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

Ryan Smith, Namik Kirlic, Jennifer Stewart, James Touthang, Rayus Kuplicki, Timothy J. McDermott, Sahib S. Khalsa, Martin P Paulus, Robin Aupperle
2020 unpublished
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 vs. reward (emotional conflict) relative to healthy controls (HCs). However, it remains unknown whether these computational parameters and group differences are stable
more » ... er time. Here 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 also assessed group differences in the entire sample as well as a subset matched for age and IQ across HCs (N=48), SUD (N=29), and DEP/ANX (N=121). Emotional conflict and decision uncertainty parameters showed moderate 1-year intra-class correlations (.52 and .46). Similar to previous baseline findings, these 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.
doi:10.31234/osf.io/bsuh8 fatcat:ebrzipgbkbdtriar73adrs4wwi