Psychopathic traits are differentially associated with efficiency of neural communication.

Recent advances in the application of graph theory made it possible to quantify the efficiency of communication within a neural network, going beyond traditional connectivity methods that only identify the degree to which neural regions are connected. Psychopathic traits, namely, interpersonal-affective and impulsive-antisocial traits, have been linked to widespread and distinct disruptions in neural connectivity. The efficiency of neural communication for individuals high on these psychopathic traits, though, is unknown. In the present study, resting-state EEG was used to generate a connectivity matrix (i.e., weighted phase lag index) for multiple frequency bands. These connectivity matrices were examined using minimum spanning tree analysis, a graph theory approach that allows for the examination of neural efficiency, and regressed on Self-Report Psychopathy-Short Form scores (n = 158, unselected community sample). Results indicated that individuals with higher interpersonal-affective traits had significantly less efficient communication within alpha1 (i.e., long-range neural communication) and gamma (i.e., short-range neural communication) frequency bands. Conversely, individuals with higher impulsive-antisocial traits had more efficient communication within these same frequency bands. Overall, elevated psychopathic traits were related to alterations in the basic efficiency of neural communication. Moreover, this unique application of graph analysis provides a new avenue for inquiry into the mechanisms underlying the chronic and severe behavior of individuals with psychopathic traits.