Endocrinological Treatment Targets for Depressive Disorder.

Depressive disorder exhibits heterogeneity in clinical presentation, progression, and treatment outcomes. While conventional antidepressants based on the monoamine hypothesis benefit many patients, a significant proportion remains unresponsive or fails to fully recover. An individualized integrative treatment approach, considering diverse pathophysiologies, holds promise for these individuals. The endocrine system, governing physiological regulation and organ homeostasis, plays a pivotal role in central nervous system functions. Dysregulations in endocrine system are major cause of depressive disorder due to other medical conditions. Subtle endocrine abnormalities, such as subclinical hypothyroidism, are associated with depression. Conversely, depressive disorder correlates with endocrine-related biomarkers. Fluctuations in sex hormone levels related to female reproduction, elevate depression risk in susceptible subjects. Consequently, extensive research has explored treatment strategies involving the endocrine system. Treatment guidelines recommend tri-iodothyronine augmentation for resistant depression, while allopregnanolone analogs have gained approval for postpartum depression, with ongoing investigations for broader depressive disorders. This book chapter will introduce the relationship between the endocrine system and depressive disorders, presenting clinical findings on neuroendocrinological treatments for depression.

Application of Transcranial Direct and Alternating Current Stimulation (tDCS and tACS) on Major Depressive Disorder.

The exploration of brain stimulation methods offers a promising avenue to overcome the shortcomings of traditional drug therapies and psychological treatments for major depressive disorder (MDD). Over the past years, there has been an increasing focus on transcranial electrical stimulation (tES), notably for its ease of use and potentially fewer side effects. This chapter delves into the use of transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), which are key components of tES, in managing depression. It begins by introducing tDCS and tACS, summarizing their action mechanisms. Following this introduction, the chapter provides an in-depth analysis of existing meta-analyses, systematic reviews, clinical studies, and case reports that have applied tES in MDD treatment. It also considers the role of tES in personalized medicine by looking at specific patient groups and evaluating research on possible biomarkers that could predict how patients with MDD respond to tES therapy.

Electroconvulsive Therapy (ECT) in Major Depression: Oldies but Goodies.

Electroconvulsive therapy is one of the useful treatment methods for symptom improvement and remission in patients with treatment-resistant depression. Considering the various clinical characteristics of patients experiencing depression, key indicators are extracted from structural brain magnetic resonance imaging, functional brain magnetic resonance imaging, and electroencephalography (EEG) data taken before treatment, and applied as explanatory variables in machine learning and network analysis. Studies that attempt to make reliable predictions about the degree of response to electroconvulsive treatment and the possibility of remission in patients with treatment-resistant depression are continuously being published. In addition, studies are being conducted to identify the correlation with clinical improvement by taking structural-functional brain magnetic resonance imaging after electroconvulsive therapy in depressed patients. By reviewing and integrating the results of the latest studies on the above matters, we aim to present the usefulness of electroconvulsive therapy for improving the personalized prognosis of patients with treatment-resistant depression.

The Potential and Challenges of Virtual Reality as an Adjunctive Treatment of Depression Patients.

Virtual reality (VR) is a promising supplemental or alternative approach for treating depression. Focusing on the unique affordances of VR, such as immersive therapeutic settings and uniform treatment delivery, this study explores both the current applications and future potential of VR technology in treating and assessing depression. VR provides users with an "immersive" and "presence" experience through multisensory stimulation. VR is an emerging paradigm in healthcare, particularly in psychiatric treatment, and presents compelling possibilities for its role in therapeutic interventions by facilitating realistic and controlled environments for both clinicians and patients. VR technology offers promising advancements in augmenting traditional cognitive-behavioral therapy (CBT) methods for treating depression, with applications in psychoeducation, behavioral activation, and cognitive restructuring. Various VR assessment techniques including biomarkers and machine learning have been discussed in this study. Although the field remains experimental, the immersive nature of VR holds the potential for more personalized and accurate therapeutic interventions and assessments. Further research is required for definitive conclusions.

The Importance of Psychotherapy for Treatment-Resistant Depression.

Many clinicians choose psychoanalytic psychotherapy or supportive psychotherapy as the primary method of treating depression with or without antidepressant medications. Despite new antidepressants, 20% or more patients showed inadequate responses to the medications, and remained in chronic courses, known as "treatment-resistant depression (TRD)."In this chapter, we described (1) the reasons for psychotherapy in treating TRD from the perspectives of the hazard of polypharmacy, resistance, and neural mechanisms. (2) Next, we focused on the importance of assessment with two clinical vignettes and the original modality of psychoanalysis, psychoanalytic psychotherapy, and supportive psychotherapy in brief. (3) Finally, we described specific considerations in undertaking psychotherapy for TRD patients in terms of transference, countertransference, and resistance. In addition, the efficacy of psychoanalytic psychotherapy in childhood, adolescent, and late-life depression has been depicted in this paper.

Telepsychiatry in the Treatment of Major Depressive Disorders.

This chapter explores the transformative role of telepsychiatry in managing major depressive disorders (MDD). Traversing geographical barriers and reducing stigma, this innovative branch of telemedicine leverages digital platforms to deliver effective psychiatric care. We investigate the evolution of telepsychiatry, examining its diverse interventions such as videoconferencing-based psychotherapy, medication management, and mobile applications. While offering significant advantages like increased accessibility, cost-effectiveness, and improved patient engagement, challenges in telepsychiatry include technological barriers, privacy concerns, ethical and legal considerations, and digital literacy gaps. Looking forward, emerging technologies like virtual reality, artificial intelligence, and precision medicine hold immense potential to personalize and enhance treatment effectiveness. Recognizing its limitations and advocating for equitable access, this chapter underscores telepsychiatry's power to revolutionize MDD treatment, making quality mental healthcare a reality for all.

The New Emerging Treatment Choice for Major Depressive Disorders: Digital Therapeutics.

The chapter provides an in-depth analysis of digital therapeutics (DTx) as a revolutionary approach to managing major depressive disorder (MDD). It discusses the evolution and definition of DTx, their application across various medical fields, regulatory considerations, and their benefits and limitations. This chapter extensively covers DTx for MDD, including smartphone applications, virtual reality interventions, cognitive-behavioral therapy (CBT) platforms, artificial intelligence (AI) and chatbot therapies, biofeedback, wearable technologies, and serious games. It evaluates the effectiveness of these digital interventions, comparing them with traditional treatments and examining patient perspectives, compliance, and engagement. The integration of DTx into clinical practice is also explored, along with the challenges and barriers to their adoption, such as technological limitations, data privacy concerns, ethical considerations, reimbursement issues, and the need for improved digital literacy. This chapter concludes by looking at the future direction of DTx in mental healthcare, emphasizing the need for personalized treatment plans, integration with emerging modalities, and the expansion of access to these innovative solutions globally.

Precision Medicine in Depression: The Role of Proteomics and Metabolomics in Personalized Treatment Approaches.

Depression, or major depressive disorder (MDD), is a widespread mental health condition marked by enduring feelings of sorrow and loss of interest. Treatment of depression frequently combines psychotherapy, medication, and lifestyle modifications. However, the occurrence of treatment resistance in certain individuals makes it difficult for physicians to effectively manage this disorder, calling for the implementation of alternative therapeutic strategies. Recently, precision medicine has gained increased attention in the field of mental health, paving the way for more personalized and effective therapeutic interventions in depression. Also known as personalized medicine, this approach relies on genetic composition, molecular profiles, and environmental variables to customize therapies to individual patients. In particular, precision medicine has offered novel viewpoints on depression through two specific domains: proteomics and metabolomics. On one hand, proteomics is the thorough study of proteins in a biological system, while metabolomics focuses on analyzing the complete set of metabolites in a living being. In the past few years, progress in research has led to the identification of numerous depression-related biomarkers using proteomics and metabolomics techniques, allowing for early identification, precise diagnosis, and improved clinical outcome. However, despite significant progress in these techniques, further efforts are required for advancing precision medicine in the diagnosis and treatment of depression. The overarching goal of this chapter is to provide the current state of knowledge regarding the use of proteomics and metabolomics in identifying biomarkers related to depression. It also highlights the potential of proteomics and metabolomics in elucidating the intricate processes underlying depression, opening the door for tailored therapies that could eventually enhance clinical outcome in depressed patients. This chapter finally discusses the main challenges in the use of proteomics and metabolomics and discusses potential future research directions.

Neuron-Microglia Crosstalk in Neuropsychiatric Disorders.

Numerous studies have investigated the causes and mechanisms of psychiatric disorders through postmortem examination of patients with a history of a schizophrenia, mood disorder, or neurocognitive disorder. In addition, the search for specific mechanism-based treatments for psychiatric disorders has been intensified through the use of transgenic animal models involving specific genes tightly associated with psychiatric disorders. As a result, many studies with patients or animal models have reported a close association of neuroglia with major psychiatric disorders. Recently, research has focused on the associations between microglia and major psychiatric disorders and on the role of the immune response and abnormal microglia in the onset and symptoms of psychiatric disorders, in particular. Postmortem studies of brain tissue and animal models recapitulating human mental disorders have also confirmed association between psychiatric disorders and quantitative, structural, or functional abnormalities of neuron-microglia crosstalk. This review aims to describe the relationships between microglia and major psychiatric disorders and to specifically examine studies of gene expression and function of microglia in depression, schizophrenia, and Alzheimer's disease.

Microbiota-Gut-Brain Axis: Pathophysiological Mechanism in Neuropsychiatric Disorders.

Gut microbiota influence human behavior. The immunological, metabolic, and endocrine systems are involved in bidirectional communication between the gut and the brain, which is regulated by microbes through the microbiota-derived neurochemicals and metabolites. Gut microbiota have certain effects on neurodevelopment and maturation of immunity. However, gut dysbiosis can lead to neuropsychiatric disorders. Animal research and clinical case-control studies have demonstrated that gut dysbiosis has an adverse effect on human behavior through a variety of mechanisms. Recent meta-analysis on clinical studies confirmed gut dysbiosis in several major neuropsychiatric disorders. Microbiota-targeted intervention has recently been in the spotlight and meta-analyses have confirmed its effectiveness. In this chapter, we summarize the evidence for the interactions between microbiota and brain-gut network, as well as the potential pathophysiological mechanisms involved.

Early Life Stress, Neuroinflammation, and Psychiatric Illness of Adulthood.

Stress exposure during early stages of life elevates the risk of developing psychopathologies and psychiatric illness in later life. The brain and immune system are not completely developed by birth and therefore continue develop after birth; this post birth development is influenced by several psychosocial factors; hence, early life stress (ELS) exposure can alter brain structural development and function. A growing number of experimental animal and observational human studies have investigated the link between ELS exposure and increased risk of psychopathology through alternations in the immune system, by evaluating inflammation biomarkers. Recent studies, including brain imaging, have also shed light on the mechanisms by which both the innate and adaptive immune systems interact with neural circuits and neurotransmitters, which affect psychopathology. Herein, we discuss the link between the experience of stress in early life and lifelong alterations in the immune system, which subsequently lead to the development of various psychiatric illnesses.

Molecular Imaging of Neuroinflammation in Alzheimer's Disease and Mild Cognitive Impairment.

Alzheimer's disease (AD) is the most prevalent neurocognitive disorder. Due to the ineffectiveness of treatments targeting the amyloid cascade, molecular biomarkers for neuroinflammation are attracting attention with increasing knowledge about the role of neuroinflammation in the pathogenesis of AD. This chapter will explore the results of studies using molecular imaging for diagnosing AD and mild cognitive impairment (MCI). Because it is critical to interpreting the data to understand which substances are targeted in molecular imaging, this chapter will discuss the two most significant targets, microglia and astrocytes, as well as the best-known radioligands for each. Then, neuroimaging results with PET neuroinflammation imaging will be reviewed for AD and MCI. Although a growing body of evidence has suggested that these molecular imaging biomarkers for neuroinflammation may have a role in the diagnosis of AD and MCI, the findings are inconsistent or cross-sectional, which indicates that it is difficult to apply the contents in practice due to the need for additional study. In particular, because the results of multiple interventions targeting neuroinflammation were inconclusive, molecular imaging markers for neuroinflammation can be used in combination with conventional markers to select appropriate patients for early intervention for neuroinflammation rather than as a single marker.

Microbiota-Gut-Brain Axis in Major Depression: A New Therapeutic Approach.

Major depression is impacted by the disruption of gut microbiota. Defects in gut microbiota can lead to microbiota-gut-brain axis dysfunction and increased vulnerability to major depression. While traditional chemotherapeutic approaches, such as antidepressant use, produce an overall partial therapeutic effect on depression, the gut microbiome has emerged as an effective target for better therapeutic outcomes. Recent representative studies on the microbiota hypothesis to explore the association between gut pathophysiology and major depression have indicated that restoring gut microbiota and microbiota-gut-brain axis could alleviate depression. We reviewed studies that supported the gut microbiota hypothesis to better understand the pathophysiology of depression; we also explored reports suggesting that gut microbiota restoration is an effective approach for improving depression. These findings indicate that gut microbiota and microbiota-gut-brain axis are appropriate new therapeutic targets for major depression.

Understanding the Connection Between the Gut-Brain Axis and Stress/Anxiety Disorders.

PURPOSE OF REVIEW: We review the association of the microbiota-gut-brain axis and anxiety disorder or stress. RECENT FINDING: The microbiota-gut-brain axis mechanism encompasses a bidirectional relationship between the brain and gastrointestinal organs. Dysregulation of the microbiota-gut-brain axis has been actively revealed in the context of various psychiatric diseases such as neurodevelopmental disorders, schizophrenia, anxiety disorders, and depression. We suggest that onset of anxiety disorders may be correlated with activation of a microbiota-gut-brain mechanism involving the immune system, neurotransmitters, and the hormonal system. By applying a microbiota-gut-brain axis mechanism, the possibility of using gastrointestinal system drugs such as probiotics and antibiotics as treatments for anxiety disorders is a possibility. Although modification of the microbiota-gut-brain axis mechanism has yet to be adopted clinically, it is expected that novel strategies employing this mechanism will be developed and deployed as new treatments not only for anxiety disorders, but also other psychiatric diseases.

Phenotype Network and Brain Structural Covariance Network of Major Depression.

Phenotype networks enable clinicians to elucidate the patterns of coexistence and interactions among the clinical symptoms, negative cognitive styles , neurocognitive performance, and environmental factors in major depressive disorder (MDD). Results of phenotype network approach could be used in finding the target symptoms as these are tightly connected or associated with many other phenomena within the phenotype network of MDD specifically when comorbid psychiatric disorder(s) is/are present. Further, by comparing the differential patterns of phenotype networks before and after the treatment, changing or enduring patterns of associations among the clinical phenomena in MDD have been deciphered.Brain structural covariance networks describe the inter-regional co-varying patterns of brain morphologies, and overlapping findings have been reported between the brain structural covariance network and coordinated trajectories of brain development and maturation. Intra-individual brain structural covariance illustrates the degrees of similarities among the different brain regions for how much the values of brain morphological features are deviated from those of healthy controls. Inter-individual brain structural covariance reflects the degrees of concordance among the different brain regions for the inter-individual distribution of brain morphologic values. Estimation of the graph metrics for these brain structural covariance networks uncovers the organizational profile of brain morphological variations in the whole brain and the regional distribution of brain hubs.

The Application of a Machine Learning-Based Brain Magnetic Resonance Imaging Approach in Major Depression.

Major depressive disorder (MDD) shows a high prevalence and is associated with increased disability. While traditional studies aimed to investigate global characteristic neurobiological substrates of MDD, machine learning-based approaches focus on individual people rather than a group. Therefore, machine learning has been increasingly conducted and applied to clinical practice. Several previous neuroimaging studies used machine learning for stratifying MDD patients from healthy controls as well as in differentially diagnosing MDD apart from other psychiatric disorders. Also, machine learning has been used to predict treatment response using magnetic resonance imaging (MRI) results. Despite the recent accomplishments of machine learning-based MRI studies, small sample sizes and the heterogeneity of the depression group limit the generalizability of a machine learning-based predictive model. Future neuroimaging studies should integrate various materials such as genetic, peripheral, and clinical phenotypes for more accurate predictability of diagnosis and treatment response.

Challenges and Strategies for Current Classifications of Depressive Disorders: Proposal for Future Diagnostic Standards.

The Diagnostic and Statistical Manual of Mental Disorder, Fourth Edition (DSM-IV) was revised based on a combination of a categorical and a dimensional approach such that in the DSM, Fifth Edition (DSM-5), depressive disorders have been separated as a distinctive disease entity from bipolar disorders, consistent with the deconstruction of Kraepelinian dualism. Additionally, the diagnostic thresholds of depressive disorders may be reduced due to the addition of "hopelessness" to the subjective descriptors of depressed mood and the removal of the "bereavement exclusion." Manic/hypomanic, psychotic, and anxious symptoms in major depressive disorder (MDD) and other depressive disorders are described using the transdiagnostic specifiers of "with mixed features," "with psychotic features," and "with anxious distress," respectively. Additionally, due to the polythetic and operational characteristics of the DSM-5 diagnostic criteria, the heterogeneity of MDD is inevitable. Thus, 227 different symptom combinations fulfill the DSM-5 diagnostic criteria for MDD. This heterogeneity of MDD is criticized in view of the Wittgensteinian analogy of language game. Depression subtypes determined by disturbances in monoamine levels and the severity of the disease have been identified in the literature. According to a review of the Gottesman and Gould criteria, neuroticism, morning cortisol, cortisol awakening response, asymmetry in frontal cortical activity on electroencephalography (EEG), and probabilistic reward learning, among other variables, are evidenced as endophenotypes for depressive disorders. Network analysis has been proposed as a potential method to compliment the limitations of current diagnostic criteria and to explore the pathways between depressive symptoms, as well as to identify novel and interesting relationships between depressive symptoms. Based on the literature on network analysis in this field, no differences in the centrality index of the DSM and non-DSM symptoms were repeatedly present among patients with MDD. Furthermore, MDD and other depressive syndromes include two of the Research Domain Criteria (RDoC), including the Loss construct within the Negative Valence Systems domains and various Reward constructs within the Positive Valence Systems domain.

Development of Neuroimaging-Based Biomarkers in Major Depression.

A leading goal in the field of biological psychiatry for depression is to find a promising diagnostic biomarker and selection of specific psychiatric treatment mode that is most likely to benefit patients with depression. Recent neuroimaging studies have characterized the pathophysiology of major depressive disorder (MDD) with functional and structural alterations in the neural circuitry involved in emotion or reward processing. Particularly, structural and functional magnetic resonance imaging (MRI) studies have reported that the brain structures deeply involved in emotion regulation or reward processing including the amygdala, prefrontal cortex (PFC), anterior cingulate cortex (ACC), ventral striatum, and hippocampus are key regions that provide useful information about diagnosis and treatment outcome prediction in MDD. For example, it has been consistently reported that elevated activity of the ACC is associated with better antidepressant response in patients with MDD. This chapter will discuss a growing body of evidence that suggests that diagnosis or prediction of outcome for specific treatment can be assisted by a neuroimaging-based biomarker in MDD.

The Role of Neurotrophic Factors in Pathophysiology of Major Depressive Disorder.

According to the neurotrophic hypothesis of major depressive disorder (MDD), impairment in growth factor signaling might be associated with the pathology of this illness. Current evidence demonstrates that impaired neuroplasticity induced by alterations of neurotrophic growth factors and related signaling pathways may be underlying to the pathophysiology of MDD. Brain-derived neurotrophic factor (BDNF) is the most studied neurotrophic factor involved in the neurobiology of MDD. Nevertheless, developing evidence has implicated other neurotrophic factors, including neurotrophin-3 (NT-3), neurotrophin-4 (NT-4), nerve growth factor (NGF), vascular endothelial growth factor (VEGF), insulin-like growth factor (IGF), glial cell-derived neurotrophic factor (GDNF), and fibroblast growth factor (FGF) in the MDD pathophysiology. Here, we summarize the current literature on the involvement of neurotrophic factors and related signaling pathways in the pathophysiology of MDD.

Associations between Melatonin, Neuroinflammation, and Brain Alterations in Depression.

Pro-inflammatory systemic conditions that can cause neuroinflammation and subsequent alterations in brain regions involved in emotional regulation have been suggested as an underlying mechanism for the pathophysiology of major depressive disorder (MDD). A prominent feature of MDD is disruption of circadian rhythms, of which melatonin is considered a key moderator, and alterations in the melatonin system have been implicated in MDD. Melatonin is involved in immune system regulation and has been shown to possess anti-inflammatory properties in inflammatory conditions, through both immunological and non-immunological actions. Melatonin has been suggested as a highly cytoprotective and neuroprotective substance and shown to stimulate all stages of neuroplasticity in animal models. The ability of melatonin to suppress inflammatory responses through immunological and non-immunological actions, thus influencing neuroinflammation and neurotoxicity, along with subsequent alterations in brain regions that are implicated in depression, can be demonstrated by the antidepressant-like effects of melatonin. Further studies that investigate the associations between melatonin, immune markers, and alterations in the brain structure and function in patients with depression could identify potential MDD biomarkers.