An electroencephalographic signature predicts antidepressant response in major depression

Integrating molecular, histopathological, neuroimaging and clinical neuroscience data with NeuroPM-box

Understanding and treating heterogeneous brain disorders requires specialized techniques spanning genetics, proteomics, and neuroimaging. Designed to meet this need, NeuroPM-box is a user-friendly, open-access, multi-tool cross-platform software capable of characterizing multiscale and multifactorial neuropathological mechanisms. Using advanced analytical modeling for molecular, histopathological, brain-imaging and/or clinical evaluations, this framework has multiple applications, validated here with synthetic (N > 2900), in-vivo (N = 911) and post-mortem (N = 736) neurodegenerative data, and including the ability to characterize: (i) the series of sequential states (genetic, histopathological, imaging or clinical alterations) covering decades of disease progression, (ii) concurrent intra-brain spreading of pathological factors (e.g., amyloid, tau and alpha-synuclein proteins), (iii) synergistic interactions between multiple biological factors (e.g., toxic tau effects on brain atrophy), and (iv) biologically-defined patient stratification based on disease heterogeneity and/or therapeutic needs. This freely available toolbox ( neuropm-lab.com/neuropm-box.html ) could contribute significantly to a better understanding of complex brain processes and accelerating the implementation of Precision Medicine in Neurology.

Deep learning applied to electroencephalogram data in mental disorders: A systematic review

In recent medical research, tremendous progress has been made in the application of deep learning (DL) techniques. This article systematically reviews how DL techniques have been applied to electroencephalogram (EEG) data for diagnostic and predictive purposes in conducting research on mental disorders. EEG-studies on psychiatric diseases based on the ICD-10 or DSM-V classification that used either convolutional neural networks (CNNs) or long -short-term-memory (LSTMs) networks for classification were searched and examined for the quality of the information they contained in three domains: clinical, EEG-data processing, and deep learning. Although we found that the description of EEG acquisition and pre-processing was sufficient in most of the studies, we found, that many of them lacked a systematic characterization of clinical features. Furthermore, many studies used misguided model selection procedures or flawed testing. It is recommended that the study of psychiatric disorders using DL in the future must improve the quality of clinical data and follow state of the art model selection and testing procedures so as to achieve a higher research standard and head toward a clinical significance.

Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions

Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.

Major Depressive Disorder in Older Patients as an Inflammatory Disorder: Implications for the Pharmacological Management of Geriatric Depression

Depression is a common and highly disabling condition in older adults. It is a heterogenous disorder and there is emerging evidence of a link between inflammation and depression in older patients, with a possible inflammatory subtype of depression. Persistent low-level inflammation, from several sources including psychological distress and chronic disease, can disrupt monoaminergic and glutaminergic systems to create dysfunctional brain networks. Despite the evidence for the role of inflammation in depression, there is insufficient evidence to recommend use of any putative anti-inflammatory agent in the treatment of depression in older adults at this stage. Further characterisation of markers of inflammation and stratification of participants with elevated rates of inflammatory markers in treatment trials is needed.

Identification of psychiatric disorder subtypes from functional connectivity patterns in resting-state electroencephalography

The understanding and treatment of psychiatric disorders, which are known to be neurobiologically and clinically heterogeneous, could benefit from the data-driven identification of disease subtypes. Here, we report the identification of two clinically relevant subtypes of post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) on the basis of robust and distinct functional connectivity patterns, prominently within the frontoparietal control network and the default mode network. We identified the disease subtypes by analysing, via unsupervised and supervised machine learning, the power-envelope-based connectivity of signals reconstructed from high-density resting-state electroencephalography in four datasets of patients with PTSD and MDD, and show that the subtypes are transferable across independent datasets recorded under different conditions. The subtype whose functional connectivity differed most from those of healthy controls was less responsive to psychotherapy treatment for PTSD and failed to respond to an antidepressant medication for MDD. By contrast, both subtypes responded equally well to two different forms of repetitive transcranial magnetic stimulation therapy for MDD. Our data-driven approach may constitute a generalizable solution for connectome-based diagnosis.

EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks:A review

Mental disorders represent critical public health challenges as they are leading contributors to the global burden of disease and intensely influence social and financial welfare of individuals. The present comprehensive review concentrate on the two mental disorders: Major depressive Disorder (MDD) and Bipolar Disorder (BD) with noteworthy publications during the last ten years. There is a big need nowadays for phenotypic characterization of psychiatric disorders with biomarkers. Electroencephalography (EEG) signals could offer a rich signature for MDD and BD and then they could improve understanding of pathophysiological mechanisms underling these mental disorders. In this review, we focus on the literature works adopting neural networks fed by EEG signals. Among those studies using EEG and neural networks, we have discussed a variety of EEG based protocols, biomarkers and public datasets for depression and bipolar disorder detection. We conclude with a discussion and valuable recommendations that will help to improve the reliability of developed models and for more accurate and more deterministic computational intelligence based systems in psychiatry. This review will prove to be a structured and valuable initial point for the researchers working on depression and bipolar disorders recognition by using EEG signals.

The intervention, the patient and the illness - Personalizing non-invasive brain stimulation in psychiatry

Current hypotheses on the therapeutic action of non-invasive brain stimulation (NIBS) in psychiatric disorders build on the abundant data from neuroimaging studies. This makes NIBS a very promising tool for developing personalized interventions within a precision medicine framework. NIBS methods fundamentally vary in their neurophysiological properties. They comprise repetitive transcranial magnetic stimulation (rTMS) and its variants (e.g. theta burst stimulation - TBS) as well as different types of transcranial electrical stimulation (tES), with the largest body of evidence for transcranial direct current stimulation (tDCS). In the last two decades, significant conceptual progress has been made in terms of NIBS targets, i.e. from single brain regions to neural circuits and to functional connectivity as well as their states, recently leading to brain state modulating closed-loop approaches. Regarding structural and functional brain anatomy, NIBS meets an individually unique constellation, which varies across normal and pathophysiological states. Thus, individual constitutions and signatures of disorders may be indistinguishable at a given time point, but can theoretically be parsed along course- and treatment-related trajectories. We address precision interventions on three levels: 1) the NIBS intervention, 2) the constitutional factors of a single patient, and 3) the phenotypes and pathophysiology of illness. With examples from research on depressive disorders, we propose solutions and discuss future perspectives, e.g. individual MRI-based electrical field strength as a proxy for NIBS dosage, and also symptoms, their clusters, or biotypes instead of disorder focused NIBS. In conclusion, we propose interleaved research on these three levels along a general track of reverse and forward translation including both clinically directed research in preclinical model systems, and biomarker guided controlled clinical trials. Besides driving the development of safe and efficacious interventions, this framework could also deepen our understanding of psychiatric disorders at their neurophysiological underpinnings.

Gray matter structures associated with neuroticism: A meta-analysis of whole-brain voxel-based morphometry studies

Neuroticism is major higher-order personality trait and has been robustly associated with mental and physical health outcomes. Although a growing body of studies have identified neurostructural markers of neuroticism, the results remained highly inconsistent. To characterize robust associations between neuroticism and variations in gray matter (GM) structures, the present meta-analysis investigated the concurrence across voxel-based morphometry (VBM) studies using the anisotropic effect size signed differential mapping (AES-SDM). A total of 13 studies comprising 2,278 healthy subjects (1,275 females, 29.20 ± 14.17 years old) were included. Our analysis revealed that neuroticism was consistently associated with the GM structure of a cluster spanning the bilateral dorsal anterior cingulate cortex and extending to the adjacent medial prefrontal cortex (dACC/mPFC). Meta-regression analyses indicated that the neuroticism-GM associations were not confounded by age and gender. Overall, our study is the first whole-brain meta-analysis exploring the brain structural correlates of neuroticism, and the findings may have implications for the intervention of high-neuroticism individuals, who are at risk of mental disorders, by targeting the dACC/mPFC.

[Comments on Relationships with Artificial Emotional Intelligence - from "Here and Now" to "There and Then"]

The structure of relationships in the past, the present and the future is shaped by the idea of humanism. Based on this construct, the article illuminates various aspects and configurations of humanism on a timeline from "here and now" to "there and then". The current reality of care goes hand in hand with an emotional alienation of relationships. Advances in technology and reductionist neurobiological ideas can make it difficult to look at a person's mental illness as a whole. Any (communication) technology that has been developed in the past or will be developed in the future will sooner or later find its way into psychiatry and psychotherapy and change relationships. Transhumanism runs the risk that people will become alienated from each other and their species. Neural networks are algorithms that work regardless of the hardware used, be it based on organic carbon units such as humans or non-organic silicon units such as the computer/cyborg. There will be different ways to achieve super intelligence. Intelligence is a "must" and consciousness is a "can". If there is a change from a homocentric to a data-centered world view and the power of humans is transferred to the algorithms, humans could lose their economic value and the humanistic goals of health and happiness would be lost.

Enhancing EEG-Based Classification of Depression Patients Using Spatial Information

BACKGROUND

Depression has become a leading mental disorder worldwide. Evidence has shown that subjects with depression exhibit different spatial responses in neurophysiological signals from the healthy controls when they are exposed to positive and negative stimuli.

METHODS

We proposed an effective electroencephalogram-based detection method for depression classification using spatial information. A face-in-the-crowd task, including positive and negative emotional facial expressions, was presented to 30 participants, including 16 depression patients and 14 healthy controls. Differential entropy and the genetic algorithm were used for feature extraction and selection, and a support vector machine was used for classification. A task-related common spatial pattern (TCSP) was proposed to enhance the spatial differences before the feature extraction.

RESULTS AND DISCUSSION

We achieved a leave-one-subject-out cross-validation classification result of 84% and 85.7% for positive and negative stimuli, respectively, using TCSP, which is statistically significantly higher than 81.7% and 83.2%, respectively, acquired without the TCSP (p < 0.05). We also evaluated the classification performance using individual frequency bands and found that the contribution of the gamma band was predominant. In addition, we evaluated different classifiers, including k-nearest neighbor and logistic regression, which showed similar trends in the improvement of classification by employing TCSP.

CONCLUSION

The results show that our proposed method, employing spatial information, significantly improves the accuracy of classifying depression patients.

Multi-View Multi-Scale Optimization of Feature Representation for EEG Classification Improvement

Effectively extracting common space pattern (CSP) features from motor imagery (MI) EEG signals is often highly dependent on the filter band selection. At the same time, optimizing the EEG channel combinations is another key issue that substantially affects the SMR feature representations. Although numerous algorithms have been developed to find channels that record important characteristics of MI, most of them select channels in a cumbersome way with low computational efficiency, thereby limiting the practicality of MI-based BCI systems. In this study, we propose the multi-scale optimization (MSO) of spatial patterns, optimizing filter bands over multiple channel sets within CSPs to further improve the performance of MI-based BCI. Specifically, several channel subsets are first heuristically predefined, and then raw EEG data specific to each of these subsets bandpass-filtered at the overlap between a set of filter bands. Further, instead of solving learning problems for each channel subset independently, we propose a multi-view learning based sparse optimization to jointly extract robust CSP features with L_2,1 -norm regularization, aiming to capture the shared salient information across multiple related spatial patterns for enhanced classification performance. A support vector machine (SVM) classifier is then trained on these optimized EEG features for accurate recognition of MI tasks. Experimental results on three public EEG datasets validate the effectiveness of MSO compared to several other competing methods and their variants. These superior experimental results demonstrate that the proposed MSO method has promising potential in MI-based BCIs.

Precision Psychiatry: Biomarker-Guided Tailored Therapy for Effective Treatment and Prevention in Major Depression

Depression contributes greatly to global disability and is a leading cause of suicide. It has multiple etiologies and therefore response to treatment can vary significantly. By applying the concepts of personalized medicine, precision psychiatry attempts to optimize psychiatric patient care by better predicting which individuals will develop an illness, by giving a more accurate biologically based diagnosis, and by utilizing more effective treatments based on an individual's biological characteristics (biomarkers). In this chapter, we discuss the basic principles underlying the role of biomarkers in psychiatric pathology and then explore multiple biomarkers that are specific to depression. These include endophenotypes, gene variants/polymorphisms, epigenetic factors such as methylation, biochemical measures, circadian rhythm dysregulation, and neuroimaging findings. We also examine the role of early childhood trauma in the development of, and treatment response to, depression. In addition, we review how new developments in technology may play a greater role in the determination of new biomarkers for depression.

Special Report on the Impact of the COVID-19 Pandemic on Clinical EEG and Research and Consensus Recommendations for the Safe Use of EEG

INTRODUCTION

The global COVID-19 pandemic has affected the economy, daily life, and mental/physical health. The latter includes the use of electroencephalography (EEG) in clinical practice and research. We report a survey of the impact of COVID-19 on the use of clinical EEG in practice and research in several countries, and the recommendations of an international panel of experts for the safe application of EEG during and after this pandemic.

METHODS

Fifteen clinicians from 8 different countries and 25 researchers from 13 different countries reported the impact of COVID-19 on their EEG activities, the procedures implemented in response to the COVID-19 pandemic, and precautions planned or already implemented during the reopening of EEG activities.

RESULTS

Of the 15 clinical centers responding, 11 reported a total stoppage of all EEG activities, while 4 reduced the number of tests per day. In research settings, all 25 laboratories reported a complete stoppage of activity, with 7 laboratories reopening to some extent since initial closure. In both settings, recommended precautions for restarting or continuing EEG recording included strict hygienic rules, social distance, and assessment for infection symptoms among staff and patients/participants.

CONCLUSIONS

The COVID-19 pandemic interfered with the use of EEG recordings in clinical practice and even more in clinical research. We suggest updated best practices to allow safe EEG recordings in both research and clinical settings. The continued use of EEG is important in those with psychiatric diseases, particularly in times of social alarm such as the COVID-19 pandemic.

Precision clinical trials: a framework for getting to precision medicine for neurobehavioural disorders

The goal of precision medicine (individually tailored treatments) is not being achieved for neurobehavioural conditions such as psychiatric disorders. Traditional randomized clinical trial methods are insufficient for advancing precision medicine because of the dynamic complexity of these conditions. We present a pragmatic solution: the precision clinical trial framework, encompassing methods for individually tailored treatments. This framework includes the following: (1) treatment-targeted enrichment, which involves measuring patients' response after a brief bout of an intervention, and then randomizing patients to a full course of treatment, using the acute response to predict long-term outcomes; (2) adaptive treatments, which involve adjusting treatment parameters during the trial to individually optimize the treatment; and (3) precise measurement, which involves measuring predictor and outcome variables with high accuracy and reliability using techniques such as ecological momentary assessment. This review summarizes precision clinical trials and provides a research agenda, including new biomarkers such as precision neuroimaging, transcranial magnetic stimulation-electroencephalogram digital phenotyping and advances in statistical and machine-learning models. Validation of these approaches - and then widespread incorporation of the precision clinical trial framework - could help achieve the vision of precision medicine for neurobehavioural conditions.

Brain Connectivity Changes after Osteopathic Manipulative Treatment: A Randomized Manual Placebo-Controlled Trial

The effects of osteopathic manipulative treatment (OMT) on functional brain connectivity in healthy adults is missing in the literature. To make up for this lack, we applied advanced network analysis methods to analyze resting state functional magnetic resonance imaging (fMRI) data, after OMT and Placebo treatment (P) in 30 healthy asymptomatic young participants randomized into OMT and placebo groups (OMTg; Pg). fMRI brain activity measures, performed before (T0), immediately after (T1) and three days after (T2) OMT or P were used for inferring treatment effects on brain circuit functional organization. Repeated measures ANOVA and post-hoc analysis demonstrated that Right Precentral Gyrus (F (2, 32) = 5.995, p < 0.005) was more influential over the information flow immediately after the OMT, while decreased betweenness centrality in Left Caudate (F (2, 32) = 6.496, p < 0.005) was observable three days after. Clustering coefficient showed a distinct time-point and group effect. At T1, reduced neighborhood connectivity was observed after OMT in the Left Amygdala (L-Amyg) (F (2, 32) = 7.269, p < 0.005) and Left Middle Temporal Gyrus (F (2, 32) = 6.452, p < 0.005), whereas at T2 the L-Amyg and Vermis-III (F (2, 32) = 6.772, p < 0.005) increased functional interactions. Data demonstrated functional connectivity re-arrangement after OMT.

Translational opportunities for circuit-based social neuroscience: advancing 21st century psychiatry

The recent advancements of social behavioral neuroscience are unprecedented. Through manipulations targeting neural circuits, complex behaviors can be switched on and off, social bonds can be induced, and false memories can be 'incepted.' Psychiatry, however, remains tethered to concepts and techniques developed over half a century ago, including purely behavioral definitions of psychopathology and chronic, brain-wide pharmacological interventions. Drawing on recent animal and human research, we outline a circuit-level approach to the social brain and highlight studies demonstrating the translational potential of this approach. We conclude by suggesting ways both clinical practice and translational research can apply circuit-level neuroscientific knowledge to advance psychiatry, including adopting neuroscience-based nomenclature, stratifying patients into diagnostic subgroups based on neurobiological phenotypes, and pharmacologically enhancing psychotherapy.

Brain-derived neurotrophic factor Val66Met polymorphism affects cortical thickness of rostral anterior cingulate in patients with major depressive disorder

OBJECTIVE

The neuro-anatomical substrates of major depressive disorder (MDD) remain poorly understood. Brain-derived neurotrophic factor (BDNF) gene polymorphism (Val66Met/rs6265) is associated with neuro-plasticity and development. In the present study, we explore the influence of BDNF gene polymorphism on cortical thickness in nonelderly, first episode, drug-naive patients with MDD.

METHODS

Two hundred and sixteen participants (105 MDD patients and 111 healthy controls) were divided into subgroups based on the BDNF genotype. High-resolution MRI was obtained in all participants. A relationship of BDNF Val66Met gene polymorphism and cortical thickness was investigated.

RESULTS

The significant main effect of diagnosis was identified in the left rostal anterior cingulate (rACC), right inferior temporal and right lateral orbitofrontal (lOFC). The main effect of the genotype was observed in the left posterior cingulate cortex. The diagnosis-by-genotype interaction effect was found located in the left rACC. MDD patients who were Met-carriers exhibited thinner cortical thickness in the left rACC than healthy controls Met-carriers. Neither the symptom severity nor the illness duration was correlated significantly with cortical thickness.

CONCLUSION

Our findings suggested that the BDNF gene polymorphism was associated with cortical thickness alterations of the left rACC in MDD patients, and genotype that carries Met may serve as a vulnerability factor in MDD regarding the cortical thickness loss in the left rACC. This finding can be considered as a supportive evidence for the neurotrophic factor hypothesis of depression.

Peripheral vascular endothelial growth factor changes after transcranial magnetic stimulation in treatment-resistant depression

OBJECTIVES

To determine if vascular endothelial growth factor (VEGF) changes with transcranial magnetic stimulation (TMS) in treatment-resistant major depressive disorder (MDD).

METHODS

Serum from a naturalistic population of 15 patients with MDD was collected at baseline and after standard TMS treatment. VEGF concentration was determined via ELISA. Inventory of Depressive Symptomatology Self Report and Patient Health Questionnaire were used as a measure of depression symptom severity, clinical response and remission. Mann-Whitney U and Kendall's Tau Correlation were used for continuous variables.

RESULTS

VEGF increased from pre- to post-TMS (+30.3%) in remitters whereas VEGF decreased in non-remitters (-9.87%) (P < 0.05). This same pattern was observed when comparing mean %change in VEGF between responders (+14.7%) and non-responders (-14.9%) (P = 0.054). Correlation was present between change in VEGF concentration (baseline to post) and change in Inventory of Depressive Symptomatology-Self Report at Tx30 (r = -0.371, P < 0.054), reflecting greater increases in VEGF linked to greater improvement in depressive symptoms following the standard 6-week course of TMS.

CONCLUSION

Patients with a successful treatment with TMS had significantly greater increase in VEGF from baseline to after treatment compared to non-responders/non-remitters and a larger increase in VEGF was associated with greater improvement in depressive symptoms after TMS. This is the first report examining VEGF levels in depressed patients receiving TMS. This study provides correlative data supporting further investigation into VEGF's role as an important mediator in the processes underpinning TMS' antidepressant effects and as a potential biomarker of clinical outcomes.

Circuit-Based Biomarkers for Mood and Anxiety Disorders

Mood and anxiety disorders are complex heterogeneous syndromes that manifest in dysfunctions across multiple brain regions, cell types, and circuits. Biomarkers using brain-wide activity patterns in humans have proven useful in distinguishing between disorder subtypes and identifying effective treatments. In order to improve biomarker identification, it is crucial to understand the basic circuitry underpinning brain-wide activity patterns. Leveraging a large repertoire of techniques, animal studies have examined roles of specific cell types and circuits in driving maladaptive behavior. Recent advances in multiregion recording techniques, data-driven analysis approaches, and machine-learning-based behavioral analysis tools can further push the boundary of animal studies and bridge the gap with human studies, to assess how brain-wide activity patterns encode and drive emotional behavior. Together, these efforts will allow identifying more precise biomarkers to enhance diagnosis and treatment.

Exploring EEG microstates for affective computing: decoding valence and arousal experiences during video watching

Investigating the electroencephalography (EEG) correlates of human emotional experiences has attracted increasing interest in the field of affective computing. Substantial progress has been made during the past decades, mainly by using EEG features extracted from localized brain activities. The present study explored a brain network-based feature defined by EEG microstates for a possible representation of emotional experiences. A publicly available and widely used benchmarking EEG dataset called DEAP was used, in which 32 participants watched 40 one-minute music videos with their 32channel EEG recorded. Four quasi-stable prototypical microstates were obtained, and their temporal parameters were extracted as features. In random forest regression, the microstate features showed better performances for decoding valence (model fitting mean squared error (MSE) = 3.85±0.28 and 4.07 ± 0.30, respectively, p = 0.022) and comparable performances for decoding arousal (MSE = 3.30±0.30 and 3.41 ±0.31, respectively, p = 0.169), as compared to conventional spectral power features. As microstate features describe neural activities from a global spatiotemporal dynamical perspective, our findings demonstrate a possible new mechanism for understanding human emotion and provide a promising type of EEG feature for affective computing.

Psychological mechanisms and functions of 5-HT and SSRIs in potential therapeutic change: Lessons from the serotonergic modulation of action selection, learning, affect, and social cognition

Uncertainty regarding which psychological mechanisms are fundamental in mediating SSRI treatment outcomes and wide-ranging variability in their efficacy has raised more questions than it has solved. Since subjective mood states are an abstract scientific construct, only available through self-report in humans, and likely involving input from multiple top-down and bottom-up signals, it has been difficult to model at what level SSRIs interact with this process. Converging translational evidence indicates a role for serotonin in modulating context-dependent parameters of action selection, affect, and social cognition; and concurrently supporting learning mechanisms, which promote adaptability and behavioural flexibility. We examine the theoretical basis, ecological validity, and interaction of these constructs and how they may or may not exert a clinical benefit. Specifically, we bridge crucial gaps between disparate lines of research, particularly findings from animal models and human clinical trials, which often seem to present irreconcilable differences. In determining how SSRIs exert their effects, our approach examines the endogenous functions of 5-HT neurons, how 5-HT manipulations affect behaviour in different contexts, and how their therapeutic effects may be exerted in humans - which may illuminate issues of translational models, hierarchical mechanisms, idiographic variables, and social cognition.

The State of Our Understanding of the Pathophysiology and Optimal Treatment of Depression: Glass Half Full or Half Empty?

Major depressive disorder is a remarkably common and often severe psychiatric disorder associated with high levels of morbidity and mortality. Patients with major depression are prone to several comorbid psychiatric conditions, including posttraumatic stress disorder, anxiety disorders, obsessive-compulsive disorder, and substance use disorders, and medical conditions, including cardiovascular disease, diabetes, stroke, cancer, which, coupled with the risk of suicide, result in a shortened life expectancy. The goal of this review is to provide an overview of our current understanding of major depression, from pathophysiology to treatment. In spite of decades of research, relatively little is known about its pathogenesis, other than that risk is largely defined by a combination of ill-defined genetic and environmental factors. Although we know that female sex, a history of childhood maltreatment, and family history as well as more recent stressors are risk factors, precisely how these environmental influences interact with genetic vulnerability remains obscure. In recent years, considerable advances have been made in beginning to understand the genetic substrates that underlie disease vulnerability, and the interaction of genes, early-life adversity, and the epigenome in influencing gene expression is now being intensively studied. The role of inflammation and other immune system dysfunction in the pathogenesis of major depression is also being intensively investigated. Brain imaging studies have provided a firmer understanding of the circuitry involved in major depression, providing potential new therapeutic targets. Despite a broad armamentarium for major depression, including antidepressants, evidence-based psychotherapies, nonpharmacological somatic treatments, and a host of augmentation strategies, a sizable percentage of patients remain nonresponsive or poorly responsive to available treatments. Investigational agents with novel mechanisms of action are under active study. Personalized medicine in psychiatry provides the hope of escape from the current standard trial-and-error approach to treatment, moving to a more refined method that augurs a new era for patients and clinicians alike.