Prediction of Antidepressant Treatment Response Using Magnetic Resonance Imaging (MRI)
Christine DeLorenzo, Ph.D.
State University of New York (SUNY), Stony Brook , Stony Brook, NY
David Mahoney Neuroimaging Program
September 2015, for 3 years
Using new powerful MRI sequence to predict whether depressed patients will respond to SSRI treatment
Clinicians soon may be able to use a type of MRI imaging, called diffusion spectrum imaging, to predict whether an individual patient with depression is likely to respond to treatment with the class of antidepressants called SSRIs (selective serotonin reuptake inhibitors).
Clinicians need to choose the best treatment option for a depressed patient at the outset, since it takes a month or more for a treatment to demonstrate effectiveness. Selection is a trial and error process, though, because: there are several classes of antidepressants; each has its own method of action in the brain; and clinicians currently have no brain “biomarker” to guide them in determining what mode of action is indicated for an individual patient. SSRIs are the most frequently prescribed antidepressant class. Yet a striking two–thirds of people with depression treated with SSRIs do not achieve treatment remission.
One type of MRI imaging, called diffusion tensor imaging (DTI) has shown some evidence of being able to indicate whether an individual patient is likely to respond to SSRI treatment. Investigators now plan to use the even more powerful diffusion spectrum imaging (DSI) MRI technique to see if it can substantially improve the capacity to accurately predict which patients are likely to benefit from SSRI treatment.
SSRIs act by blocking brain cells from taking up serotonin. This electrochemical “transmitter” is involved in mood regulation. Serotonin neurons primarily originate in a small brain region called the “raphe nucleus.” From there, the cells’ axons (communication cables) connect to other regions of the brain including the amygdala, which is implicated in depression. Bundles of these communication cables are the brain’s “white matter” connecting the raphe nucleus to the amygdala and they can be imaged. Diffusion imaging indicates whether axons are healthy (well insulated, running in parallel). In the investigators’ prior studies, DTI imaging has indicated that patients’ white matter health prior to SSRI treatment is correlated with patients’ subsequent response to SSRIs—the healthier the white matter tracts, the better the SSRI response. But so far, imaging results of white matter health have explained less than half of the variability in patients’ treatment response.
The investigators hypothesize that they can substantially increase predictive accuracy by using the more powerful DSI imaging technique. DSI is able to detect white matter fibers that cross each other (rather than running in parallel) and provides increased resolution of white matter, especially in areas surrounding the small raphe nucleus. They will enroll 100 patients with depression, half of whom will be treated with SSRIs while the other half will receive placebo. Patients will undergo DSI imaging prior to treatment, and followed for eight weeks to assess depression remission or recurrence. If investigators find that patients with good white matter health respond effectively to SSRI treatment while patients with poor white matter health do not, they will have preliminary evidence that white matter health is a biomarker for predicting that SSRI treatment will be effective.
Significance: DSI imaging may provide the first marker in the brain that can be used to predict the likely effectiveness of SSRI treatment for depression on an individual level.
Christine DeLorenzo, Ph.D.
Dr. DeLorenzo is the Director of Stony Brook Medicine’s Center for Understanding Biology using Imaging Technology (CUBIT) and an Assistant Professor of Psychiatry, Biomedical Engineering and Electrical & Computer Engineering at Stony Brook University. Dr. DeLorenzo received Bachelor’s and Master’s Degrees in Biomedical Engineering from the Thayer School of Engineering at Dartmouth College. She received a PhD in Biomedical Engineering from Yale University. Dr. DeLorenzo’s postdoctoral research fellowship at Columbia University focused on the application of neuroimaging techniques, specifically Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI), in anxiety and mood disorders. Based on this training, Dr. DeLorenzo began to focus her studies on the use of state-of-the-art imaging and analysis techniques to uncover biological markers of depression and antidepressant treatment response. Funded by an NIMH career development award (K01), Dr. DeLorenzo examined the role of glutamate receptors in depression using PET. Currently supported by a research project (R01) grant from the NIMH, Dr. DeLorenzo uses Stony Brook Medicine’s simultaneous PET/MRI scanner to obtain rich imaging information from depressed subjects prior to, and following, antidepressant treatment. In this way, pretreatment markers of antidepressant effectiveness as well neurobiological correlates of depression improvement can be identified. The ultimate goal is to use neuroimaging techniques to reduce failed treatment trails and improve outcomes for people with depression.