Conduct disorder symptomatology is associated with an altered functional connectome in a large national youth sample

Antisocial behavior is associated with reduced frontoparietal network efficiency in youth

Youth antisocial behavior (AB) is associated with deficits in socioemotional processing, reward and threat processing and executive functioning. These deficits are thought to emerge from differences in neural structure, functioning and connectivity, particularly within the default, salience and frontoparietal networks. However, the relationship between AB and the organization of these networks remains unclear. To address this gap, the current study applied unweighted, undirected graph analyses to resting-state functional magnetic resonance imaging data in a cohort of 161 adolescents (95 female) enriched for exposure to poverty, a risk factor for AB. As prior work indicates that callous-unemotional (CU) traits may moderate the neurocognitive profile of youth AB, we examined CU traits as a moderator. Using multi-informant latent factors, AB was found to be associated with less efficient frontoparietal network topology, a network associated with executive functioning. However, this effect was limited to youth at low or mean levels of CU traits, indicating that these neural differences were specific to those high on AB but not CU traits. Neither AB, CU traits nor their interaction was significantly related to default or salience network topologies. Results suggest that AB, specifically, may be linked with shifts in the architecture of the frontoparietal network.

Classifying Conduct Disorder Using a Biopsychosocial Model and Machine Learning Method

BACKGROUND

Conduct disorder (CD) is a common syndrome with far-reaching effects. Risk factors for the development of CD span social, psychological, and biological domains. Researchers note that predictive models of CD are limited if the focus is on a single risk factor or even a single domain. Machine learning methods are optimized for the extraction of trends across multidomain data but have yet to be implemented in predicting the development of CD.

METHODS

Social (e.g., family, income), psychological (e.g., psychiatric, neuropsychological), and biological (e.g., resting-state graph metrics) risk factors were measured using data from the baseline visit of the Adolescent Brain Cognitive Development Study when youth were 9 to 10 years old (N = 2368). Applying a feed-forward neural network machine learning method, risk factors were used to predict CD diagnoses 2 years later.

RESULTS

A model with factors that included social, psychological, and biological domains outperformed models representing factors within any single domain, predicting the presence of a CD diagnosis with 91.18% accuracy. Within each domain, certain factors stood out in terms of their relationship to CD (social: lower parental monitoring, more aggression in the household, lower income; psychological: greater attention-deficit/hyperactivity disorder and oppositional defiant disorder symptoms, worse crystallized cognition and card sorting performance; biological: disruptions in the topology of subcortical and frontoparietal networks).

CONCLUSIONS

The development of an accurate, sensitive, and specific predictive model of CD has the potential to aid in prevention and intervention efforts. Key risk factors for CD appear best characterized as reflecting unpredictable, impulsive, deprived, and emotional external and internal contexts.