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Abstract Background With the advances in high-throughput sequencing and bioinformatic pipelines, mitochondrial genomes have become increasingly popular for phylogenetic analyses across different clades of invertebrates. Despite the vast rise in available mitogenomic datasets of molluscs, one class of aplacophoran molluscs – Solenogastres (or Neomeniomorpha) – is still neglected. Results Here, we present six new mitochondrial genomes from five families of Solenogastres (Amphimeniidae, Gymnomeniidae, Proneomeniidae, Pruvotinidae, Simrothiellidae), including the first complete mitogenomes, thereby now representing three of the four traditional orders. Solenogaster mitogenomes are variable in size (ranging from approximately 15,000 bp to over 17,000 bp). The gene order of the 13 protein coding genes and two rRNA genes is conserved in three blocks, but considerable variation occurs in the order of the 22 tRNA genes. Based on phylogenetic analyses and reconstruction of ancestral mitochondrial genomes of Aculifera, the position of (1) trnD gene between atp8 and atp6, (2) trnT and P genes between atp6 and nad5, and (3) trnL1 gene between G and E, resulting in a ‘MCYWQGL1E’-block of tRNA genes, are all three considered synapomorphies for Solenogastres. The tRNA gene block ‘KARNI’ present in Polyplacophora and several conchiferan taxa is dissolved in Solenogastres. Conclusion Our study shows that mitogenomes are suitable to resolve the phylogenetic relationships among Aculifera and within Solenogastres, thus presenting a cost and time efficient compromise to approach evolutionary history in these clades.

Abstract Human brain morphology undergoes complex changes over the lifespan. Despite recent progress in tracking brain development via normative models, current knowledge of underlying biological mechanisms is highly limited. We demonstrate that human cortical thickness development and aging trajectories unfold along patterns of molecular and cellular brain organization, traceable from population-level to individual developmental trajectories. During childhood and adolescence, cortex-wide spatial distributions of dopaminergic receptors, inhibitory neurons, glial cell populations, and brain-metabolic features explain up to 50% of the variance associated with a lifespan model of regional cortical thickness trajectories. In contrast, modeled cortical thickness change patterns during adulthood are best explained by cholinergic and glutamatergic neurotransmitter receptor and transporter distributions. These relationships are supported by developmental gene expression trajectories and translate to individual longitudinal data from over 8000 adolescents, explaining up to 59% of developmental change at cohort- and 18% at single-subject level. Integrating neurobiological brain atlases with normative modeling and population neuroimaging provides a biologically meaningful path to understand brain development and aging in living humans.

Abstract Adolescents exhibit remarkable heterogeneity in the structural architecture of brain development. However, due to limited large-scale longitudinal neuroimaging studies, existing research has largely focused on population averages, and the neurobiological basis underlying individual heterogeneity remains poorly understood. Here we identify, using the IMAGEN adolescent cohort followed up over 9 years (14–23 y), three groups of adolescents characterized by distinct developmental patterns of whole-brain gray matter volume (GMV). Group 1 show continuously decreasing GMV associated with higher neurocognitive performances than the other two groups during adolescence. Group 2 exhibit a slower rate of GMV decrease and lower neurocognitive performances compared with Group 1, which was associated with epigenetic differences and greater environmental burden. Group 3 show increasing GMV and lower baseline neurocognitive performances due to a genetic variation. Using the UK Biobank, we show these differences may be attenuated in mid-to-late adulthood. Our study reveals clusters of adolescent neurodevelopment based on GMV and the potential long-term impact.

Introduction:A growing literature has shown that exposure to adverse life events during childhood or adolescence is associated with the presence of psychotic-like experiences (PLEs), which is in turn associated with the risk of psychotic outcomes. Ruminative thinking, i.e., the tendency to dwell on particular issues or ideas, may affect the perceived aversiveness and ability to cope with adverse life events. However, the role that rumination plays in the relationship between adverse life events and the presence of PLEs remains unclear. The purpose of this study is to assess the association between adverse life events and PLEs in a longitudinal sample of young adults and adolescents, and to investigate whether this relationship is mediated by ruminative thinking.MethodsWe used a longitudinal naturalistic sample of 706 volunteers assessed at ages 18 and 22 years, within the Imagen consortium. Lifetime occurrence of adverse life events (i.e., events perceived as strongly negative by participants) was investigated using the Life Events Questionnaire. The Community Assessment of Psychic Experience (CAPE-42) served to assess the presence of PLEs, while ruminative thinking was investigated through the Ruminative Response Scale.ResultsResults showed that both frequency of PLEs and their persistence over time were associated with greater adverse life events exposure (r = 0.32, p < 0.001 and F1 = 9.8; p < 0.001, respectively) and greater ruminative response (r = 0.66, p < 0.001 and F1 = 94.9; p < 0.001, respectively). Mediation analyses showed that relationship between adverse life events and PLEs frequency was partially mediated by rumination (direct effect Z: 5.4, p < 0.001; indirect effect Z: 6.9, p < 0.001; total effect Z: 5.9, p < 0.001). Considering changes between the two assessment timepoints, relationship between PLEs variation between 18 and 22 years and adverse life events occurred during the same period was partially mediated by changes in rumination (direct effect Z: 2.8, p < 0.005; indirect effect Z: 4.3, p < 0.001; total effect Z: 4.3; p < 0.001).DiscussionOverall, our findings confirm that the presence of adverse life events may increase the risk of experiencing PLEs in healthy individuals and suggest that dysfunctional coping strategies, such as ruminative thinking, may be related to psychosis proneness. Results do not disentangle whether individuals with greater risk for psychosis tend to ruminate more or whether rumination exacerbates psychosis risk.

Structural brain aging has demonstrated strong inter-individual heterogeneity and mirroring patterns with brain development. However, due to the lack of large-scale longitudinal neuroimaging studies, most of the existing research focused on the cross-sectional changes of brain aging. In this investigation, we present a data-driven approach that incorporate both cross-sectional changes and longitudinal trajectories of structural brain aging and identified two brain aging patterns among 37,013 healthy participants from UK Biobank. Participants with accelerated brain aging also demonstrated accelerated biological aging, cognitive decline and increased genetic susceptibilities to major neuropsychiatric disorders. Further, by integrating longitudinal neuroimaging studies from a multi-center adolescent cohort, we validated the ‘last in, first out’ mirroring hypothesis and identified brain regions with manifested mirroring patterns between brain aging and brain development. Genomic analyses revealed risk loci and genes contributing to accelerated brain aging and delayed brain development, providing molecular basis for elucidating the biological mechanisms underlying brain aging and related disorders.

Adolescence is a crucial period for physical and psychological development. The impact of negative life events represents a risk factor for the onset of neuropsychiatric disorders. This study aims to investigate the relationship between negative life events and structural brain connectivity, considering both graph theory and connectivity strength. A group (n = 487) of adolescents from the IMAGEN Consortium was divided into Low and High Stress groups. Brain networks were extracted at an individual level, based on morphological similarity between grey matter regions with regions defined using an atlas-based region of interest (ROI) approach. Between-group comparisons were performed with global and local graph theory measures in a range of sparsity levels. The analysis was also performed in a larger sample of adolescents (n = 976) to examine linear correlations between stress level and network measures. Connectivity strength differences were investigated with network-based statistics. Negative life events were not found to be a factor influencing global network measures at any sparsity level. At local network level, between-group differences were found in centrality measures of the left somato-motor network (a decrease of betweenness centrality was seen at sparsity 5%), of the bilateral central visual and the left dorsal attention network (increase of degree at sparsity 10% at sparsity 30% respectively). Network-based statistics analysis showed an increase in connectivity strength in the High stress group in edges connecting the dorsal attention, limbic and salience networks. This study suggests negative life events alone do not alter structural connectivity globally, but they are associated to connectivity properties in areas involved in emotion and attention.

Substance use, including cigarettes and cannabis, is associated with poorer sustained attention in late adolescence and early adulthood. Previous studies were predominantly cross-sectional or under-powered and could not indicate if impairment in sustained attention was a predictor of substance use or a marker of the inclination to engage in such behavior. This study explored the relationship between sustained attention and substance use across a longitudinal span from ages 14 to 23 in over 1000 participants. Behaviors and brain connectivity associated with diminished sustained attention at age 14 predicted subsequent increases in cannabis and cigarette smoking, establishing sustained attention as a robust biomarker for vulnerability to substance use. Individual differences in network strength relevant to sustained attention were preserved across developmental stages and sustained attention networks generalized to participants in an external dataset. In summary, brain networks of sustained attention are robust, consistent, and able to predict aspects of later substance use.

This study explored the generality versus specificity of two trait-liability factors for externalizing problems-disinhibition and callousness-in the concurrent and prospective prediction of symptoms of conduct disorder, attention-deficit/hyperactivity disorder (ADHD), and substance use (i.e., alcohol use disorder and history of illicit substance use). Disinhibition involves an impulsive, unrestrained cognitive-behavioral style; callousness entails a dispositional lack of social-emotional sensitivity. Participants were European adolescents from the multisite IMAGEN project who completed questionnaires and clinical interviews at ages 14 (N = 1,504, Mage = 14.41, 51.13% female) and 16 (N = 1,407, Mage = 16.46, 51.88% female). Disinhibition was related concurrently and prospectively to greater symptoms of conduct disorder, ADHD, and alcohol use disorder; higher scores on a general externalizing factor; and greater likelihood of having tried an illicit substance. Callousness was selectively related to greater conduct disorder symptoms. These findings indicate disinhibition confers broad liability for externalizing spectrum disorders, perhaps due to its affiliated deficits in executive function. In contrast, callousness appears to represent more specific liability for antagonistic (aggressive/exploitative) forms of externalizing, as exemplified by antisocial behavior. Results support the utility of developmental-ontogenetic and hierarchical-dimensional models of psychopathology and have important implications for early assessment of risk for externalizing problems. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The causes of aggressive behavior in children with autism are poorly understood, which limits treatment options. Therefore, this study used behavioral testing and parent reports of 60 children with autism to investigate the interplay of emotion misinterpretation and hostile attribution bias in the prediction of different aggressive behaviors. Further, the additional impact of dysfunctional emotion regulation was examined. Path analyses indicated that hostile attribution bias increased verbal and covert aggression but not physical aggression and bullying. Dysfunctional emotion regulation had an additional impact on bullying, verbal aggression, and covert aggression. Emotion recognition was positively associated with hostile attribution bias. These findings provide a first insight into a complex interplay of socio-emotional variables; longitudinal studies are needed to examine causal relationships.

Satisfaction with the detection process of autism and its determinants was investigated using data from the Autism Spectrum Disorder in the European Union (2015-2018) network. A total of 1342 family members, including 1278 parents, completed an online survey collecting information about their experience and satisfaction with the early detection of autism in their child. Overall, the level of satisfaction varied considerably from one respondent to another. Difficulty in finding information about detection services, lack of professional guidance and support in response to first concerns, finding a diagnostic service on one's own, and a delay of more than 4 months between the confirmation of concerns and the first appointment with a specialist were all experiences individually associated with greater odds of being less satisfied. Using a dominance analysis approach, we further identified professional guidance and support in response to first concerns as the most important predictor of the level of satisfaction. These findings highlight the aspects of the process that need to be improved to enhance the experience of the detection process and are therefore relevant to guide health administrations toward actions to be implemented to this effect.

Autism spectrum disorder shares many symptoms with other mental health disorders, and comorbid disorders such as mood and anxiety disorders are common, making the diagnostic process challenging. We aimed to explore the diagnostic accuracy of two standard autism spectrum disorder diagnostic instruments and to identify those behavioral items that best differentiate between autism spectrum disorder and mood and anxiety disorder in a naturalistic sample of patients utilizing autism spectrum disorder specialist services. The study included data of 847 participants (5-65 years of age, n = 586 with autism spectrum disorder, n = 261 with mood and anxiety disorder) all evaluated with the Autism Diagnostic Observation Schedule in the context of the diagnostic process. Data of the Autism Diagnostic Interview--Revised were available for 428 participants (5-51 years of age, n = 367 with autism spectrum disorder, n = 61 with mood and anxiety disorder). By means of binominal logistic regressions and an ensemble feature selection, we identified a subset of items that best differentiated between autism spectrum disorder and mood and anxiety disorder. Overall, the results indicate that a combination of communicational deficits and unusual and/or inappropriate social overtures differentiates autism spectrum disorder and mood and anxiety disorder. Aspects of social cognition are also relevant. Limitations of the current study and implications for research and practice are discussed.

BackgroundDysfunctional reward processing is implicated in multiple mental disorders. Novelty seeking (NS) assesses preference for seeking novel experiences, which is linked to sensitivity to reward environmental cues.MethodsA subset of 14-year-old adolescents (IMAGEN) with the top 20% ranked high-NS scores was used to identify high-NS-associated multimodal components by supervised fusion. These features were then used to longitudinally predict five different risk scales for the same and unseen subjects (an independent dataset of subjects at 19 years of age that was not used in predictive modeling training at 14 years of age) (within IMAGEN, n ≈1100) and even for the corresponding symptom scores of five types of patient cohorts (non-IMAGEN), including drinking (n = 313), smoking (n = 104), attention-deficit/hyperactivity disorder (n = 320), major depressive disorder (n = 81), and schizophrenia (n = 147), as well as to classify different patient groups with diagnostic labels.ResultsMultimodal biomarkers, including the prefrontal cortex, striatum, amygdala, and hippocampus, associated with high NS in 14-year-old adolescents were identified. The prediction models built on these features are able to longitudinally predict five different risk scales, including alcohol drinking, smoking, hyperactivity, depression, and psychosis for the same and unseen 19-year-old adolescents and even predict the corresponding symptom scores of five types of patient cohorts. Furthermore, the identified reward-related multimodal features can classify among attention-deficit/hyperactivity disorder, major depressive disorder, and schizophrenia with an accuracy of 87.2%.ConclusionsAdolescents with higher NS scores can be used to reveal brain alterations in the reward-related system, implicating potential higher risk for subsequent development of multiple disorders. The identified high-NS-associated multimodal reward-related signatures may serve as a transdiagnostic neuroimaging biomarker to predict disease risks or severity.

Abstract Smoking of cigarettes among young adolescents is a pressing public health issue. However, the neural mechanisms underlying smoking initiation and sustenance during adolescence, especially the potential causal interactions between altered brain development and smoking behaviour, remain elusive. Here, using large longitudinal adolescence imaging genetic cohorts, we identify associations between left ventromedial prefrontal cortex (vmPFC) gray matter volume (GMV) and subsequent self-reported smoking initiation, and between right vmPFC GMV and the maintenance of smoking behaviour. Rule-breaking behaviour mediates the association between smaller left vmPFC GMV and smoking behaviour based on longitudinal cross-lagged analysis and Mendelian randomisation. In contrast, smoking behaviour associated longitudinal covariation of right vmPFC GMV and sensation seeking (especially hedonic experience) highlights a potential reward-based mechanism for sustaining addictive behaviour. Taken together, our findings reveal vmPFC GMV as a possible biomarker for the early stages of nicotine addiction, with implications for its prevention and treatment.

Abstract Background Aiming at objective early detection of neuromotor disorders such as cerebral palsy, we propose an innovative non-intrusive approach using a pressure sensing device to classify infant general movements. Here we differentiate typical general movement patterns of the “fidgety period” (fidgety movements) vs. the “pre-fidgety period” (writhing movements). Methods Participants (N = 45) were sampled from a typically-developing infant cohort. Multi-modal sensor data, including pressure data from a pressure sensing mat with 1024 sensors, were prospectively recorded for each infant in seven succeeding laboratory sessions in biweekly intervals from 4 to 16 weeks of post-term age. 1776 pressure data snippets, each 5 s long, from the two targeted age periods were taken for movement classification. Each snippet was pre-annotated based on corresponding synchronised video data by human assessors as either fidgety present or absent. Multiple neural network architectures were tested to distinguish the fidgety present vs. fidgety absent classes, including support vector machines, feed-forward networks, convolutional neural networks, and long short-term memory networks. Results Here we show that the convolution neural network achieved the highest average classification accuracy (81.4%). By comparing the pros and cons of other methods aiming at automated general movement assessment to the pressure sensing approach, we infer that the proposed approach has a high potential for clinical applications. Conclusions We conclude that the pressure sensing approach has great potential for efficient large-scale motion data acquisition and sharing. This will in return enable improvement of the approach that may prove scalable for daily clinical application for evaluating infant neuromotor functions.

Abstract Background Early negative life events (NLE) have long‐lasting influences on neurodevelopment and psychopathology. Reduced orbitofrontal cortex (OFC) thickness was frequently associated with NLE and depressive symptoms. OFC thinning might mediate the effect of NLE on depressive symptoms, although few longitudinal studies exist. Using a complete longitudinal design with four time points, we examined whether NLE during childhood and early adolescence predict depressive symptoms in young adulthood through accelerated OFC thinning across adolescence. Methods We acquired structural MRI from 321 participants at two sites across four time points from ages 14 to 22. We measured NLE with the Life Events Questionnaire at the first time point and depressive symptoms with the Center for Epidemiologic Studies Depression Scale at the fourth time point. Modeling latent growth curves, we tested whether OFC thinning mediates the effect of NLE on depressive symptoms. Results A higher burden of NLE, a thicker OFC at the age of 14, and an accelerated OFC thinning across adolescence predicted young adults' depressive symptoms. We did not identify an effect of NLE on OFC thickness nor OFC thickness mediating effects of NLE on depressive symptoms. Conclusions Using a complete longitudinal design with four waves, we show that NLE in childhood and early adolescence predict depressive symptoms in the long term. Results indicate that an accelerated OFC thinning may precede depressive symptoms. Assessment of early additionally to acute NLEs and neurodevelopment may be warranted in clinical settings to identify risk factors for depression.

Summary: During late adolescence, the brain undergoes ontogenic organization altering subcortical-cortical circuitry. This includes regions implicated in pain chronicity, and thus alterations in the adolescent ontogenic organization could predispose to pain chronicity in adulthood - however, evidence is lacking. Using resting-state functional magnetic resonance imaging from a large European longitudinal adolescent cohort and an adult cohort with and without chronic pain, we examined links between painful symptoms and brain connectivity. During late adolescence, thalamo-, caudate-, and red nucleus-cortical connectivity were positively and subthalamo-cortical connectivity negatively associated with painful symptoms. Thalamo-cortical connectivity, but also subthalamo-cortical connectivity, was increased in adults with chronic pain compared to healthy controls. Our results indicate a shared basis in basothalamo-cortical circuitries between adolescent painful symptomatology and adult pain chronicity, with the subthalamic pathway being differentially involved, potentially due to a hyperconnected thalamo-cortical pathway in chronic pain and ontogeny-driven organization. This can inform neuromodulation-based prevention and early intervention.