AI-Based Analysis of EEG and Neuro-imaging Correlates of PCL-R Dimensions: Opportunities, Constraints, and a Research Agenda

Psychopathy is conventionally assessed through structured clinical interviews and behavioral rating instruments such as the Psychopathy Checklist-Revised (PCL-R), a process that is time-intensive, dependent on rater training, and not scalable for large-cohort or screening use. Advances in electroencephalography (EEG) signal processing, functional magnetic resonance imaging (fMRI) analysis, and deep learning have enabled automated classification pipelines for a range of psychiatric and neurological conditions, raising the question of whether comparable pipelines could support research into the neurocognitive correlates of psychopathic traits. This paper does not report a trained model or empirical classification results; rather, it proposes a conceptual and methodological framework intended to orient future empirical work in this area. We review the current state of AI-assisted neuroimaging classification in adjacent psychiatric domains, identify the specific theoretical and practical obstacles that distinguish psychopathy research from those domains, and propose a four-layer framework spanning theoretical grounding, data and labeling strategy, computational modeling, and validation. We argue that the field's most useful near-term contribution is not a binary psychopath/non-psychopath classifier but a dimensional, explainable, cross-validated modeling approach anchored to established neurocognitive theory. We close with a concrete research agenda and an explicit discussion of the ethical constraints that should shape how such systems are built, evaluated, and reported.