Advancing Personalized Antidepressant Treatment Using PET/MRI

2015-12-08 01:58:24 | BioPortfolio


Despite current medications, morbidity and mortality of Major Depressive Disorder (MDD) remain high. According to the World Health Organization, MDD affects 121 million people worldwide, and is projected to be the second leading cause of global disability by 2020. Monotherapy with SSRIs is the most widely used treatment for MDD. However, on average, SSRIs require six weeks for onset of action, and two-thirds of those on SSRIs fail to achieve remission.Compounding this problem, patients with residual symptoms are significantly more likely to discontinue treatment or relapse, be hospitalized for medical and psychiatric conditions, or die of suicide and other causes. Although eliminating ineffective treatment trials would significantly reduce patient suffering and healthcare costs,clinicians currently do not have the tools to objectively select treatment based on an individual's likelihood of remission. Therefore, there is an urgent need to identify markers predictive of an individual's SSRI treatment outcome. Developing this personalized treatment requires increased understanding of the relationship between pretreatment neurobiology, SSRI-induced biological changes, and the corresponding symptom improvements.


Aim 1: Determine a Pretreatment Marker of SSRI Effectiveness Using PET. With the goal of reducing MDD burden, many studies have assessed the utility of FDG-PET in antidepressant treatment prediction. However, due to the limitations listed above, there is no consensus on which brain regions are predictive of treatment efficacy. In addition to serving as a biomarker of SSRI effectiveness, only conclusive determination of these regions will provide insight into depression pathophysiology, helping uncover SSRI mechanism of action, and aiding in the search of novel therapeutics. Based on the investigators' preliminary data and other, similar studies, the investigators hypothesize that SSRI-induced change in the Hamilton Depression Rating Scale (deltaHDRS) will be correlated with pretreatment metabolic rate of glucose (MRGlu, quantified using arterial blood analysis) in three potential regions: (1) midbrain, (2) right anterior insula, and/or (3) left ventral prefrontal cortex.

Aim 2: Isolate the Neurobiological Basis of the "Loss" Research Domain Criteria (RDoc) and the Change Associated with Treatment. Using a factor analysis of the HDRS, the investigators have previously demonstrated that the "loss" RDoC criteria is significantly correlated to MRGlu in frontal cortical areas. The investigators therefore hypothesize that change in MRGlu (pre to post treatment) in these regions will be correlated with symptom improvement specifically in "loss" symptoms. As an exploratory extension, the investigators will determine whether these changes are treatment-specific (i.e. to SSRI or placebo). A validation of the hypothesis suggests a targeted mechanism of action, and provides a significant step forward for precision treatment. If regional changes in MRGlu are not correlated to improvement in this RDoC category, it suggests that SSRI (or placebo) induced changes may be a downstream effect that should be examined further.

Aim 3: Validate NonInvasive Full Quantification of MRGlu Using Simultaneous Estimation. Full quantification of brain MRGlu with FDG (as performed in this study) requires measuring FDG in arterial plasma (input function) from arterial catheter insertion and blood analysis. This costly and invasive procedure creates a barrier to widespread PET use. The investigators have developed an innovative method for Simultaneous Estimation (SimE) of input information and PET outcome measures (e.g. MRGlu). SimE fully quantifies brain MRGlu without requiring an arterial catheter. In the case of FDG, the investigators' data suggests that SimE used with a single venous sample can provide accurate results. The investigators further hypothesize that the venous sample may be entirely replaced by study data (e.g., injected dose) and biometrics (e.g., body surface area, lean body mass index). Using two different approaches (statistical imputation and physiological parametric modeling) and previously collected data, the investigators will train the SimE for accurate quantification in the absence of blood data. The rich data collected in this study will then provide a robust benchmark for validation of the SimE approach.

Aim 4: Validate Noninvasive Estimates of Plasma Radioactivity from a Novel miniPET Scanner. In parallel to SimE (algorithm/software) development, the investigators will test a noninvasive method of plasma analysis using hardware. FDG concentration will be measured at the wrist, arm, ankle or leg with a novel synchronized PET scanner developed by co-Investigator, Dr. Paul Vaska.

Study Design

Allocation: Randomized, Intervention Model: Parallel Assignment, Masking: Double Blind (Subject, Investigator), Primary Purpose: Treatment




Escitalopram, Placebo


Stony Brook University
Stony Brook
New York
United States




Stony Brook University

Results (where available)

View Results


Published on BioPortfolio: 2015-12-08T01:58:24-0500

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