Depression has been associated with reduced expression of brain-derived neurotrophic factor (BDNF) in the hippocampus. In addition, animal studies suggest an association between reduced hippocampal neurogenesis and depressive-like behavior. These associations were predominantly established based on responses to antidepressant drugs and alterations in BDNF levels and neurogenesis in depressive patients or animal models for depressive behavior. Nevertheless, there is no direct evidence that the actual reduction of the BDNF protein in specific brain sites can induce depressive-like behaviors or affect neurogenesis in vivo. Using BDNF knockdown by RNA interference and lentiviral vectors injected into specific subregions of the hippocampus we show that a reduction in BDNF expression in the dentate gyrus, but not the CA3, reduces neurogenesis and affects behaviors associated with depression. Moreover, we show that BDNF has a critical function in neuronal differentiation, but not proliferation in vivo. Finally, we found that a specific BDNF knockdown in the ventral subiculum induces anhedonic-like behavior. These findings provide substantial support for the neurotrophic hypothesis of depression and specify anatomical and neurochemical targets for potential antidepressant interventions. Moreover, the specific effect of BDNF reduction on neuronal differentiation has broader implications for the study of neurodevelopment and neurodegenerative diseases.

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.

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 (bothp’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.

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.

Skills development in infants: a possible role for widespread neurogenesis?