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Objective: The goal of the current study was to determine the extent to which atypical neural responsiveness during retaliation is associated with observed aggression in youth in residential care. Method: This functional magnetic resonance imaging study involved 83 adolescents (56 male and 27 female; mean age, 16.18 years) in residential care performing a retaliation task. Of the 83 adolescents, 42 displayed aggressive behavior within the first 3 months of residential care, whereas 41 did not. During the retaliation task, participants were offered either fair or unfair divisions of $20 pots (allocation phase) and could either accept the offer or reject it, and, by spending $1, $2, or $3, punish the partner (retaliation phase). Results: The study's main findings were that aggressive adolescent showed the following: reduced down-regulation of activity within regions involved in representing the expected value of choice options (left ventromedial prefrontal cortex and left posterior cingulate cortex) as a function of offer unfairness and retaliation level; and reduced recruitment of regions implicated in response control (right inferior frontal gyrus and bilateral anterior insular cortex) and associated fronto-parietal regions as a function of retaliation level. The aggressive adolescents were also significantly more likely to have been aggressive prior to residential care and showed a strong trend for increased retaliation on the task. Conclusion: We suggest that individuals with a greater propensity for aggression show reduced representation of the negative consequences of retaliation and associated reduced recruitment of regions potentially involved in over-ruling these negative consequences to engage in retaliation. Diversity & Inclusion Statement: We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. We worked to ensure race, ethnic, and/or ot

Background: Widespread structural alterations have been shown to be implicated in individuals with generalized anxiety disorder (GAD). However, there have been inconsistent findings in cortical volume (CV) differences. Most structural neuroimaging studies looking at GAD used region-based approach with relatively small sample sizes, let alone be specific to adolescents with GAD. We believe this is the first study to look at CV measures using a network-based approach in a larger sample of adolescents with GAD. The goal of the current study was to focus on three different brain networks (i.e., Limbic, Frontoparietal, and Default Mode Network [DMN]) in adolescents with GAD. Method: The study involved 81 adolescents with GAD and 112 typically developing (TD) comparison individuals matched on age (15.98 and 15.63 respective means), sex (42F/39M and 45F/67M), and IQ (101.90 and 103.94 respective means). Participants underwent structural MRI. Freesurfer was used to estimate CV (both network-specific and region-specific within networks) and region-specific sub-cortical volume measures. Multivariate analysis of covariance (MANCOVA; with sex, age, IQ, and intracranial volume [ICV] as potential covariates) was used to estimate group differences. Results: We found significantly lower CV for the DMN in adolescents with GAD, compared with TD individuals. Adolescents with GAD also showed significantly lower hemispheric mean CV of the default-mode regions (particularly the prefrontal and temporal regions) and the hippocampus, compared with TD individuals. Conclusion: The current findings suggest structural alterations in adolescents with GAD. These structural alterations will need to be addressed when implementing and developing treatments for patients with GAD.

The identification of clinically relevant neuroimaging biomarkers in psychiatry is a research priority. Neuropsychological tasks and functional MRI (fMRI) are used, via FDA-approved assessments, in clinical decision-making in many neurology departments. However, currently, psychiatry lacks neuro-psychological/fMRI biomarkers that could help in diagnosis and treatment planning. In our opinion, this likely reflects task design choices commonly used with psychiatric patients that limit test re-test reliability (TRR). Clinical decision-making can only occur via tests with excellent TRR. Statistical analyses indicate that TRR is particularly compromised if: (1) there are relatively few trials per condition; and (2) contrast-based analyses are adopted. We suggest, on the basis of the simulation work, that machine learning techniques combined with increasing the number of trials (per condition) and limiting the reliance on contrast-based analyses, can increase TRR and thus allow the successful development of cognitive neuroscience-based biomarkers for psychiatry in the near future.