There are high rates of comorbidity between heavy drinking and depressive symptoms among college students, often resulting in severe alcohol-related consequences. No empirically supported treatment exists that concurrently addresses both of these problems in this population. Research with college students has demonstrated that brief motivational interventions (BMIs) reduce heavy drinking and alcohol-related consequences, and that cognitive behavioral therapy for depression (CBT-D) is effective in reducing depressive symptoms. Thus, a program combining BMI and CBT-D appears ideal for college students with co-occurring binge drinking and depressive symptoms. This manuscript presents the rationale and format of a BMI + CBT-D treatment protocol for this population, and provides a case example of a female college student who received the protocol and experienced improvement in depressive symptoms, a reduction in alcohol use and alcohol-related negative consequences, and an increase in readiness to change alcohol consumption. We discuss theoretical and clinical implications of these findings, and suggest directions for future research.

[Correction Notice: An Erratum for this article was reported in Vol 34(2) of Psychology of Addictive Behaviors (see record 2020-16883-001). In the original article the order of authorship was incorrect. The correct second and third authors should appear instead as Brian Borsari and Jennifer E. Merrill.] Heavy episodic drinking (HED) and depressive symptoms often co-occur among college students and are associated with significant impairment. However, evidence-based treatments for these common co-occurring conditions are not available for college students. The current study compared the effectiveness of a treatment combining Cognitive-Behavioral Therapy for Depression and Brief Motivational Interviewing (CBT-D + BMI) versus Cognitive-Behavioral Therapy for Depression (CBT-D) alone among 94 college students with HED and depressive symptoms. Both treatment programs were associated with significant reductions of similar magnitude in HED, alcohol-related problems (ARP), and depressive symptoms at the end of treatment and at the 1-month follow-up assessment. Moderation analyses indicated that, among college students with fewer depressive symptoms at baseline, CBT-D was associated with greater sustained reduction in heavy drinking relative to CBT-D + BMI at the 1-month follow-up. Although the study did not include a no-treatment condition, the magnitude of improvement during treatment in both groups was greater than what is expected with passage of time. Although clinicians in college counseling centers may lack specialty training for co-occurring conditions, CBT-D is widely implemented in college settings. Our findings suggest that CBT-D may reduce both depressive symptoms and HED in college students and may be used to address a significant public health problem. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

The efficacy of antidepressant treatment for depression is controversial due to the only modest superiority demonstrated over placebo. However, neurobiological heterogeneity within depression may limit overall antidepressant efficacy. We sought to identify a neurobiological phenotype responsive to antidepressant treatment by testing pretreatment brain activation during response to, and regulation of, emotional conflict as a moderator of the clinical benefit of the antidepressant sertraline versus placebo. Using neuroimaging data from a large randomized controlled trial, we found widespread moderation of clinical benefits by brain activity during regulation of emotional conflict, in which greater downregulation of conflict-responsive regions predicted better sertraline outcomes. Treatment-predictive machine learning using brain metrics outperformed a model trained on clinical and demographic variables. Our findings demonstrate that antidepressant response is predicted by brain activity underlying a key self-regulatory emotional capacity. Leveraging brain-based measures in psychiatry will forge a path toward better treatment personalization, refined mechanistic insights and improved outcomes.

Antidepressants are widely prescribed, but their efficacy relative to placebo is modest, in part because the clinical diagnosis of major depression encompasses biologically heterogeneous conditions. Here, we sought to identify a neurobiological signature of response to antidepressant treatment as compared to placebo. We designed a latent-space machine-learning algorithm tailored for resting-state electroencephalography (EEG) and applied it to data from the largest imaging-coupled, placebo-controlled antidepressant study (n = 309). Symptom improvement was robustly predicted in a manner both specific for the antidepressant sertraline (versus placebo) and generalizable across different study sites and EEG equipment. This sertraline-predictive EEG signature generalized to two depression samples, wherein it reflected general antidepressant medication responsivity and related differentially to a repetitive transcranial magnetic stimulation treatment outcome. Furthermore, we found that the sertraline resting-state EEG signature indexed prefrontal neural responsivity, as measured by concurrent transcranial magnetic stimulation and EEG. Our findings advance the neurobiological understanding of antidepressant treatment through an EEG-tailored computational model and provide a clinical avenue for personalized treatment of depression.