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TOWARD THE APPLICATION OF FUNCTIONAL NEUROIMAGING TO INDIVIDUALIZED TREATMENT FOR ANXIETY AND DEPRESSION
Author(s) -
Ball Tali M.,
Stein Murray B.,
Paulus Martin P.
Publication year - 2014
Publication title -
depression and anxiety
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.634
H-Index - 129
eISSN - 1520-6394
pISSN - 1091-4269
DOI - 10.1002/da.22299
Subject(s) - neuroimaging , panic disorder , anterior cingulate cortex , anxiety , psychology , functional neuroimaging , orbitofrontal cortex , major depressive disorder , clinical psychology , generalized anxiety disorder , antidepressant , psychiatry , prefrontal cortex , cognition
Functional neuroimaging has led to significant gains in understanding the biological bases of anxiety and depressive disorders. However, the ability of functional neuroimaging to directly impact clinical practice is unclear. One important method by which neuroimaging could impact clinical care is to generate single patient level predictions that can guide clinical decision‐making. The present review summarizes published functional neuroimaging studies of predictors of medication or psychotherapy outcome in major depressive disorder, obsessive‐compulsive disorder (OCD), posttraumatic stress disorder, generalized anxiety disorder, panic disorder, and social anxiety disorder. In major depressive disorder and OCD, there is converging evidence of specific brain circuitry that has both been implicated in the disordered state itself, and where pretreatment activation levels have been predictive of treatment response. Specifically, in major depressive disorder, greater pretreatment ventral and pregenual anterior cingulate cortex (ACC) activation may predict better antidepressant medication outcome but poorer psychotherapy outcome. In OCD, activation in the ACC and orbitofrontal cortex has been inversely associated with pharmacological treatment response. In other anxiety disorders, research in this area is just beginning, with the ACC potentially implicated. However, the question of whether these results can directly translate to clinical practice remains open. In order to achieve the goal of single patient level prediction and individualized treatment, future research should strive to establish replicable models with good predictive performance and clear incremental validity.