Cognitive control circuit function predicts antidepressant outcomes: A signal detection approach to actionable clinical decisions

Abstract

Background

We previously identified a cognitive biotype of depression characterized by dysfunction of the brain’s cognitive control circuit, comprising the dorsolateral prefrontal cortex (dLPFC) and dorsal anterior cingulate cortex (dACC), derived from functional magnetic resonance imaging (fMRI). We evaluate these circuit metrics as personalized predictors of antidepressant remission.

Methods

We undertook a secondary analysis of data from the international Study to Predict Optimized Treatment in Depression (iSPOT-D) for 159 patients who completed fMRI during a GoNoGo task, 8 weeks treatment with one of three study antidepressants and who were assessed for remission status (Hamilton Depression Rating Scale score of ≤ 7). Circuit predictors of remission were dLPFC and dACC activity and connectivity quantified in standard deviations. Using established software implementing receiver operating analysis (ROC) we calculated the sensitivity and specificity of these predictors at every cut-point for every circuit measure. We calculated number needed to treat (NNT) metrics for the ROC model identifying optimal cut-point values.

Results

ROC models identified maximum separation of remitters (62.5%) from non-remitters (21.2%) at an initial cut-point of −0.75 standard deviations for dLPFC activity and averaged circuit metrics at secondary cutpoints. The NNT was 3.72, implying that if 4 patients (rounding of 3.72) were randomly selected, one would be likely to remit, but if circuit metrics informed treatment, two would be likely to remit.

Conclusions

Our findings contribute to identifying clinically actionable circuit measures for clinical trials and clinical practice. Future studies are needed to replicate these findings and expand the assessment of longer-term outcomes.