Resting State fMRI: A Novel Approach to Understanding Brain Dysfunction in Major Depression

Michael Greicius, M.D., M.P.H.

Stanford University

Funded in June, 2008: $200000 for 3 years
LAY SUMMARY . ABSTRACT . BIOGRAPHY .

LAY SUMMARY

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Exploring Neural Connectivity Patterns That Predict Responsiveness to Antidepressant Treatment

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.

ABSTRACT

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Resting State fMRI: A Novel Approach to Understanding Brain Dysfunction in Major Depression

Despite decades of imaging research, the brain basis of major depression remains ill-defined.  Limited understanding of the underlying mechanisms has resulted in a trial and error approach to treatment, which can result in prolonged delays and exposure to potentially avoidable side-effects.  This study proposes using a novel imaging approach, resting-state functional connectivity to 1) enhance our understanding of the brain bases of the affective and cognitive symptoms of depression and 2) develop an objective fMRI biomarker of depression that will predict response to treatment before or shortly after starting an antidepressant.

Using independent component analysis of resting-state fMRI data, functional connectivity will be assessed separately in three brain networks that we have previously related to mood, anxiety, and executive function.  Thirty drug-free patients with depression will be scanned at baseline, at one week, and at eight weeks after treatment with citalopram.  Brain network connectivity in the baseline depressed state will be compared to network connectivity in thirty healthy controls using a two-sample t-test.  Baseline network connectivity in the depressed subjects will be correlated against measures of mood, anxiety, and cognition to explore relationships between distinct networks and distinct symptoms of depression.  Baseline scans of responders and non-responders will be compared using a two-sample t-test to search for patterns of brain network connectivity that can predict treatment response or failure.  Finally, paired t-tests, performed separately in responders and non-responders, will compare changes in network connectivity between the baseline scan and the one-week and eight-week scans to determine if early network changes seen at one week can predict subsequent clinical outcome at eight weeks.  Meeting the goals of this study will advance our understanding of the brain bases of depression and allow for concrete clinical applications of fMRI in its treatment.

INVESTIGATOR BIOGRAPHIES

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Michael Greicius, M.D., M.P.H.

Dr. Greicius is an Assistant Professor of Neurology and Neurological Sciences and, by courtesy, Psychiatry and Behavioral Sciences at Stanford University.  He received his medical degree from the Columbia University College of Physicians and Surgeons and completed a neurology residency at Brigham and Women’s and Massachusetts General Hospitals (Partners Program) in Boston.  He arrived at Stanford in 2000 for a combined fellowship in functional MRI and behavioral neurology (at UCSF).

He is the medical director of the Stanford Memory Clinic and principal investigator of the Functional Imaging in Neuropsychiatric Disorders (FIND) Lab at Stanford.  The FIND Lab uses functional MRI in conjunction with other imaging modalities to detect and characterize neural networks in healthy adults and patients with neuropsychiatric disorders. The main research objective of the lab is to develop novel imaging biomarkers that will enhance the understanding, diagnosis, and treatment of disorders such as Alzheimer's disease, major depression, and schizophrenia.