The dot-probe task has been widely used in research to produce an index of biased attention based on reaction times (RTs). Despite its popularity, very few published studies have examined psychometric properties of the task, including test-retest reliability, and no previous study has examined reliability in clinically anxious samples or systematically explored the effects of task design and analysis decisions on reliability. In the current analysis, we used dot-probe data from 3 studies in which attention bias toward threat-related faces was assessed at multiple (≥5) time-points. Two of the studies were similar (adults with social anxiety disorder, similar design features) whereas 1 was more disparate (pediatric healthy volunteers, distinct task design). We explored the effects of analysis choices (e.g., bias score formula, outlier handling method) on reliability and searched for convergent findings across the 3 studies. We found that, when concurrently considering the 3 studies, the most reliable RT index of bias used data from dot-bottom trials, comparing congruent to incongruent trials, with rescaled outliers, particularly after averaging across more than 1 assessment point. Although reliability of RT bias indices was moderate to low, within-session variability in bias (attention bias variability; ABV), a recently proposed RT index, was more reliable across sessions. Several eyetracking-based indices of attention bias (available in the pediatric healthy sample only) showed reliability that matched the optimal RT index (ABV). On the basis of these findings, we make specific recommendations to researchers using the dot-probe, particularly those wishing to investigate individual differences and/or single-patient applications.

Depressed patients show abnormalities in brain connectivity at rest, including hyperconnectivity within the default mode network (DMN). However, there is well-known heterogeneity in the clinical presentation of depression that is overlooked when averaging connectivity data. We used data-driven parsing of neural connectivity to reveal subgroups among 80 depressed patients completing resting state fMRI. Directed functional connectivity paths (eg, region A influences region B) within a depression-relevant network were characterized using Group Iterative Multiple Model Estimation, a method shown to accurately recover the direction and presence of connectivity paths in individual participants. Individuals were clustered using community detection on neural connectivity estimates. Subgroups were compared on network features and on clinical and biological/demographic characteristics that influence depression prognosis. Two subgroups emerged. Subgroup A, containing 71% of the patients, showed a typical pattern of connectivity across DMN nodes, as previously reported in depressed patients on average. Subgroup B exhibited an atypical connectivity profile lacking DMN connectivity, with increased dorsal anterior cingulate-driven connectivity paths. Subgroup B members had an over-representation of females (87% of Subgroup B vs 65% of Subgroup A; χ²=3.89, p=0.049), comorbid anxiety diagnoses (42.9% of Subgroup B vs 17.5% of Subgroup A; χ²=5.34, p=.02), and highly recurrent depression (63.2% of Subgroup B vs 31.8% of Subgroup A; χ²=5.38, p=.02). Neural connectivity-based categorization revealed an atypical pattern of connectivity in a depressed patient subset that would be overlooked in group comparisons of depressed and healthy participants, and tracks with clinically relevant phenotypes including anxious depression and episodic recurrence. Data-driven parsing suggests heterogeneous substrates of depression; ideally, future work building on these findings will inform personalized treatment.

Younger adults with anxiety disorders are known to show an attentional bias toward negative information. Little is known regarding the role of biased attention in anxious older adults, and even less is known about the neural substrates of any such bias. Functional magnetic resonance imaging (fMRI) was used to assess the mechanisms of attentional bias in late life by contrasting predictions of a top-down model emphasizing deficient prefrontal cortex (PFC) control and a bottom-up model emphasizing amygdalar hyperreactivity. In all, 16 older generalized anxiety disorder (GAD) patients (mean age=66 years) and 12 non-anxious controls (NACs; mean age=67 years) completed the emotional Stroop task to assess selective attention to negative words. Task-related fMRI data were concurrently acquired. Consistent with hypotheses, GAD participants were slower to identify the color of negative words relative to neutral, whereas NACs showed the opposite bias, responding more quickly to negative words. During negative words (in comparison with neutral), the NAC group showed PFC activations, coupled with deactivation of task-irrelevant emotional processing regions such as the amygdala and hippocampus. By contrast, GAD participants showed PFC decreases during negative words and no differences in amygdalar activity across word types. Across all participants, greater attentional bias toward negative words was correlated with decreased PFC recruitment. A significant positive correlation between attentional bias and amygdala activation was also present, but this relationship was mediated by PFC activity. These results are consistent with reduced prefrontal attentional control in late-life GAD. Strategies to enhance top-down attentional control may be particularly relevant in late-life GAD treatment.