Age-related brain deviations and aggression

An Attention-Based Hemispheric Relation Inference Network for Perinatal Brain Age Prediction

Brain anatomical age is an effective feature to assess the status of the brain, such as atypical development and aging. Although some deep learning models have been developed for estimating infant brain age, the performance of these models was unsatisfactory because few of them considered the developmental characteristics of brain anatomy during the perinatal period-the most rapid and complex developmental stage across the lifespan. The present study proposed an attention-based hemispheric relation inference network (HRINet) that takes advantage of the nature of brain structural lateralization during early development. This model captures the inter-hemispheric relationship using a graph attention mechanism and transmits lateralization information as features to describe the interactive development between bilateral hemispheres. The HRINet was used to estimate the brain age of 531 preterm and full-term neonates from the Developing Human Connectome Project (dHCP) database based on two metrics (mean curvature and sulcal depth) characterizing the folding morphology of the cortex. Our results showed that the HRINet outperformed other benchmark models in fitting the perinatal brain age, with mean absolute error of 0.53 and determination coefficient of 0.89. We also verified the generalizability of the HRINet on an extra independent dataset collected from the Gansu Provincial Maternity and Child-care Hospital. Furthermore, by applying the best-performing model to an independent dataset consisting of 47 scans of preterm infants at term-equivalent age, we showed that the predicted age was significantly lower than the chronological age, suggesting a delayed development of premature brains. Our results demonstrate the effectiveness and generalizability of the HRINet in estimating infant brain age, providing promising clinical applications for assessing neonatal brain maturity.

Neural correlates of aggression outcome expectation and their association with aggression: A voxel-based morphometry study

BACKGROUND

Aggression outcome expectation is an important cognitive factor of aggression. Discovering the neural mechanism of aggression outcome expectation is conducive to developing aggression research. However, the neural correlates underlying aggression outcome expectation and its effect remain elusive.

METHODS

We utilized voxel-based morphometry (VBM) to unravel the neural architecture of aggression outcome expectation measured by the Social Emotional Information Processing Assessment for Adults and its relationship with aggression measured by the Buss Perry Aggression Questionnaire in a sample of 185 university students (114 female; mean age = 19.94 ± 1.62 years; age range: 17-32 years).

RESULTS

We found a significantly positive correlation between aggression outcome expectation and the regional gray matter volume (GMV) in the right middle temporal gyrus (MTG) (x = 55.5, y = -58.5, z = 1.5; t = 3.35; cluster sizes = 352, p < 0.05, GRF corrected). Moreover, aggression outcome expectation acted as a mediator underlying the association between the right MTG volume and aggression.

CONCLUSIONS

These results revealed the neural correlates of aggression outcome expectation and its effect on aggression for the first time, which may contribute to our understanding of the cognitive neural mechanism of aggression and potentially identifying neurobiological markers for aggression.

Psychopharmacology in children and adolescents: unmet needs and opportunities

Psychopharmacological treatment is an important component of the multimodal intervention approach to treating mental health conditions in children and adolescents. Currently, there are many unmet needs but also opportunities, alongside possible risks to consider, regarding the pharmacological treatment of mental health conditions in children and adolescents. In this Position Paper, we highlight and address these unmet needs and opportunities, including the perspectives of clinicians and researchers from the European College of Neuropsychopharmacology-Child and Adolescent Network, alongside those of experts by lived experience from national and international associations, via a survey involving 644 participants from 13 countries, and of regulators, through representation from the European Medicines Agency. We present and discuss the evidence base for medications currently used for mental disorders in children and adolescents, medications in the pipeline, opportunities in the development of novel medications, crucial priorities for the conduct of future clinical studies, challenges and opportunities in terms of the regulatory and legislative framework, and innovations in the way research is conducted, reported, and promoted.

A stable and replicable neural signature of lifespan adversity in the adult brain

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.

Evidence for embracing normative modeling

In this work, we expand the normative model repository introduced in Rutherford et al., 2022a to include normative models charting lifespan trajectories of structural surface area and brain functional connectivity, measured using two unique resting-state network atlases (Yeo-17 and Smith-10), and an updated online platform for transferring these models to new data sources. We showcase the value of these models with a head-to-head comparison between the features output by normative modeling and raw data features in several benchmarking tasks: mass univariate group difference testing (schizophrenia versus control), classification (schizophrenia versus control), and regression (predicting general cognitive ability). Across all benchmarks, we show the advantage of using normative modeling features, with the strongest statistically significant results demonstrated in the group difference testing and classification tasks. We intend for these accessible resources to facilitate the wider adoption of normative modeling across the neuroimaging community.

Early Social Adversity, Altered Brain Functional Connectivity, and Mental Health

Early adverse environmental exposures during brain development are widespread risk factors for the onset of severe mental disorders and strong and consistent predictors of stress-related mental and physical illness and reduced life expectancy. Current evidence suggests that early negative experiences alter plasticity processes during developmentally sensitive time windows and affect the regular functional interaction of cortical and subcortical neural networks. This, in turn, may promote a maladapted development with negative consequences on the mental and physical health of exposed individuals. In this review, we discuss the role of functional magnetic resonance imaging-based functional connectivity phenotypes as potential biomarker candidates for the consequences of early environmental exposures-including but not limited to-childhood maltreatment. We take an expanded concept of developmentally relevant adverse experiences from infancy over childhood to adolescence as our starting point and focus our review of functional connectivity studies on a selected subset of functional magnetic resonance imaging-based phenotypes, including connectivity in the limbic and within the frontoparietal as well as default mode networks, for which we believe there is sufficient converging evidence for a more detailed discussion in a developmental context. Furthermore, we address specific methodological challenges and current knowledge gaps that complicate the interpretation of early stress effects on functional connectivity and deserve particular attention in future studies. Finally, we highlight the forthcoming prospects and challenges of this research area with regard to establishing functional connectivity measures as validated biomarkers for brain developmental processes and individual risk stratification and as target phenotypes for mechanism-based interventions.

Candidate diagnostic biomarkers for neurodevelopmental disorders in children and adolescents: a systematic review

Neurodevelopmental disorders - including attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, communication disorders, intellectual disability, motor disorders, specific learning disorders, and tic disorders - manifest themselves early in development. Valid, reliable and broadly usable biomarkers supporting a timely diagnosis of these disorders would be highly relevant from a clinical and public health standpoint. We conducted the first systematic review of studies on candidate diagnostic biomarkers for these disorders in children and adolescents. We searched Medline and Embase + Embase Classic with terms relating to biomarkers until April 6, 2022, and conducted additional targeted searches for genome-wide association studies (GWAS) and neuroimaging or neurophysiological studies carried out by international consortia. We considered a candidate biomarker as promising if it was reported in at least two independent studies providing evidence of sensitivity and specificity of at least 80%. After screening 10,625 references, we retained 780 studies (374 biochemical, 203 neuroimaging, 133 neurophysiological and 65 neuropsychological studies, and five GWAS), including a total of approximately 120,000 cases and 176,000 controls. While the majority of the studies focused simply on associations, we could not find any biomarker for which there was evidence - from two or more studies from independent research groups, with results going into the same direction - of specificity and sensitivity of at least 80%. Other important metrics to assess the validity of a candidate biomarker, such as positive predictive value and negative predictive value, were infrequently reported. Limitations of the currently available studies include mostly small sample size, heterogeneous approaches and candidate biomarker targets, undue focus on single instead of joint biomarker signatures, and incomplete accounting for potential confounding factors. Future multivariable and multi-level approaches may be best suited to find valid candidate biomarkers, which will then need to be validated in external, independent samples and then, importantly, tested in terms of feasibility and cost-effectiveness, before they can be implemented in daily clinical practice.