Using the biopsychosocial model for identifying subgroups of detained juveniles at different risk of re-offending in practice: a latent class regression analysis approach

Neurobiological correlates of antisociality across adolescence and young adulthood: a multi-sample, multi-method study

BACKGROUND

Antisociality across adolescence and young adulthood puts individuals at high risk of developing a variety of problems. Prior research has linked antisociality to autonomic nervous system and endocrinological functioning. However, there is large heterogeneity in antisocial behaviors, and these neurobiological measures are rarely studied conjointly, limited to small specific studies with narrow age ranges, and yield mixed findings due to the type of behavior examined.

METHODS

We harmonized data from 1489 participants (9-27 years, 67% male), from six heterogeneous samples. In the resulting dataset, we tested relations between distinct dimensions of antisociality and heart rate, pre-ejection period (PEP), respiratory sinus arrhythmia, respiration rate, skin conductance levels, testosterone, basal cortisol, and the cortisol awakening response (CAR), and test the role of age throughout adolescence and young adulthood.

RESULTS

Three dimensions of antisociality were uncovered: 'callous-unemotional (CU)/manipulative traits', 'intentional aggression/conduct', and 'reactivity/impulsivity/irritability'. Shorter PEPs and higher testosterone were related to CU/manipulative traits, and a higher CAR is related to both CU/manipulative traits and intentional aggression/conduct. These effects were stable across age.

CONCLUSIONS

Across a heterogeneous sample and consistent across development, the CAR may be a valuable measure to link to CU/manipulative traits and intentional aggression, while sympathetic arousal and testosterone are additionally valuable to understand CU/manipulative traits. Together, these findings deepen our understanding of the fundamental mechanisms underlying different components of antisociality. Finally, we illustrate the potential of using current statistical techniques for combining multiple datasets to draw robust conclusions about biobehavioral associations.

Added value of neurotechnology for forensic psychiatric and psychological assessment

The attention for neuroscience in relation to criminal behavior is growing rapidly, and research shows that neurobiological factors have added value to the understanding of psychological and social factors in explaining delinquency. There is evidence that neurotechnology can be used in criminal justice and may be of relevance for forensic psychiatric and psychological assessment. However, the question is whether scientific knowledge of neurobiological factors is applicable in daily practice of forensic assessment. Incorporation of basic technologies, e.g., psychophysiology (heart rate, skin conductance, wearables), hormonal measures (cortisol, testosterone), and neuropsychological testing might be evident, since they can be applied relatively easy. Moreover, a body of research shows the additive value of these technologies in this field. In addition, first steps are taken to apply these technologies in individual diagnostics, treatment, and risk assessment. Complex neurotechnologies like functional MRI (e.g., brain reading) and EEG show potential to be applicable in criminal justice, once it is known what additional information these indices offer for individual cases.

Evaluating sensitivity to classification uncertainty in latent subgroup effect analyses

Background

Increasing attention is being given to assessing treatment effect heterogeneity among individuals belonging to qualitatively different latent subgroups. Inference routinely proceeds by first partitioning the individuals into subgroups, then estimating the subgroup-specific average treatment effects. However, because the subgroups are only latently associated with the observed variables, the actual individual subgroup memberships are rarely known with certainty in practice and thus have to be imputed. Ignoring the uncertainty in the imputed memberships precludes misclassification errors, potentially leading to biased results and incorrect conclusions.

Methods

We propose a strategy for assessing the sensitivity of inference to classification uncertainty when using such classify-analyze approaches for subgroup effect analyses. We exploit each individual’s typically nonzero predictive or posterior subgroup membership probabilities to gauge the stability of the resultant subgroup-specific average causal effects estimates over different, carefully selected subsets of the individuals. Because the membership probabilities are subject to sampling variability, we propose Monte Carlo confidence intervals that explicitly acknowledge the imprecision in the estimated subgroup memberships via perturbations using a parametric bootstrap. The proposal is widely applicable and avoids stringent causal or structural assumptions that existing bias-adjustment or bias-correction methods rely on.

Results

Using two different publicly available real-world datasets, we illustrate how the proposed strategy supplements existing latent subgroup effect analyses to shed light on the potential impact of classification uncertainty on inference. First, individuals are partitioned into latent subgroups based on their medical and health history. Then within each fixed latent subgroup, the average treatment effect is assessed using an augmented inverse propensity score weighted estimator. Finally, utilizing the proposed sensitivity analysis reveals different subgroup-specific effects that are mostly insensitive to potential misclassification.

Conclusions

Our proposed sensitivity analysis is straightforward to implement, provides both graphical and numerical summaries, and readily permits assessing the sensitivity of any machine learning-based causal effect estimator to classification uncertainty. We recommend making such sensitivity analyses more routine in latent subgroup effect analyses.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01720-8.

Integrating Cognitive Developmental Neuroscience in Society: Lessons Learned From a Multidisciplinary Research Project on Education and Social Safety of Youth

Integrating fundamental science in society, with the goal to translate research findings to daily practice, comes with certain challenges. Successfully integrating research projects into society requires (1) good collaboration between scientists and societal stakeholders, (2) collaboration partners with common expectations and goals, and (3) investment in clear communication. Here we describe an integrative research project conducted by a large Dutch consortium that consisted of neuroscientists, psychologists, sociologists, ethicists, teachers, health care professionals and policy makers, focusing on applying cognitive developmental neuroscience for the benefit of youth in education and social safety. We argue that to effectively integrate cognitive developmental neuroscience in society, (1) it is necessary to invest in a well-functioning, diverse and multidisciplinary team involving societal stakeholders and youth themselves from the start of the project. This aids to build a so-called productive interactive network that increases the chances to realize societal impact in the long-term. Additionally, we propose that to integrate knowledge, (2) a different than standard research approach should be taken. When focusing on integration, the ultimate goal of research is not solely to understand the world better, but also to intervene with real-life situations, such as education or (forensic) youth care. To accomplish this goal, we propose an approach in which integration is not only started after the research has been conducted, but taken into account throughout the entire project. This approach helps to create common expectations and goals between different stakeholders. Finally, we argue that (3) dedicating sufficient resources to effective communication, both within the consortium and between scientists and society, greatly benefits the integration of cognitive developmental neuroscience in society.

Heart-wired to be cold? Exploring cardiac markers of callous-unemotional traits in incarcerated offenders

Elevated callous-unemotional (CU) traits have been repeatedly identified in a subgroup of offenders that displays severe antisocial behavior; establishing physiological markers may help improve early identification and treatment efforts. This study examines to what extent baseline-resting heart rate (HR) and heart rate variability (HRV) can be used as markers of CU in incarcerated juvenile and adult offenders. CU traits were assessed using the Inventory of Callous-Unemotional traits. Results of the multiple hierarchical regression tests indicated that there was a small yet significant positive association between baseline HR and CU and negative association between HRV and CU in juvenile offenders with medium model effect sizes (R² = 0.115 for HR-CU; R² = 0.126 for HRV-CU). The cardiac markers were unrelated to CU in adult offenders. These findings are important because they demonstrate that impaired cardiac autonomic activity is related to CU traits in juveniles, suggesting that socioemotional processing difficulties should be considered in understanding these deficits. Future research should be conducted in large samples, under reactive and static conditions, while including cardiac covariates, to get more clarity on the interplay between biological systems and behavioral expression.