Using Machine Learning to Determine a Functional Classifier of Retaliation and Its Association With Aggression.

Objective: Methods to determine integrity of integrated neural systems engaged in functional processes have proven elusive. This study sought to determine the extent to which a machine learning retaliation classifier (retaliation vs unfair offer) developed from a sample of typically developing (TD) adolescents could be applied to an independent sample of clinically concerning youth and the classifier-determined functional integrity for retaliation was associated with antisocial behavior and proactive and reactive aggression. Method: Blood oxygen level-dependent response data were collected from 82 TD and 120 clinically concerning adolescents while they performed a retaliation task. The support vector machine algorithm was applied to the TD sample and tested on the clinically concerning sample (adolescents with externalizing and internalizing diagnoses). Results: The support vector machine algorithm was able to distinguish the offer from the retaliation phase after training in the TD sample (accuracy = 92.48%, sensitivity = 89.47%, and specificity = 93.18%) that was comparably successful in distinguishing function in the test sample. Increasing retaliation distance from the hyperplane was associated with decreasing conduct problems and proactive aggression. Conclusion: The current study provides preliminary data of the importance of a retaliation endophenotype whose functional integrity is associated with reported levels of conduct problems and proactive aggression. Diversity & Inclusion Statement: We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. We worked to ensure sex balance in the selection of non-human subjects. We worked to ensure diversity in experimental samples through the selection of the cell lines. We worked to ensure diversity in experimental samples through the selection of the genomic datasets. Diverse cell lines and/or genomic datasets were not available. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list. While citing references scientifically relevant for this work, we also actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our reference list. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work.