4500 annotated video-frames from 75 recordings of infant spontaneous motor functions from 4 to 16 weeks. To determine which pose estimation method and camera angle yield the best pose estimation accuracy on infants in a GMA related setting, the error with respect to human annotations and the percentage of correct key-points (PCK) were computed and compared. The results show that the best performing generic model trained on adults, ViTPose, also performs best on infants. We see no improvement from using specific infant-pose estimators over the generic pose estimators on our infant dataset. However, when retraining a generic model on our data, there is a significant improvement in pose estimation accuracy. This indicates limited generalization capabilities of infant-pose estimators to other infant datasets, meaning that one should be careful when choosing infant pose estimators and using them on infant datasets which they were not trained on. The pose estimation accuracy obtained from the top-down view is significantly better than that obtained from the diagonal view (the standard view for GMA). This suggests that a top-down view should be included in recording setups for automated GMA research.

Background Aberrant resting state electroencephalography (rsEEG) is a well-established indicator of psychopathological brain activity in clinical disorders. In Autism Spectrum Disorder (ASD), a substantial body of research reports reduced Alpha activity in the electrocortical resting state of affected individuals. However, effective interventions based on neurophysiological patterns and objective biological markers of treatment outcome remain scarce. Methods In this randomized controlled trial, the primary objective was to examine rsEEG changes in adolescents with ASD following 24 sessions of slow cortical potential neurofeedback training ( n  = 21) compared to a treatment-as-usual control group ( n  = 20). A repeated-measures analysis of variance was used to assess group differences over time. Additionally, Pearson correlation analyses were conducted to exploratorily investigate associations between rsEEG measures and clinical psychopathology and affective well-being, as assessed via parental and self-report questionnaires at baseline and post-intervention. Results Analyses revealed significant differences in the development of rsEEG between the intervention groups: while Alpha activity increased in the experimental neurofeedback group, it decreased in the control group, demonstrating an opposite trend. Exploratory analyses showed that Delta activity decreased in both groups, with a more pronounced decrease in the experimental group. Correlational analyses revealed significant associations between subjective-psychological and electrocortical levels: lower alpha power at baseline was related to greater severity of ASD symptoms, while both lower alpha and higher delta power were associated with greater negative affect at baseline. Increases in alpha power after NF-training were linked with enhanced positive affect, whereas reductions in delta power corresponded to decreases in negative affect. Conclusions This study provides insights into changes in resting-state neural activity before and after clinical interventions alongside clinical-psychological assessment, overcoming single-level assessments and emphasizing the need for multi-level outcome measures for a more comprehensive treatment evaluation. Clinical Trial Registration : DRKS00012339.

International studies show disadvantages for adults with autism spectrum disorder (ASD) in the labor market. Data about their participation in the German labor market are scarce. The aim of this study was to examine the integration of adults with ASD in the German labor market in terms of education, employment and type of occupation by means of a cross-sectional-study, using a postal questionnaire. Findings show above average levels of education for adults with ASD compared to the general population of Germany and simultaneously, below average rates of employment and high rates of financial dependency. That indicates a poor integration of adults with ASD in the German labor market and emphasizes the need for vocational support policies for adults with ASD.

Replicating results (i.e. obtaining consistent results using a new independent dataset) is an essential part of good science. As replicability has consequences for theories derived from empirical studies, it is of utmost importance to better understand the underlying mechanisms influencing it. A popular tool for non-invasive neuroimaging studies is functional magnetic resonance imaging (fMRI). While the effect of underpowered studies is well documented, the empirical assessment of the interplay between sample size and replicability of results for task-based fMRI studies remains limited. In this work, we extend existing work on this assessment in two ways. Firstly, we use a large database of 1400 subjects performing four types of tasks from the IMAGEN project to subsample a series of independent samples of increasing size. Secondly, replicability is evaluated using a multi-dimensional framework consisting of 3 different measures: (un)conditional test-retest reliability, coherence and stability. We demonstrate not only a positive effect of sample size, but also a trade-off between spatial resolution and replicability. When replicability is assessed voxelwise or when observing small areas of activation, a larger sample size than typically used in fMRI is required to replicate results. On the other hand, when focussing on clusters of voxels, we observe a higher replicability. In addition, we observe variability in the size of clusters of activation between experimental paradigms or contrasts of parameter estimates within these.