Long-term stability of computational parameters during approach-avoidance conflict in a transdiagnostic psychiatric patient sample

Maladaptive behavior during approach-avoidance conflict (AAC) is common to multiple psychiatric disorders. Using computational modeling, we previously reported that individuals with depression, anxiety, and substance use disorders (DEP/ANX; SUDs) exhibited differences in decision uncertainty and sensitivity to negative outcomes versus reward (emotional conflict) relative to healthy controls (HCs). However, it remains unknown whether these computational parameters and group differences are stable over time. We analyzed 1-year follow-up data from a subset of the same participants (N=325) to assess parameter stability and relationships to other clinical and task measures. We assessed group differences in the entire sample as well as a subset matched for age and IQ across HCs (N=48), SUDs (N=29), and DEP/ANX (N=121). We also assessed 2-3 week reliability in a separate sample of 30 HCs. Emotional conflict and decision uncertainty parameters showed moderate 1-year intra-class correlations (.52 and .46, respectively) and moderate to excellent correlations over the shorter period (.84 and .54, respectively). Similar to previous baseline findings, parameters correlated with multiple response time measures (ps<.001) and self-reported anxiety (r=.30, p<.001) and decision difficulty (r=.44, p<.001). Linear mixed effects analyses revealed that patients remained higher in decision uncertainty (SUDs, p=.009) and lower in emotional conflict (SUDs, p=.004, DEP/ANX, p=.02) relative to HCs. This computational modelling approach may therefore offer relatively stable markers of transdiagnostic psychopathology.

Automated summary

By analyzing 1-year follow-up data, computational modeling parameters show a certain level of stability and correlation with other clinical indicators. Patients demonstrate differences in decision uncertainty and emotional conflict compared to healthy controls.