This application addresses broad Challenge Area (03) Biomarker Discovery and Validation, and specific Challenge Topic 03-MH-101: Biomarkers in mental disorders, in Project BOLD (Biomarkers for Outcomes In Late-life Depression). Major depressive disorder (MDD) is a common psychiatric illness with high cost to society and individual patients. One reason for the high cost is that most patients endure lengthy and ultimately unsuccessful empiric antidepressant trials before a successful medication is identified by trial-and-error. Care would be improved if a biomarker could determine, early in the course of treatment, whether a particular antidepressant would likely lead to response, remission, or treatment failure. Physicians could rapidly change treatments to an antidepressant which the biomarker indicated would be likely to help the patient. We have identified quantitative electroencephalographic (QEEG) changes that emerge early in the course of treatment with selective serotonin reuptake inhibitors (SSRIs) that appear to predict later response and remission in a general adult patient population. Demographic trends in the United States suggest that improved care for MDD will be essential for a growing number of elderly with late-life depression;expansion of the ranks of the elderly in the Hispanic community is projected to be explosive. While the consequences of prolonged trial-and- error periods to find a successful treatment are particularly inauspicious for elders with late-life depression, this patient group has not been included in the past studies which demonstrated the use of this biomarker approach in a general adult population. Similarly, there has been a dearth of information about treatment for MDD in the Hispanic community in general, and about potential biomarkers in particular. We propose a 12- week treatment trial to evaluate a practical biomarker for predicting outcome based on data from the first week of antidepressant treatment, with a focus only on depression in late life (age e65) and with oversampling of the Hispanic community in Los Angeles. Our specific aims are (1) to evaluate the performance of the ATR biomarker in late-life depression generally, and in the Hispanic community in particular and (2) to evaluate the additive contributions to predictive accuracy of the model by including clinical, socio-demographic, and genetic factors. We will test four specific hypotheses: H1: ATR prediction of treatment outcome in older adults will show >70% accuracy. H2: The predictive accuracy of the model will be enhanced by including clinical, socio- demographic, and genetic predictors. H3: The accuracy of ATR prediction in older Hispanic subjects will show >70% accuracy. H4: The accuracy of ATR prediction will not show a significant dependence on subject gender. A total of 80 older adults with MDD will receive a one-week pharmacologic challenge with the SSRI escitalopram (ESC), and receive 11 more weeks of ESC monotherapy. The primary outcome will be 12-week response (e50% improvement) on the 30-item Inventory of Depressive Symptomatology. We will test our hypotheses (1) by comparing clinical outcomes with biomarker predictions, and (2) by examining improvement in the predictive model from incorporating non-physiologic factors. A Data Safety Monitoring Board will oversee the project. This preliminary evaluation of applying our model to treatment selection in the elderly and in Hispanics will support further developments in personalized medicine approaches for depression. This project is highly appropriate for the RC1 mechanism and for support by the ARRA. The project will provide treatment for depression to elders who may not be able to afford care, will preserve academic positions endangered by the economic downturn, and will refine a new technology that promises to improve care for MDD. Finally, the project is supported by a unique public-private partnership with a small medical device company. This partnership will help ensure that the technology developed under the project will be developed further after the two-year project period. Furthermore, the public-private partnership will amplify the economic benefits of this technology by potentially creating hundreds of new private sector jobs. Late-life depression is a common and costly psychiatric illness, and is linked to increased healthcare costs, loss of independence, and poorer outcomes with heart disease, stroke, cancer and other illnesses. Biomarker- guided treatment could improve outcomes by rapidly identifying an effective medication for each patient. This project examines a neurophysiologic biomarker that has been tested in adults but not in depressed elders, particularly Hispanic elders, two groups that will grow dramatically in the next decades.