No abstract

Biased patterns of attention toward threat are implicated as key mechanisms in anxiety that can be modified through automated intervention (attention-bias modification; ABM). Intervention refinement and personalized dissemination efforts are substantially hindered by gaps in understanding the precise attentional components that underlie ABM’s effects on symptoms—particularly with respect to longer-term outcomes. Seventy adults with transdiagnostic anxiety were randomized to receive eight sessions of active ABM ( n = 49) or sham training ( n = 21). Reaction time and eye-tracking data, collected at baseline, posttraining, and 1-month follow-up, dissociated multiple core attentional processes spanning overt and covert processes of engagement and disengagement. Self-reported symptoms were collected out to 1-year follow-up. Covert disengagement bias was specifically reduced by ABM, unlike all other indices. Overt disengagement bias at baseline predicted acute post-ABM outcomes, whereas covert engagement bias was nonspecifically predictive of symptom trajectories out to 1-year follow-up. Results suggest unique and dissociable roles for each discrete mechanism.

Dopaminergic function is a critical transdiagnostic neurophysiological dimension with broad relevance in psychiatry. Normalized T2*-weighted (nT2*w) imaging has been previously investigated as a method to quantify biological properties of tissue in the striatum (e.g., tissue iron), providing a widely available, in vivo marker with potential relevance to dopaminergic function; but no prior study to our knowledge has examined this neuroimaging marker in clinical depression. In a treatment-seeking, clinically depressed sample (n = 110), we quantified tissue iron (nT2*w) in striatal regions. We assessed test-retest reliability and correlated values with dimensional features across levels of analysis, including demographic/biological (sex, age, Body Mass Index), neuroanatomical (hippocampal atrophy, which was quantified using a recently validated machine-learning algorithm), and performance-based (Affective Go/NoGo task performance) indices with relevance to depressive neurocognition. Across patients, decreased tissue iron concentration (as indexed by higher nT2*w) in striatal regions correlated with indices of decreased cognitive-affective function on the Affective Go/NoGo task. Greater caudate nT2*w also correlated with greater hippocampal atrophy. Striatal tissue iron concentrations were robustly lower in female patients than males but gender differences did not explain relations with other neurocognitive variables. A widely available fMRI index of striatal tissue properties, which exhibited strong psychometric properties and can be readily quantified from most fMRI datasets irrespective of study-specific features such as task design, showed relevance to multiple biobehavioral markers of pathophysiology in the context of moderate-to-severe, treatment-resistant depression. Striatal tissue iron may play a role in dimensional and subgroup-specific features of depression, with implications for future research on depression heterogeneity.

Implicit self-associations are theorized to be rigidly and excessively negative in affective disorders like depression. Such information processing patterns may be useful as an approach to parsing heterogeneous etiologies, substrates, and treatment outcomes within the broad syndrome of depression. However, there is a lack of sufficient data on the psychometric, neural, and computational substrates of Implicit Association Test (IAT) performance in patient populations. In a treatment-seeking, clinically depressed sample (n ϭ 122), we administered five variants of the IAT-a dominant paradigm used in hundreds of studies of implicit cognition to date-at two repeated sessions (outside and inside a functional MRI scanner). We examined reliability, clinical correlates, and neural and computational substrates of IAT performance. IAT scores showed adequate (.67-.81) split-half reliability and convergent validity with one another and with relevant explicit symptom measures. Test-retest correlations (in vs. outside the functional MRI scanner) were present but modest (.15-.55). In depressed patients, on average, negative implicit self-representations appeared to be weaker or less efficiently processed relative to positive self-representations; elicited greater recruitment of frontoparietal task network regions; and, according to computational modeling of trial-by-trial data, were driven primarily by differential efficiency of information accumulation for negative and positive attributes. Greater degree of discrepancy between implicit and explicit self-worth predicted depression severity. Overall, these IATs show potential utility in understanding heterogeneous substrates of depression but leave substantial room for improvement. The observed clinical, neural, and computational correlates of implicit self-associations offer novel insights into a simple computer-administered task in a clinical population and point toward heterogeneous depression mechanisms and treatment targets.