Investigation of Psychoactive Medications: Challenges and a Practical and Scalable New Path.

In the last two decades the validity of clinical trials in psychiatry has been subject to discussion. The most accepted clinical study method in the medical area, randomized controlled trial (RCT), faces significant problems when applied to the psychiatric world. One of the causes for this scenario is the strict participant inclusion and exclusion criteria that may not represent the real world.

A New Characterization of Mental Health Disorders Using Digital Behavioral Data: Evidence from Major Depressive Disorder.

Mental health disorders are ambiguously defined and diagnosed. The established diagnosis technique, which is based on structured interviews, questionnaires and data subjectively reported by the patients themselves, leaves the mental health field behind other medical areas. We support these statements with examples from major depressive disorder (MDD).

Optimizing prediction of response to antidepressant medications using machine learning and integrated genetic, clinical, and demographic data.

Major depressive disorder (MDD) is complex and multifactorial, posing a major challenge of tailoring the optimal medication for each patient. Current practice for MDD treatment mainly relies on trial and error, with an estimated 42-53% response rates for antidepressant use. Here, we sought to generate an accurate predictor of response to a panel of antidepressants and optimize treatment selection using a data-driven approach analyzing combinations of genetic, clinical, and demographic factors.

Altered brain-derived neurotrophic factor expression in the ventral tegmental area, but not in the hippocampus, is essential for antidepressant-like effects of electroconvulsive therapy.

Background: Impaired neuronal plasticity and, specifically, altered expression of brain-derived neurotrophic factor (BDNF) were shown to play a critical role in depressive behavior and the mechanism of various antidepressant treatments including electroconvulsive therapy (ECT). Interestingly, opposing roles were suggested for BDNF in the hippocampus and the ventral tegmental area (VTA), while interactions between these regions were shown on various levels. Here, we evaluated whether BDNF plays an essential role in the antidepressant-like effects of ECT and performed a direct comparison between BDNF manipulations in the VTA and the hippocampus.

Resilience to chronic stress is mediated by hippocampal brain-derived neurotrophic factor.

Chronic stress is a trigger for several psychiatric disorders, including depression; however, critical individual differences in resilience to both the behavioral and the neurochemical effects of stress have been reported. A prominent mechanism by which the brain reacts to acute and chronic stress is activation of the hypothalamic-pituitary-adrenal (HPA) axis, which is inhibited by the hippocampus via a polysynaptic circuit. Alterations in secretion of stress hormones and levels of brain-derived neurotrophic factor (BDNF) in the hippocampus were implicated in depression and the effects of antidepressant medications.