Journal
JOURNAL OF AFFECTIVE DISORDERS
Volume 233, Issue -, Pages 21-35Publisher
ELSEVIER
DOI: 10.1016/j.jad.2017.10.049
Keywords
Major Depressive Disorder; Neuroimaging response biomarker; Frontolimbic; Pharmacotherapy; Psychotherapy; Stimulation therapy
Categories
Funding
- Ontario Brain Institute, an independent non-profit corporation, - Ontario Government
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Background: Current practice for selecting pharmacological and non-pharmacological antidepressant treatments has yielded low response and remission rates in Major Depressive Disorder (MDD). Neuroimaging biomarkers of brain structure and function may be useful in guiding treatment selection by predicting response vs. non-response outcomes. Methods: In this review, we summarize data from studies examining predictors of treatment response using structural and functional neuroimaging modalities, as they pertain to pharmacotherapy, psychotherapy, and stimulation treatment strategies. A literature search was conducted in OVID Medline, EMBASE, and PsycINFO databases with coverage from January 1990 to January 2017. Results: Several imaging biomarkers of therapeutic response in MDD emerged: frontolimbic regions, including the prefrontal cortex, anterior cingulate cortex, hippocampus, amygdala, and insula were regions of interest. Since these sub-regions are implicated in the etiology of MDD, their association with response outcomes may be the result of treatments having a normalizing effect on structural or activation abnormalities. Limitations: The direction of findings is inconsistent in studies examining these biomarkers, and variation across 'biotypes' within MDD may account for this. Limitations in sample size and differences in methodology likely also contribute. Conclusions: The identification of accurate, reliable neuroimaging biomarkers of treatment response holds promise toward improving treatment outcomes and reducing burden of illness for patients with MDD. However, before these biomarkers can be translated into clinical practice, they will need to be replicated and validated in large, independent samples, and integrated with data from other biological systems.
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