Investigators will use a new method of fMRI analysis to study depressed patients undergoing treatment with an antidepressant medication and ask whether resting state network activity can be used to predict which patients will benefit from the medication.
The biological basis of depression remains poorly understood. The disease has become more readily treatable in the last two decades with new medications such as the selective serotonin reuptake inhibitors (Prozac, Celexa, and related drugs). Individual patients, however, show considerable variability in their responsiveness to different medications. As a result, physicians must often have patients try various medications until they determine which treatment most effectively treats their symptoms. This trial and error approach often results in delays in effective treatment and unpleasant side effects when patients need to undergo several different medication trials. A major issue in the treatment of depression, therefore, is to determine in advance which patients will respond best to which medications.
Investigators hypothesize that individual responsiveness to specific antidepressant medications can be predicted based on patterns of brain network activity as visualized by functional connectivity analyses of fMRI data. Brain networks consist of groups of nerve cells from different brain regions that communicate with one another. Using fMRI, researchers can monitor which groups of nerve cells are active at any given moment, much as a telephone switchboard shows which phone lines are in use. Functional connectivity studies take this analysis one step further. By comparing levels of activity among different groups of brain cells, researchers can determine which areas are communicating with one another. Recent functional connectivity analyses have shown that certain brain networks are especially active when the brain is at rest, and these have been termed “resting state networks.” (These networks may be involved in consolidating recent experiences into memory, or in scanning the sensory world for new information.)
The investigators have found that one of these networks, the “default-mode” network, shows increased connectivity in depressed patients compared to healthy controls. Now, they will assess the connectivity of this network and two others in controls and depressed patients undergoing treatment with the antidepressant citalopram (Celexa) to see if connectivity patterns in any of these networks are correlated with (1) clinical measures of depression and/or (2) positive response of depressed patients to medication. The study will involve 30 patients with active depression and 10 healthy, age-matched controls.
Significance: This work will advance understanding of the neural bases of depression, as well as potentially providing a tool for tailoring treatments to individual patients.