Pre-treatment scans of brain activity predicted whether depressed patients would best achieve remission with an antidepressant medication or psychotherapy, in a study funded by the National Institutes of Health.
“Our goal is to develop reliable biomarkers that match an individual patient to the treatment option most likely to be successful, while also avoiding those that will be ineffective,” explained Helen Mayberg, M.D., of Emory University, Atlanta, a grantee of the NIH’s National Institute of Mental Health.
Mayberg and colleagues report on their findings in JAMA Psychiatry, June 12, 2013.
“For the treatment of mental disorders, brain imaging remains primarily a research tool, yet these results demonstrate how it may be on the cusp of aiding in clinical decision-making,” said NIMH Director Thomas R. Insel, M.D.
Currently, determining whether a particular patient with depression would best respond to psychotherapy or medication is based on trial and error. In the absence of any objective guidance that could predict improvement, clinicians typically try a treatment that they, or the patient, prefer for a month or two to see if it works. Consequently, only about 40 percent of patients achieve remission following initial treatment. This is costly in terms of human suffering as well as health care spending.
Mayberg’s team hoped to identify a biomarker that could predict which type of treatment a patient would benefit from based on the state of his or her brain. Using a positron emission tomography (PET) scanner, they imaged pre-treatment resting brain activity in 63 depressed patients. PET pinpoints what parts of the brain are active at any given moment by tracing the destinations of a radioactively-tagged form of glucose, the sugar that fuels its metabolism.
They compared brain circuit activity of patients who achieved remission following treatment with those who did not improve.
Activity in one specific brain area emerged as a pivotal predictor of outcomes from two standard forms of depression treatment: cognitive behavior therapy (CBT) or escitalopram, a serotonin specific reuptake inhibitor (SSRI) antidepressant. If a patient’s pre-treatment resting brain activity was low in the front part of an area called the insula, on the right side of the brain, it signaled a significantly higher likelihood of remission with CBT and a poor response to escitalopram. Conversely, hyperactivity in the insula predicted remission with escitalopram and a poor response to CBT.
Among several sites of brain activity related to outcome, activity in the anterior insula best predicted response and non-response to both treatments. The anterior insula is known to be important in regulating emotional states, self-awareness, decision-making and other thinking tasks. Changes in insula activity have been observed in studies of various depression treatments, including medication, mindfulness training, vagal nerve stimulation and deep brain stimulation.
Emory clinical neuroscientists study brain scans in effort to find better ways to help patients recover from depression.
“If these findings are confirmed in follow-up replication studies, scans of anterior insula activity could become clinically useful to guide more effective initial treatment decisions, offering a first step towards personalized medicine measures in the treatment of major depression” said Mayberg.
Grant number: 5 R01 MH073719 04
The mission of the NIMH is to transform the understanding and treatment of mental illnesses through basic and clinical research, paving the way for prevention, recovery and cure. For more information, visit http://www.nimh.nih.gov.
The National Institutes of Health (NIH), the nation’s medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov.