Martin O. Job

This study validates the MISSING model by showing that sex differences in drug self-administration may arise from individual variability rather than biological sex.

The study develops a behavioral economic model to assess the recreational-to-habitual shift in drug use and finds it is unrelated to drug intake under normal and punishment conditions.

The paper uses a cluster-based model to show that high and low drug takers do not represent distinct user types based on cocaine intake levels.

The paper develops a new QSCAn model to differentiate dopamine receptor-mediated locomotor activity from cocaine and caffeine in rats, showing it outperforms existing methods.

The paper tests whether opioid user groups defined by access conditions represent distinct phenotypes using CENTERED clustering, finding they are mixtures of different user types.

The paper compares grouping by biological sex versus individual attributes to identify behavioral diversity in psychostimulant activity, finding that individual clustering reveals greater differences than sex-based grouping.

The study validates the MISSING model to explain inconsistencies in sex differences in methamphetamine self-administration by analyzing behavioral clusters in male and female rats.

The paper compares grouping subjects by biological sex versus behavioral clustering to identify distinct groups with specific dopamine activity patterns in rats.

The paper validates the MISSING model by showing that sex differences in psychostimulant activity only occur when comparing males and females from different behavioral groups.

The paper introduces a new model, BEST, to detect differences in drug effects on cocaine consumption, finding it more sensitive than existing models in certain contexts.

The paper introduces a novel TREND model to detect experience-dependent sex differences in the role of dorsal striatal dopamine D1 receptor systems in methamphetamine self-administration, finding that TREND outperforms existing models in this task.