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. The inconsistency of the different endpoint parameters that are used in the field is another cause. We think that psychiatric RCTs' challenges, together with the underlying complexity of psychiatry, lead to a problematic clinical practice. Today, psychoactive drugs are routinely tested not in an official clinical trial setting. Off-label psychoactive drugs are commonly prescribed, and other substances, such as herbal remedies, are also regularly consumed. Learning from those real-life experiments can teach us useful lessons. Real-world data (RWD) includes information about heterogeneous patient populations, and it can be measured with standardized parameters. Collecting RWD can also address the need for systematically documenting and sharing case reports' outcomes. We suggest using digital tools to capture objective and continuous behavioral data from patients passively. New conclusions will be constantly drawn, possibly allowing more personalized treatment outcomes. The relevant next-generation decision support tools are already available.

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). The National Institute of Mental Health (NIMH) launched the Research Domain Criteria (RDoC) project in 2009 as a new framework to investigate psychiatric pathologies from a multidisciplinary point of view. This is a good step in the right direction. Contemporary psychiatry considers mental illnesses as diseases that manifest in the mind and arise from the brain, expressed as a behavioral condition; therefore, we claim that these syndromes should be characterized primarily using behavioral characteristics. We suggest the use of smartphones and wearable devices to passively collect quantified behavioral data from patients by utilizing digital biomarkers of mental disorder symptoms. Various digital biomarkers of MDD symptoms have already been detected, and apps for collecting this longitudinal behavioral data have already been developed. This quantified data can be used to determine a patient's diagnosis and personalized treatment, and thereby minimize the diagnosis rate of comorbidities. As there is a wide spectrum of human behavior, such a fluidic and personalized approach is essential.

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. We analyzed the response patterns of patients to three antidepressant medications in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, and employed state-of-the-art machine learning (ML) tools to generate a predictive algorithm. To validate our results, we assessed the algorithm's capacity to predict individualized antidepressant responses on a separate set of 530 patients in STAR*D, consisting of 271 patients in a validation set and 259 patients in the final test set. This assessment yielded an average balanced accuracy rate of 72.3% (SD 8.1) and 70.1% (SD 6.8) across the different medications in the validation and test set, respectively (p < 0.01 for all models). To further validate our design scheme, we obtained data from the Pharmacogenomic Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) of patients treated with citalopram, and applied the algorithm's citalopram model. This external validation yielded highly similar results for STAR*D and PGRN-AMPS test sets, with a balanced accuracy of 60.5% and 61.3%, respectively (both p's < 0.01). These findings support the feasibility of using ML algorithms applied to large datasets with genetic, clinical, and demographic features to improve accuracy in antidepressant prescription.

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. METHODS: Knockdown or overexpression of BDNF was induced in hippocampus or VTA of rats by microinjection of specific lentiviral vectors. The effects of these manipulations on antidepressant outcomes of ECT were evaluated by the forced swim test and by sucrose preference measurements, and BDNF expression levels were assessed in other reward-related brain regions. RESULTS: Here, we show that whereas ECT increased hippocampal BDNF expression, induction of hippocampal BDNF knockdown did not block its antidepressant-like effect. Importantly, we found that ECT caused a robust reduction in VTA BDNF levels. Moreover, VTA BDNF knockdown alone was sufficient to induce an antidepressant-like effect, and VTA BDNF overexpression blocked the antidepressant-like effect of ECT. CONCLUSIONS: While neuroplastic alterations, as expressed by changes in BDNF expression within different brain regions, are induced by ECT, the antidepressant-like effect of ECT in an animal model depends on reduction of VTA BDNF expression but not on the elevation of hippocampal BDNF expression.

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. However, the potential role of hippocampal BDNF in behavioral resilience to chronic stress and in the regulation of the HPA axis has not been evaluated. In the present study, Sprague Dawley rats were subjected to 4 weeks of chronic mild stress (CMS) to induce depressive-like behaviors after lentiviral vectors were used to induce localized BDNF overexpression or knockdown in the hippocampus. The behavioral outcome was measured during 3 weeks after the CMS procedure, then plasma samples were taken for measurements of corticosterone levels, and finally hippocampal tissue was taken for BDNF measurements. We found that hippocampal BDNF expression plays a critical role in resilience to chronic stress and that reduction of hippocampal BDNF expression in young, but not adult, rats induces prolonged elevations in corticosterone secretion. The present study describes a mechanism for individual differences in responses to chronic stress and implicates hippocampal BDNF in the development of neural circuits that control adequate stress adaptations.