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Background Despite cognitive function impairment in depression, its relationship to treatment outcome is not well understood. Here, we examined whether pretreatment activation of cortical circuitry during test of cognitive functions predicts outcomes for three commonly used antidepressants. Methods Eighty medication-free outpatients with major depression and 34 matched healthy controls were included as participants in the International Study to Predict Optimized Treatment in Depression (iSPOT-D) trial. During functional magnetic resonance imaging, participants completed three tasks that assessed core domains of cognitive functions: response inhibition (Go/NoGo), selective attention (oddball), and selective working memory updating (1-back). Participants were randomized to 1 of 3 arms: escitalopram, sertraline (serotonin-specific reuptake inhibitors [SSRI]), or venlafaxine-extended release (serotonin and norepinephrine reuptake inhibitor [SNRI]) therapy. Functional magnetic resonance imaging scans were repeated after 8 weeks of treatment, and remission was assessed using the Hamilton Rating Scale for Depression. Results Dorsolateral prefrontal cortex activation during inhibitory “no go” responses was a general predictor of remission, with remitters having the same pretreatment activation as control participants and nonremitters hypoactivating relative to controls. Posttreatment dorsolateral prefrontal cortex activation was reduced in both remitters and controls but not in nonremitters. By contrast, inferior parietal activation differentially predicted remission between SSRI and SNRI medications, with SSRI remitters showing greater pretreatment activation than SSRI nonremitters and the SNRI group showing the opposite pattern. Conclusions Intact activation in the frontoparietal network during response inhibition, a core cognitive function, predicts remission with antidepressant treatment, particularly for SSRIs, and may be a potential substrate of the clinical effect of treatment.

Background There is increasing interest in using neurobiological measures to inform psychiatric nosology. It is unclear at the present time whether anxiety and depression are neurobiologically distinct or similar processes. It is also unknown if the best way to examine these disorders neurobiologically is by contrasting categorical definitions or by examining symptom dimensions. Methods A cross-sectional neuroimaging study was conducted of patients with generalized anxiety disorder (GAD), major depressive disorder (MDD), comorbid GAD and MDD (GAD/MDD), or neither GAD nor MDD (control subjects). There were 90 participants, all medication-free (17 GAD, 12 MDD, 23 GAD/MDD, and 38 control subjects). Diagnosis/category and dimensions/symptoms were assessed to determine the best fit for neurobiological data. Symptoms included general distress, common to anxiety and depression, and anxiety-specific (anxious arousal) or depression-specific (anhedonia) symptoms. Low-frequency (.008–.1 Hz) signal amplitude and functional connectivity analyses of resting-state functional magnetic resonance imaging data focused on a priori cortical and subcortical regions of interest. Results Support was found for effects of diagnosis above and beyond effects related to symptom levels as well as for effects of symptom levels above and beyond effects of diagnostic categories. The specific dimensional factors of general distress and anxious arousal as well as a diagnosis of MDD explained unique proportions of variance in signal amplitude or functional connectivity. Conclusions Using resting-state functional magnetic resonance imaging, our data show that a single conceptual model alone (i.e., categorical diagnoses or symptom dimensions) provides an incomplete mapping of psychopathology to neurobiology. Instead, the data support an additive model that best captures abnormal neural patterns in patients with anxiety and depression.