Predicting Internet-Based Treatment Response for Major Depressive Disorder

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As many as 53% of students report experiencing depression during college, and these depressive episodes are associated with a higher frequency of academic problems, comorbidity, and suicide. Although there are effective options for treatment, the majority of individuals (>70%) do not pursue services, and even for those who do, response rates remain modest (~40-50%). As a means of increasing accessibility to treatment, internet-based interventions for depression have been developed and tested. Despite increased availability, response to internet-based interventions continues to vary substantially, and failed treatment often contributes to persistence and worsening of symptoms. Therefore, identifying individuals with a high likelihood of responding to internet-based treatment would represent a major advance and address a critical unmet need. In recent years, promising approaches for testing the heterogeneity of the treatment effects – delineating which individuals are likely to respond to a given treatment – have been developed. However, their use for identifying predictors of treatment response in depression remains unclear. To address this unmet need, the proposed study will test a new, cost-effective, and feasibly-scaled method of predicting differential treatment response following internet-based cognitive behavioral therapy (iCBT) for depression in a large, representative college sample (Boston Consortium of Colleges and Universities which includes 7 schools). Members of the consortium have committed to screening all incoming freshmen (N = ~14,000) through a rigorous online assessment and to offer iCBT to students with elevated levels of depressive symptoms (i.e., minor or major depression). The following steps will be pursued. First, in the initial phase of the study, freshmen students from the Boston Consortium will be screened for depression through an online survey, and they also will complete web-based neurocognitive tasks probing key mechanisms underpinning depression. Depressed students will be invited to enroll in iCBT, and a predictive algorithm will be developed based on assessments across different units of analysis (i.e., clinical characteristics, neurocognitive indices) to identify iCBT responders. Second, after the development phase, an independent sample of college students will be recruited to validate the predictive algorithm. This validation phase will use clinical indicators and neurocognitive data. Additionally, functional magnetic resonance imaging (fMRI) data targeting key mechanisms within the Research Domain Criteria (RDoC) will be acquired from a subset of participants. Neural data will be integrated to determine whether they improve the predictive algorithm. Third, data across independent samples will be combined, which will increase power to refine our predictive model for both acute and sustained response. In summary, there are alarming rates of depression among college students, and only a minority of students utilize mental health services. The proposed research will personalize our approach to depression treatment, which, ultimately, will improve effectiveness and better inform mental health care across college campuses.