Ali 1 PROJECT SUMMARY There is a pressing need for predicting antidepressant response early in treatment to reduce patient suffering and economic burden. Conventional antidepressants typically require two months to determine efficacy, and two-thirds of patients will not remit (be free of depression) while on their first-line treatment. No study to date has identified clinically useful markers to predict antidepressant response early in treatment. Therefore, the long- term objective of this project is to develop a predictive algorithm for antidepressant treatment efficacy early in treatment by using noninvasive brain imaging. The central hypothesis of this proposal is that brain changes, assessed by imaging, can be used as early predictors of antidepressant response. Magnetic resonance imaging (MRI) can provide valuable information about brain structure and function through various techniques early in treatment that may relate to the final response to antidepressant treatment. Even though these imaging techniques have been used to predict antidepressant response, the findings have been inconsistent, most likely due to variable study design and small sample size, and none of the imaging markers have been clinically validated. To fill these gaps, I will use a recently acquired imaging data from a large sample of patients at their initiation and first week of treatment, and their depression severity was quantified regularly by expert clinicians, to build a prediction model for antidepressant efficacy through the following aims. 1) Aim 1: Compare brain images acquired before and after antidepressant treatment to identify regions that need to change for the treatment to be effective. I will use imaging from a moderately large data set where patients with major depressive disorder (MDD) were imaged before and after 8 weeks of antidepressant treatment. I will measure brain structures and their activity in individuals who got better with treatment and analyze if there is significant difference in any brain regions in their depressive state compared to remitted state. I will then explore those regions in a large imaging data set to see if these necessary brain changes can be detected early in the first week of treatment. 2) Aim 2: Examine brain changes from the first week of treatment based on brain imaging and incorporate them into a predictive model for antidepressant efficacy. I will reduce the number of features related to brain structure and activity without losing information about the data. The selected features will be entered in a machine learning algorithm called XGBoost, which is time-efficient and cost-effective and has been used for detecting depression with moderate success. The model will rank features based on their contribution to prediction of antidepressant efficacy. If treatment response is found to be unrelated to imaging, this will inform future alternative imaging (e.g., EEG) or non-imaging (e.g., sleep, motor activity or location) studies. Impact : If successful, the proposed work will have broad implications for early monitoring of antidepressant efficacy and application of an effective clinical decision-making tool for treatment planning. Page 1 of 1

Ali 1 PROJECT NARRATIVE Individuals with major depressive disorder must wait approximately two months or even longer to determine efficacy of conventional first-line antidepressant treatments, even though two-thirds of them will not be free of depression. We are proposing to 1) identify changes in brain that are necessary to get effective results from 8 weeks of antidepressant treatment and examine if we can detect the initiation of these brain changes within the first week of treatment using brain imaging, and 2) develop a clinical decision support system with novel machine learning algorithms for predicting antidepressant response early in treatment. If successful, this tool will provide immediate clinical utility to assist clinicians with antidepressant treatment planning to reduce the burden of failed antidepressant trials. Page 1 of 1