Cognitive Signatures of Depressive and Anhedonic Symptoms and Affective States Using Computational Modeling and Neurocognitive Testing.

BACKGROUND: Deeper phenotyping may improve our understanding of depression. Because depression is heterogeneous, extracting cognitive signatures associated with severity of depressive symptoms, anhedonia, and affective states is a promising approach. METHODS: Sequential sampling models decomposed behavior from an adaptive approach-avoidance conflict task into computational parameters quantifying latent cognitive signatures. Fifty unselected participants completed clinical scales and the approach-avoidance conflict task by either approaching or avoiding trials offering monetary rewards and electric shocks. RESULTS: Decision dynamics were best captured by a sequential sampling model with linear collapsing boundaries varying by net offer values, and with drift rates varying by trial-specific reward and aversion, reflecting net evidence accumulation toward approach or avoidance. Unlike conventional behavioral measures, these computational parameters revealed distinct associations with self-reported symptoms. Specifically, passive avoidance tendencies, indexed by starting point biases, were associated with greater severity of depressive symptoms (R = 0.34, p = .019) and anhedonia (R = 0.49, p = .001). Depressive symptoms were also associated with slower encoding and response execution, indexed by nondecision time (R = 0.37, p = .011). Higher reward sensitivity for offers with negative net values, indexed by drift rates, was linked to more sadness (R = 0.29, p = .042) and lower positive affect (R = -0.33, p = .022). Conversely, higher aversion sensitivity was associated with more tension (R = 0.33, p = .025). Finally, less cautious response patterns, indexed by boundary separation, were linked to more negative affect (R = -0.40, p = .005). CONCLUSIONS: We demonstrated the utility of multidimensional computational phenotyping, which could be applied to clinical samples to improve characterization and treatment selection.

A Qualitative Examination of a School-Based Implementation of Computer-Assisted Cognitive-Behavioral Therapy for Child Anxiety.

Mental health treatment in schools has the potential to improve youth treatment access. However, school-specific barriers can make implementing evidence-based interventions difficult. Task-shifting (i.e., training lay staff to implement interventions) and computer-assisted interventions may mitigate these barriers. This paper reports on a qualitative examination of facilitators and barriers of a school-based implementation of a computer-assisted intervention for anxious youth (Camp Cope-A-Lot; CCAL). Participants (N = 45) included school staff in first through fourth grades. Providers attended a training in CCAL and received weekly, hour-long group consultation calls for three months. In the second year, the sustainability of CCAL use was assessed. Qualitative interviews were conducted after the first year (initial implementation) and second year (sustainability). Interviews were analyzed using the Consolidated Framework for Implementation Research domains to classify themes. Although participants reported that CCAL included useful skills, they expressed concerns about recommended session length (45 minutes) and frequency (weekly). Time burden of consultation calls was also a barrier. School staff facilitated implementation by enabling flexible scheduling for youth to be able to participate in the CCAL program. However, the sustainability of the program was limited due to competing school/time demands. Results suggest that even with computer assisted programs, there is a need to tailor interventions and implementation efforts to account for the time restrictions experienced by school-based service providers. Optimal fit between the intervention and specific school is important to maintain the potential benefits of computer-assisted treatments delivered by lay service providers in schools.