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.

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.

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.

The shared molecular mechanisms underlying aging of the brain, major depressive disorder, and Alzheimer's disease: The role of circadian rhythm disturbances.

An association with circadian clock function and pathophysiology of aging, major depressive disorder (MDD), and Alzheimer's disease (AD) is well established and has been proposed as a factor in the development of these diseases. Depression and changes in circadian rhythm have been increasingly suggested as the two primary overlapping and interpenetrating changes that occur with aging. The relationship between AD and depression in late life is not completely understood and probably is complex. Patients with major depression or AD suffer from disturbed sleep/wake cycles and altered rhythms in daily activities. Although classical monoaminergic hypotheses are traditionally proposed to explain the pathophysiology of MDD, several clinical and preclinical studies have reported a strong association between circadian rhythm and mood regulation. In addition, a large body of evidence supports an association between disruption of circadian rhythm and AD. Some clock genes are dysregulated in rodent models of depression. If aging, AD, and MDD share a common biological basis in pathophysiology, common therapeutic tools could be investigated for their prevention and treatment. Nitro-oxidative stress (NOS), for example, plays a fundamental role in aging, as well as in the pathogenesis of AD and MDD and is associated with circadian clock disturbances. Thus, development of therapeutic possibilities with these NOS-related conditions is advisable. This review describes recent findings that link disrupted circadian clocks to aging, MDD, and AD and summarizes the experimental evidence that supports connections between the circadian clock and molecular pathologic factors as shared common pathophysiological mechanisms underlying aging, AD, and MDD.

World Federation of Societies of Biological Psychiatry (WFSBP) guidelines for treatment of anxiety, obsessive-compulsive and posttraumatic stress disorders - Version 3. Part I: Anxiety disorders.

AIM: This is the third version of the guideline of the World Federation of Societies of Biological Psychiatry (WFSBP) Task Force for the Pharmacological Treatment of Anxiety, Obsessive-Compulsive and Posttraumatic Stress Disorders (published in 2002, revised in 2008). METHOD: A consensus panel of 33 international experts representing 22 countries developed recommendations based on efficacy and acceptability of available treatments. In total, 1007 RCTs for the treatment of these disorders in adults, adolescents, and children with medications, psychotherapy and other non-pharmacological interventions were evaluated, applying the same rigorous methods that are standard for the assessment of medications. RESULT: This paper, Part I, contains recommendations for the treatment of panic disorder/agoraphobia (PDA), generalised anxiety disorder (GAD), social anxiety disorder (SAD), specific phobias, mixed anxiety disorders in children and adolescents, separation anxiety and selective mutism. Selective serotonin reuptake inhibitors (SSRI) and serotonin-norepinephrine reuptake inhibitors (SNRIs) are first-line medications. Cognitive behavioural therapy (CBT) is the first-line psychotherapy for anxiety disorders. The expert panel also made recommendations for patients not responding to standard treatments and recommendations against interventions with insufficient evidence. CONCLUSION: It is the goal of this initiative to provide treatment guidance for these disorders that has validity throughout the world.

World Federation of Societies of Biological Psychiatry (WFSBP) guidelines for treatment of anxiety, obsessive-compulsive and posttraumatic stress disorders - Version 3. Part II: OCD and PTSD.

AIM: This is the third version of the guideline of the World Federation of Societies of Biological Psychiatry (WFSBP) Task Force for the Pharmacological Treatment of Anxiety, Obsessive-Compulsive and Posttraumatic Stress Disorders which was published in 2002 and revised in 2008. METHOD: A consensus panel of 34 international experts representing 22 countries developed recommendations based on efficacy and acceptability of the treatments. In this version, not only medications but also psychotherapies and other non-pharmacological interventions were evaluated, applying the same rigorous methods that are standard for the assessment of medication treatments. RESULT: The present paper (Part II) contains recommendations based on published randomised controlled trials (RCTs) for the treatment of OCD (n = 291) and PTSD (n = 234) in children, adolescents, and adults. The accompanying paper (Part I) contains the recommendations for the treatment of anxiety disorders.For OCD, first-line treatments are selective serotonin reuptake inhibitors (SSRIs) and cognitive behavioural therapy (CBT). Internet-CBT was also superior to active controls. Several second-line medications are available, including clomipramine. For treatment-resistant cases, several options are available, including augmentation of SSRI treatment with antipsychotics and other drugs.Other non-pharmacological treatments, including repetitive transcranial magnetic stimulation (rTMS), deep brain stimulation (DBS) and others were also evaluated.For PTSD, SSRIs and the SNRI venlafaxine are first-line treatments. CBT is the psychotherapy modality with the best body of evidence. For treatment-unresponsive patients, augmentation of SSRI treatment with antipsychotics may be an option. CONCLUSION: OCD and PTSD can be effectively treated with CBT and medications.

Possible oxytocin-related biomarkers in anxiety and mood disorders.

Anxiety and mood disorders are prevalent, disabling, and frequently difficult to treat. Such disorders are often comorbid and share similar characteristics. For more accurate diagnosis and improved treatment, a deeper understanding of the pathophysiology of anxiety and mood disorders is important. Oxytocin, a neuropeptide synthesized in the hypothalamus, affects human psychology and behaviors such as social and affiliative behaviors, fear and emotion processing, and stress regulation. Thus, oxytocin is believed to exert anxiolytic and antidepressant-like effects. This review article provides an overview of clinical studies on relationships between the oxytocin system and anxiety and mood disorders, focusing on oxytocin-related biomarker findings. Biomarkers used in such studies include central and peripheral oxytocin levels, analysis of oxytocin-related genes, and expression levels of oxytocin and oxytocin receptor genes in postmortem brains. Although a growing number of studies support the presence of oxytocinergic effects on anxiety and mood disorders, study results are heterogeneous and inconclusive. Moderating factors such as the characteristics of study populations, including sex, age, context, early life adversity, and attachment styles in patient cohorts, might affect the heterogeneity of the study results. Limitations in existing research such as small sample sizes, large dependence on peripheral sources of oxytocin, and inconsistent results between immunoassay methods complicate the interpretation of existing findings.

Changes in Brain Electrical Activity According to Post-traumatic Stress Symptoms in Survivors of the Sewol Ferry Disaster: A 1-year Longitudinal Study.

OBJECTIVE: The pathology of post-traumatic stress disorder (PTSD) is associated with changes in brain structure and function, especially in the amygdala, medial prefrontal cortex, hippocampus, and insula. Survivors of tragic accidents often experience psychological stress and develop post-traumatic stress symptoms (PTSS), regardless of the diagnosis of PTSD. This study aimed to evaluate electroencephalographic changes according to PTSS in victims of a single traumatic event. METHODS: This study enrolled 60 survivors of the Sewol ferry disaster that occurred in 2014 from Danwon High School and collected electroencephalographic data through 19 channels twice for each person in 2014 and 2015 (mean 451.88 [standard deviation 25.77] days of follow-up). PTSS was assessed using the PTSD Checklist-Civilian Version (PCL-C) and the participants were divided into two groups according to the differences in PCL-C scores between 2014 and 2015. Electroencephalographic data were converted to three-dimensional data to perform low-resolution electrical tomographic analysis. RESULTS: Significant electroencephalographic changes over time were observed. The group of participants with worsened PCL-C score showed an increased change of delta slow waves in Brodmann areas 13 and 44, with the largest difference in the insula region, compared to those with improved PCL-C scores. CONCLUSION: Our findings suggests that the electrophysiological changes in the insula are associated with PTSS changes.

Graph theory approach for the structural-functional brain connectome of depression.

To decipher the organizational styles of neural underpinning in major depressive disorder (MDD), the current article reviewed recent neuroimaging studies (published during 2015-2020) that applied graph theory approach to the diffusion tensor imaging data or functional brain activation data acquired during task-free resting state. The global network organization of resting-state functional connectivity network in MDD were diverse according to the onset age and medication status. Intra-modular functional connections were weaker in MDD compared to healthy controls (HC) for default mode and limbic networks. Weaker local graph metrics of default mode, frontoparietal, and salience network components in MDD compared to HC were also found. On the contrary, brain regions comprising the limbic, sensorimotor, and subcortical networks showed higher local graph metrics in MDD compared to HC. For the brain white matter-based structural connectivity network, the global network organization was comparable to HC in adult MDD but was attenuated in late-life depression. Local graph metrics of limbic, salience, default-mode, subcortical, insular, and frontoparietal network components in structural connectome were affected from the severity of depressive symptoms, burden of perceived stress, and treatment effects. Collectively, the current review illustrated changed global network organization of structural and functional brain connectomes in MDD compared to HC and were varied according to the onset age and medication status. Intra-modular functional connectivity within the default mode and limbic networks were weaker in MDD compared to HC. Local graph metrics of structural connectome for MDD reflected severity of depressive symptom and perceived stress, and were also changed after treatments. Further studies that explore the graph metrics-based neural correlates of clinical features, cognitive styles, treatment response and prognosis in MDD are required.

The Relationship between Plasma Erythropoietin Levels and Symptoms of Attention Deficit Hyperactivity Disorder.

OBJECTIVE: There are animal models associating dopamine dysfunction with behavioral impairments that model attention deficit hyperactivity disorder (ADHD). Erythropoietin (EPO) has trophic effects on dopaminergic neurons. The aim of this study was to examine the EPO plasma levels and determine whether there was any correlation between plasma EPO levels and clinical characteristics of ADHD. METHODS: Plasma EPO levels were measured in 78 drug-naïve children with ADHD and in 81 healthy children. The severity of ADHD symptoms was determined by scores on the Korean ADHD Rating Scale (K-ARS) in ADHD children and healthy controls. RESULTS: The difference between median plasma EPO levels in ADHD children and in healthy controls was not statistically significant. Adjusting for age and sex, a linear regression analysis showed that inattention score was significantly higher in the second highest tertile of plasma EPO compared to those in the lowest tertile. Hyperactivity-impulsivity score was significantly higher in the highest tertile of plasma EPO compared to those in the lowest tertile. Moreover, total K-ARS scores were significantly higher in the second highest tertile of plasma EPO compared to those in the lowest tertile. CONCLUSION: These findings suggest that plasma EPO levels were related to some ADHD symptoms, which could be used in the monitoring of the disorder.

Gut Microbiota and Bipolar Disorder: An Overview on a Novel Biomarker for Diagnosis and Treatment.

The gut microbiota is the set of microorganisms that colonize the gastrointestinal tract of living creatures, establishing a bidirectional symbiotic relationship that is essential for maintaining homeostasis, for their growth and digestive processes. Growing evidence supports its involvement in the intercommunication system between the gut and the brain, so that it is called the gut-brain-microbiota axis. It is involved in the regulation of the functions of the Central Nervous System (CNS), behavior, mood and anxiety and, therefore, its implication in the pathogenesis of neuropsychiatric disorders. In this paper, we focused on the possible correlations between the gut microbiota and Bipolar Disorder (BD), in order to determine its role in the pathogenesis and in the clinical management of BD. Current literature supports a possible relationship between the compositional alterations of the intestinal microbiota and BD. Moreover, due to its impact on psychopharmacological treatment absorption, by acting on the composition of the microbiota beneficial effects can be obtained on BD symptoms. Finally, we discussed the potential of correcting gut microbiota alteration as a novel augmentation strategy in BD. Future studies are necessary to better clarify the relevance of gut microbiota alterations as state and disease biomarkers of BD.

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.

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 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.

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.