Usual first-line treatments for major depressive disorder (MDD) show less than a 40% remission rate. For many patients, the wrong treatment has significant individual and societal costs due to continued distress, risk of suicide, loss of productivity, wasted resources and potential neural effects associated with 2-3 months of an ineffective strategy. Though treatments are highly effective in some individuals, there is no reliable way to match patients to their best option. Therefore, a long term goal is to develop a clinically viable algorithm that selects the best treatment and avoids ineffective treatments, whil also identifying patients that require alternatives to standard first-line options. The objective o this proposal is to test the efficacy of a novel imaging biomarker developed to stratify patients into two subtypes that predict the likelihood of remission to monotherapy with cognitive behavioral therapy (CBT) or escitalopram (sCIT)--two standard first-line treatments for MDD. Our central hypothesis is that use of insula metabolism as a treatment selection biomarker (TSB) to assign treatment will increase remission rates to at least 50%, exceeding the usual 35-40% commonly reported with usual care. Data from the previous funding period identified the insula as the best discriminator of remission/non response to CBT and sCIT. The data further indicated other potential biomarkers that identify patients who will fail to remit to combined treatment with both CBT and sCIT. We will examine two specific aims: 1) To prospectively test the efficacy of the insula TSB to assign individual MDD patients to treatment with either CBT or sCIT; and 2) To further characterize brain subtypes predictive of combined treatment failure., Treatment assignment will be determined by the level of glucose metabolism in the right anterior insula measured with positron emission tomography (FDG PET). We will derive each patient's insula TSB using a simple region/whole brain ratio approach and a fixed cut-off. Patients will be assigned to a 12-week treatment course of CBT or sCIT using the insula TSB value. Patients who do not remit to their TSB-assigned first treatment will receive a second 12-week course of combined CBT and sCIT. Baseline whole brain metabolism will be evaluated in patients who do not remit after both monotherapy and combination therapy to determine biomarkers of dual failure. Lastly, we will examine regional metabolic changes over the first 12 weeks of treatment to characterize mechanisms mediating remission across insula subtypes. This proposal is innovative because it tests a novel imaging biomarker strategy developed specifically to prospectively stratify patients into two distinct treatment specific subtypes that will predict the likelihood of response to two standard first line therapies for a major depressive episode. In addition, this study will further characterize a second biomarker that identifies patients who fail to remit to both treatments. The proposed research is significant because it directly addresses the need for evidence-based methods for selecting optimal treatments for individual depressed patients with the ultimate goal of improving treatment outcomes.

Public Health Relevance

For patients with major depression, a dependable treatment selection algorithm that matches an individual to their best treatment option while also avoiding an ineffective treatment is a critical clinical need. This proposal will test the efficacy of a novl brain imaging biomarker to prospectively stratify patients into two subtypes that predict the likelihood of remission to monotherapy with cognitive behavioral therapy or escitalopram--two standard first-line treatments for a major depressive episode.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH073719-08
Application #
9262994
Study Section
Neural Basis of Psychopathology, Addictions and Sleep Disorders Study Section (NPAS)
Program Officer
Rumsey, Judith M
Project Start
2005-04-01
Project End
2019-04-30
Budget Start
2017-05-01
Budget End
2018-04-30
Support Year
8
Fiscal Year
2017
Total Cost
$736,722
Indirect Cost
$264,464
Name
Emory University
Department
Psychiatry
Type
Schools of Medicine
DUNS #
066469933
City
Atlanta
State
GA
Country
United States
Zip Code
30322
Ramirez-Mahaluf, Juan P; Roxin, Alexander; Mayberg, Helen S et al. (2017) A Computational Model of Major Depression: the Role of Glutamate Dysfunction on Cingulo-Frontal Network Dynamics. Cereb Cortex 27:660-679
Carrillo-Roa, Tania; Labermaier, Christiana; Weber, Peter et al. (2017) Common genes associated with antidepressant response in mouse and man identify key role of glucocorticoid receptor sensitivity. PLoS Biol 15:e2002690
Kang, Jian; Bowman, F DuBois; Mayberg, Helen et al. (2016) A depression network of functionally connected regions discovered via multi-attribute canonical correlation graphs. Neuroimage 141:431-441
Mayberg, Helen S (2016) Implementing Recommendations for Depression Screening of Adults: How Can Neurology Contribute to the Dialogue? JAMA Neurol 73:270-1
Dunlop, Boadie W; Kelley, Mary E; McGrath, Callie L et al. (2015) Preliminary Findings Supporting Insula Metabolic Activity as a Predictor of Outcome to Psychotherapy and Medication Treatments for Depression. J Neuropsychiatry Clin Neurosci 27:237-9
McGrath, Callie L; Kelley, Mary E; Dunlop, Boadie W et al. (2014) Pretreatment brain states identify likely nonresponse to standard treatments for depression. Biol Psychiatry 76:527-35
Dunlop, Boadie W; Mayberg, Helen S (2014) Neuroimaging-based biomarkers for treatment selection in major depressive disorder. Dialogues Clin Neurosci 16:479-90
Choi, Ki Sueng; Holtzheimer, Paul E; Franco, Alexandre R et al. (2014) Reconciling variable findings of white matter integrity in major depressive disorder. Neuropsychopharmacology 39:1332-9
Mayberg, Helen S (2014) Neuroimaging and psychiatry: the long road from bench to bedside. Hastings Cent Rep Spec No:S31-6
McGrath, Callie L; Kelley, Mary E; Holtzheimer, Paul E et al. (2013) Toward a neuroimaging treatment selection biomarker for major depressive disorder. JAMA Psychiatry 70:821-9

Showing the most recent 10 out of 19 publications