Grant: Doctoral: Vanier Canada Graduate Scholarships

While a variety of antidepressant medications and psychological ("talk") therapies have been shown to effectively treat depression, fewer than half of depressed individuals will reach remission following initial treatment. This is due, in part, to a trial-and-error approach often used in the treatment of depression, and a lack of tools for determining which treatment will be most effective for an individual. Cognitive behavioural therapy (CBT), which consists of skill-building techniques for helping individuals think and act in ways that will help alleviate and prevent depressive symptoms, has been shown to be as effective as antidepressant medication in treating acute depression and preventing future depressive episodes. However, we do not currently have a method for predicting what specific CBT techniques will be helpful for a person, and which can be omitted to save time and costs associated with psychological interventions. Therefore, the current research will investigate whether cost-effective, easy-to-administer computer-based tasks measuring core features of depression, namely negative emotional bias and motivational insensitivity, can predict what specific CBT techniques will be most effective for treating an individual's depressive symptoms. If shown to selectively predict response to cognitive and behavioural strategies for improving depressed mood, these computer-based tools may be useful for streamlining depression treatment decisions in a range of clinical and geographical contexts.