Major Depressive Disorder (MDD) is a highly prevalent, chronic and recurrent disorder predicted to be the leading cause of disease burden by the year 2030. Monotherapy with selective serotonin reuptake inhibitors (SSRIs) is the most widely used MDD treatment. However, on average, SSRIs require six weeks for onset of action, and two-thirds of those on SSRIs fail to achieve remission. Consequently, to reduce MDD morbidity and mortality, there is a critical need to improve our understanding of the neural signatures predictive of, and correlated with, an individual's SSRI treatment outcome. Positron Emission Tomography (PET) imaging with 2- [18F]-fluorodeoxyglucose (FDG), a sensitive indicator of cerebral function, has the potential to provide this insight. In this proposal, we wil image 100 MDD subjects using a simultaneous PET/MRI scanner prior to and following 12 weeks of antidepressant treatment. Subjects will be randomized to either escitalopram (an SSRI) or placebo, allowing separation of SSRI-induced changes from the placebo effect. This proposal overcomes limitations of previous FDG treatment studies (including our own) by using the largest sample size to date and full FDG quantification (including arterial blood analysis). Pretreatment images will allow the determination of a pretreatment marker of SSRI effectiveness. Post to pre-treatment image comparison will allow analysis of treatment-induced brain metabolism changes and the correlation between these changes and certain dimensions of NIMH's Research Domain Criteria (RDoC). Since these domains are independent of diagnosis, this study has the potential to improve our understanding and treatment of these symptoms across all diagnoses. Regardless of study outcome, these aims will provide insight into the pathophysiology of MDD and mechanism of SSRI action. This would have immediate and significant clinical utility. Further, identification of useful brain markers is the first step toward the development of other, potentially non-imaging based, diagnostics. In addition to these clinical aims, development and validation of significant and novel methodology/hardware pioneered by Stony Brook investigators is proposed. Our group has previously successfully developed and tested a simultaneous estimation algorithm that calculates a subject's arterial input function (required for the most accurate quantification) from a single blood sample. In this application, we will both validate this algorithm as well as use statistical or physiological modeling to obviate the need for any blood samples. We will also validate a miniature PET scanner that fits around the wrist for estimation of arterial samples. These innovative techniques have the potential to entirely eliminate the need for blood sampling (while obtaining full quantification), which would be a significant advantage for the majority of institutions that are nt equipped for blood analysis or cannot afford it. Our multidisciplinary team is uniquely able to perform this clinical and technical study, the results of which have the potential to advance the field, as well as reduce barriers (price and subject burden) to widespread clinical and research PET use.

Public Health Relevance

Currently, there are no objective methods for choosing an antidepressant treatment, leading to weeks of ineffective treatment trials, and increased burden on depressed patients, their families and the economy. In this proposal, an image-based method (that has already shown promising results) is developed that provides insight into the biology of major depression and can be used to predict an individual's likelihood of antidepressant response (prior to treatment). In addition, innovative techniques, developed by Stony Brook investigators, that would decrease the burden and cost of this imaging method are applied and validated.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Medical Imaging Study Section (MEDI)
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Rumsey, Judith M
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State University New York Stony Brook
Schools of Medicine
Stony Brook
United States
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