Recent advances in predicting responses to antidepressant treatment [version 1; referees: 2 approved]

Abstract

Major depressive disorder is one of the leading causes of disability in the world since depression is highly frequent and causes a strong burden. In order to reduce the duration of depressive episodes, clinicians would need to choose the most effective therapy for each individual right away. A prerequisite for this would be to have biomarkers at hand that would predict which individual would benefit from which kind of therapy (for example, pharmacotherapy or psychotherapy) or even from which kind of antidepressant class. In the past, neuroimaging, electroencephalogram, genetic, proteomic, and inflammation markers have been under investigation for their utility to predict targeted therapies. The present overview demonstrates recent advances in all of these different methodological areas and concludes that these approaches are promising but also that the aim to have such a marker available has not yet been reached. For example, the integration of markers from different systems needs to be achieved. With ongoing advances in the accuracy of sensing techniques and improvement of modelling approaches, this challenge might be achievable.

Keywords

• Behavioral Neuroscience
• Developmental & Pediatric Neurology
• Genomics
• Medical Genetics
• Mood Disorders
• Neurobiology of Disease & Regeneration
• Neurogenetics
• Neuroimaging
• Neuropharmacology & Psychopharmacology