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Alzheimer’s disease is the most common form of dementia. Due to the lack of effective interventions, early and accurate diagnosis for new interventions are emphasized. However, significant neuronal loss and neuropathological lesions can damage the brain substantially before diagnosis. With our growing knowledge of the role of neuroinflammation in the pathogenesis of Alzheimer’s disease, inflammatory biomarkers are attracting increasing interest in the context of diagnosis. This review is focused on the use of inflammatory biomarkers detected through neuroimaging, cerebrospinal fluid, and peripheral blood for diagnosing Alzheimer’s disease, and also suggests clinical implications. This review includes the following biomarkers: neuroimaging, various ligands binding to the translocator protein (TSPO); cerebrospinal fluid, soluble triggering receptor expressed on myeloid cells (sTREM2), human cartilage glycoprotein-39 (YKL-40), and monocyte chemoattractant protein 1 (MCP-1), and various biomarkers in peripheral blood. Although accumulating evidence has suggested the potential role of these inflammatory biomarkers in diagnosing AD, there are limitations to their use. However, combining these biomarkers with conventional diagnostic clues such as genotype and amyloid pathology may improve the stratification and selection of patients for targeted early interventions.

To identify the reliable and consistent grey matter volume (GMV) abnormalities associated with major depressive disorder (MDD), we excluded the influence of confounding clinical characteristics, comorbidities and brain degeneration on brain morphological abnormalities by inclusion of non-comorbid and non-geriatric drug-naïve MDD individuals experiencing first episode depressive.The PubMed, Scopus, Web of Science, Science Direct and Google scholar databases were searched for papers published in English up to April 2020.A total of 21 voxel based morphometric (VBM) studies comparing 845 individuals in the first depressive episode and medication-naïve with 940 healthy control subjects were included. The results showed a grey matter volumes reductions in the orbitofrontal cortex (OFC), prefrontal cortex (PFC), frontal and temporal gyri, temporal pole, insular lobe, thalamus, basal ganglia, cerebellum, hippocampus, cingulate cortex, and amygdala. In addition, increased grey matter volumes in the postcentral gyrus, superior frontal gyrus, insula, basal ganglia, thalamus, amygdala, cuneus, and precuneus differentiated the first depressive episode in medication-naïve individuals from healthy subjects.The present systematic review provided additional support for the involvement of grey matter structural abnormalities in limbic-cortical circuits as possibly specific structural abnormalities in the early stage of MDD.Key pointsDistinct brain regions in MDD patients might be associated with the early stages of illness, and thus it is critical to study the causal relationship between brain structures and the onset of the disease to improve the evaluation in clinic.Grey matter alterations in the fronto-limbic networks in the first episode, medication-naïve MDD might suggest that these abnormalities may play an important role in the neuropathophysiology of MDD at its onset.First episode, medically naïve depressive patients show grey matter volume alterations in brain regions mainly associated with emotion regulation including parietal-temporal regions, PFC, insular lobe, thalamus, basal ganglia, cerebellum and limbic structures that may be specific changes in early stage of MDD.Genotype-diagnosis interaction effects on brain morphology in the cortico-limbic-striatal circuits, including the PFC, amygdala, hippocampus and striatum that might be implicated in the dysfunctional regulation of emotion in first-episode MDD patients.Future longitudinal and prospective studies should be conducted to identify the core structural brain changes in people at-risk for MDD and explore the association of their brain volumes with symptom onset.

Background: A research on mood disorder pathophysiology has hypothesized abnormalities in glutamatergic neurotransmission, by suggesting further investigation on glutamatergic N-methyl-Daspartate (NMDA) receptor modulators in treating Major Depressive Disorder (MDD). Esketamine (ESK), an NMDA receptor antagonist able to modulate glutamatergic neurotransmission has been recently developed as an intranasal formulation for treatment-resistant depression (TRD) and for rapid reduction of depressive symptomatology, including suicidal ideation in MDD patients at imminent risk for suicide. Objective: The present study aims at investigating recent clinical findings on research on the role of the glutamatergic system and ESK in treating suicidal depression in MDD and TRD. Methods: A systematic review was here carried out on PubMed/Medline, Scopus and the database on U.S. N.I.H. Clinical Trials (https://clinicaltrials.gov) and the European Medical Agency (EMA) (https://clinicaltrialsregister.eu) from inception until October 2019. Results: Intravenous infusion of ESK is reported to elicit rapid-acting and sustained antidepressant activity in refractory patients with MDD and TRD. In phase II studies, intranasal ESK demonstrated a rapid onset and a persistent efficacy in patients with TRD as well as in MDD patients at imminent risk for suicide. However, some data discrepancies have emerged in phase III studies. Conclusion: The U.S. Food and Drug Administration (FDA) granted fast track and Breakthrough Therapy Designation to Janssen Pharmaceuticals®, Inc. for intranasal ESK in 2013 for treatment-resistant depression (TRD) and in 2016 for the treatment of MDD with an imminent risk of suicide. However, further studies should be implemented to investigate the long-term efficacy and safety of intranasal ESK.

Obsessive-compulsive disorder is often associated with schizophrenia and may represent a significant challenge in the treatment as this comorbidity may not respond properly to antipsychotic medication and usually require a pharmacological and psychotherapeutic add-on. In the present case report, we present the case of a 26-year-old male blue-collar subject who developed obsessive-compulsive disorder after a year of complete remission of schizophrenia symptoms under paliperidone long-acting injection that rapidly resolved after low-dosage cariprazine add-on. No adverse effects were reported due to cariprazine- paliperidone long-acting injection combination.

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.

Depressive symptoms have been found to be associated with decreased hand-grip strength (HGS) and low socioeconomic status (SES) in older adults. We aimed to investigate the potential moderating effect of SES on the association between HGS and depressive symptoms and the potential mediating effect of HGS on the association between SES and depressive symptoms using a nationally representative sample of older adults.Data from 3169 community-dwelling adults aged 60 years or older were acquired from the Korea National Health and Nutrition Examination Survey conducted in 2014 and 2016. HGS was measured using a digital hand-grip dynamometer. Depressive symptoms were evaluated using the 9-item version of the Patient Health Questionnaire-9 (PHQ-9). SES was assessed using equivalent monthly household income and education level.Older adults in the lowest tertile of HGS measures were more likely to have experienced depressive symptoms compared to those in the highest tertile (odds ratio = 1.95, 95% confidence interval = 1.25-2.74). A significant moderating effect of household income level was observed on the association between HGS and PHQ-9 score (P = 0.014). Older adults with a low income had a stronger inverse correlation between HGS and PHQ-9 score compared to those with a high income (low income: beta = -0.162, P 0.001; high income: beta = -0.119, P = 0.036). HGS partially mediated the association between low income and depressive symptoms.Our findings indicate that there may be a stronger relationship between low HGS and depressive symptoms in socioeconomically deprived older people. Further research on muscle strength and income level in older adults is required regarding depression risk assessment.

The heterogeneity of post-traumatic stress disorder (PTSD) symptoms indicates that multiple neurobiological mechanisms underlie the pathophysiology of the condition. However, no generally accepted PTSD biomarkers in clinical practice currently exist. The sequential responses to recurrent and chronic stress by the hypothalamic-pituitary-adrenal (HPA) axis and the autonomic nervous system (ANS) system are considered to play a significant role in the onset and progression of PTSD. Decreased activity of the HPA axis and parasympathetic nervous system, along with increased activity of the sympathetic nervous system, have been observed in PTSD, which may lead to increased levels of proinflammatory cytokines. Such heightened activity of the immune system may cause alterations in the structure and function of brain regions-for example, the amygdala, hippocampus, medial prefrontal cortex, anterior cingulate cortex, and insula-through changes in levels of serotonin and kynurenine pathway metabolites, and direct neurotoxic effects of cytokines. Although chronic inflammation-induced alterations in brain regions critical in controlling emotional behavior and fear regulation may represent a strong candidate biomarker of PTSD, future studies are necessary to further elucidate inflammation-associated neural biomarkers of PTSD. Continued research on therapeutic methods that involve the normalization of the HPA axis, ANS, and immune system is expected to contribute to the development of novel ways to treat PTSD.

The etiology of depression is characterized by the interplay of genetic and environmental factors and brain structural alteration. Childhood adversity is a major contributing factor in the development of depression. Interactions between childhood adversity and candidate genes for depression could affect brain morphology via the modulation of neurotrophic factors, serotonergic neurotransmission, or the hypothalamus-pituitary-adrenal (HPA) axis, and this pathway may explain the subsequent onset of depression. Childhood adversity is associated with structural changes in the hippocampus, amygdala, anterior cingulate cortex (ACC), and prefrontal cortex (PFC), as well as white matter tracts such as the corpus callosum, cingulum, and uncinate fasciculus. Childhood adversity showed an interaction with the brain-derived neurotrophic factor (BDNF) gene Val66Met polymorphism, serotonin transporter-linked promoter region (5-HTTLPR), and FK506 binding protein 51 (FKBP5) gene rs1360780 in brain morphologic changes in patients with depression and in a non-clinical population. Individuals with the Met allele of BDNF Val66Met and a history of childhood adversity had reduced volume in the hippocampus and its subfields, amygdala, and PFC and thinner rostral ACC in a study of depressed patients and healthy controls. The S allele of 5-HTTLPR combined with exposure to childhood adversity or a poorer parenting environment was associated with a smaller hippocampal volume and subsequent onset of depression. The FKBP5 gene rs160780 had a significant interaction with childhood adversity in the white matter integrity of brain regions involved in emotion processing. This review identified that imaging genetic studies on childhood adversity may deepen our understanding on the neurobiological background of depression by scrutinizing complicated pathways of genetic factors, early psychosocial environments, and the accompanying morphologic changes in emotion-processing neural circuitry.

Posttraumatic stress disorder (PTSD) is prevalent, disabling, and frequently becomes chronic. Despite this, only two selective serotonergic reuptake inhibitors have been approved to date for its treatment by the United States Food and Drug Administration, and treatment results are often disappointing, with a remission rate of

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.

Background We investigated the interactive effects of depressive symptoms and chronic diseases on health care utilization among elderly people and explored the potential moderating effect of gender and the mediating effect of self-rated health (SRH) on the association between depressive symptoms and health care utilization. Method We analyzed data from 5223 people aged 60 years or older living in the community from the Korea Welfare Panel Study in 2015. Depressive symptoms were measured using an 11-item version of the Center for Epidemiologic Studies Depression (CES-D-11) Scale and morbidity within 28 disease categories was assessed. Health care utilization was evaluated as the number of outpatient visits (OV), number of hospitalizations (NH), and number of days spent in the hospital (DH) during past year. Hierarchical moderated regression analyses were applied to investigate the interactive effects. We also adopted the mediation analysis method by Hayes and Preacher. Results A significant interactive effect of CES-D-11 score and chronic disease on OV was found. A positive correlation between CES-D-11 score and OV was only observed in those with chronic disease. Gender had a moderating effect on the association of depression symptoms with OV, NH, and DH among elderly people with chronic disease. SRH had mediating effects on the association of CES-D-11 with OV, NH, and DH only among those with chronic disease. Limitations The severity or multimorbidity of chronic diseases, which could affect health care utilization among elderly were not considered. Conclusions We elucidated the complex aspects of the relationship between depressive symptoms and chronic disease and their interactive effects on health care utilization among elderly people, and identified important roles of gender and SRH.

The prediction of acute and maintenance lithium treatment response carries major clinical and neurobiological implications, warranting systematic review. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) compliant review searched major electronic databases from inception until December 2017 for studies documenting a clinical diagnosis of bipolar disorder (BD) made according to the mainstream diagnostic manuals and confirmed by a structured interview. Eligible studies allowed a quantitative comparison of endpoint vs baseline mean values of a given biomarker, regardless of the mood phase of patients with BD, and the disorder was assessed for severity using validated rating tool(s). Owing to the purposely applied stringent selection criteria, 16 acute and 12 maintenance studies could be included. The anticipated publication bias limited the chances of reportable generalizable findings, hindering a side-by-side comparison of different records across varying biomarkers and subsequent meta-analyses. The PRISMA approach was nonetheless preferred; it aimed at enhancing the homogeneity of the included results and minimizing the chances of “apples and oranges” with respect to the present research theme. The present critical review confirms the need for future research to specifically assess either pretreatment and/or posttreatment putative biomarkers of patients with BD and treated with lithium. © 2018 Elsevier B.V.

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 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.

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.

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.

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 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.

We review the association of the microbiota-gut-brain axis and anxiety disorder or stress.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.

It is hypothesized that major depression disorder (MDD) is associated with impaired neuronal plasticity, and that antidepressant treatments restore neuroplasticity. Brain-derived neurotrophic factor (BDNF) and erythropoietin (Epo) show neurotrophic and neuroprotective effects. We evaluated plasma Epo and BDNF levels in 50 MDD inpatients before treatment and in 50 healthy controls. The MDD inpatients consisted of 20 MDD patients without and 30 MDD patients with a recent suicide attempt. The plasma Epo level was significantly higher in nonsuicidal and suicidal MDD patients than in healthy controls (p ≤ 0.001), while the plasma BDNF level was significantly lower in suicidal MDD than in nonsuicidal MDD patients and healthy controls (p ≤ 0.001). When classifying study participants into low-Epo and high-Epo and low-BDNF and high-BDNF subgroups based on the cutoff of Epo or BDNF calculated using receiver operating characteristics (ROC) curve analysis, logistic regression analysis revealed that high-Epo and low-BDNF status correlated with a respective significant odds ratio of 7.367 (p = 0.015) and 33.123 (p ≤ 0.001) for suicidal MDD. In conclusion, plasma BDNF level was decreased in untreated MDD patients, which was presumed to be a dysfunctional effect of the onset of MDD. However, an increase in plasma Epo was observed in MDD in connection with a recent suicide attempt, indicating that this triggers hypoxic stress to induce a compensatory increase in Epo.

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.

Postpartum depression occurs in 10–15% of mothers. Brain-derived neurotrophic factor (BDNF) is a nerve growth factor that plays a role in neuroplasticity. We hypothesized that the concentration of BDNF is related to reproduction and childbirth, and that women with postpartum depression show alteration in BDNF level. A total of 104 pregnant women was selected as subjects, and 60 non-pregnant women were selected as normal controls. Symptoms of depression were evaluated in the pregnant study subjects using the diagnostic criteria outlined in the Edinburgh Postnatal Depression Scale (EPDS). The pregnant subjects were divided into three groups of perinatal non-depressed controls (n = 61), postpartum depression-recovery (n = 18), and postpartum depression (n = 25). The plasma concentration of BDNF was higher in the pregnant group than in non-pregnant controls and lower in the postpartum depression group at 6 weeks after delivery than in the perinatal non-depressed group. In the postpartum depression-recovery group, the BDNF concentration increased at 6 weeks after delivery compared to that at 24 weeks of gestation. This study found significant changes in plasma BDNF concentration in depressed pregnant women.

Stress-induced changes in the immune system, which lead to neuroinflammation and consequent brain alterations, have been suggested as possible neurobiological substrates of anxiety disorders, with previous literature predominantly focusing on panic disorder, agoraphobia, and generalized anxiety disorder, among the anxiety disorders. Anxiety disorders have frequently been associated with chronic stress, with chronically stressful situations being reported to precipitate the onset of anxiety disorders. Also, chronic stress has been reported to lead to hypothalamic–pituitary–adrenal axis and autonomic nervous system disruption, which may in turn induce systemic proinflammatory conditions. Preliminary evidence suggests anxiety disorders are also associated with increased inflammation. Systemic inflammation can access the brain, and enhance pro-inflammatory cytokine levels that have been shown to precipitate direct and indirect neurotoxic effects. Prefrontal and limbic structures are widely reported to be influenced by neuroinflammatory conditions. In concordance with these findings, various imaging studies on panic disorder, agoraphobia, and generalized anxiety disorder have reported alterations in structure, function, and connectivity of prefrontal and limbic structures. Further research is needed on the use of inflammatory markers and brain imaging in the early diagnosis of anxiety disorders, along with the possible efficacy of anti-inflammatory interventions on the prevention and treatment of anxiety disorders.

Alzheimer's disease (AD) is the most common form of dementia worldwide. β-amyloid peptide (Aβ) is currently assumed to be the main cause of synaptic dysfunction and cognitive impairments in AD, but the molecular signaling pathways underlying its neurotoxic consequences have not yet been completely explored. Additional investigations regarding these pathways will contribute to development of new therapeutic targets. In context, developing evidence suggest that Aβ decreases brain-derived neurotrophic factor (BDNF) mostly by lowering phosphorylated cyclic adenosine monophosphate (cAMP) response element binding protein (CREB) protein. In fact, it has been observed that brain or serum levels of BDNF appear to be beneficial markers for cognitive condition. In addition, the participation of transcription mediated by CREB has been widely analyzed in the memory process and AD development. Designing pharmacologic or genetic therapeutic approaches based on the targeting of CREB-BDNF signaling could be a promising treatment potential for AD. In this review, we summarize data demonstrating the role of CREB-BDNF signaling pathway in cognitive status and mediation of Aβ toxicity in AD. Finally, we also focus on the developing intervention methods for improvement of cognitive decline in AD based on targeting of CREB-BDNF pathway.

Ketamine was initially used as an anesthetic which could induce cognitive impairment and psychomimetic effects. In initial randomized controlled trials (RCTs) that mostly included a small sample size and were investigator-initiated, ketamine reportedly exerted antidepressant effects 1 to 2 h after a single intravenous infusion in patients with major depressive episodes, particularly treatment-resistant depression (TRD). Interest in ketamine was reported in systematic reviews and meta-analyses, however, many were primarily focused on the rapid onset of ketamine effects without equal attention to its safety and tolerability. Furthermore, several meta-analyses were based on many duplicated RCTs. The initial trends emphasized the clinical utility of ketamine as an antidepressant. The development of esketamine nasal spray by a pharmaceutical company led to an RCT with a large sample size and segmented therapeutic strategy, which provided results applicable to patients with TRD in the real-world clinical environment. However, possible effects of ketamine on cognitive function have not yet been investigated in RCTs. In numerous studies, chronic, recreational use of ketamine reportedly substantially impaired cognitive function in most domains. Although results of several human and animal studies indicated the therapeutic use of ketamine for treatment of depression did not induce cognitive impairment, this issue should be further investigated. Based on the current knowledge about ketamine, future antidepressants are expected to be glutamatergic drugs without ketamine-like adverse events (e.g., psychomimetic symptoms and cognitive impairment), but having only ketamine-like therapeutic properties (e.g., rapid antidepressants effects without time lag).

Under the partial influences of paradigm shift form category to dimension, the Diagnostic and Statistical Manual of Mental Disorder (DSM) was revised to the fifth edition (DSM-5); however, due to the lack of consistent biological makers and processes and the restricted availability of dimensional meta-structure, the revisions for the DSM-5 were based on a combination of categorical and dimensional approaches. Anxiety disorders were more clearly and consistently defined in the DSM-5 with the removal of obsessive compulsive, acute stress, and post-traumatic stress disorders. Differences between the childhood and adulthood categories of anxiety disorders were decreased, and overall, the symmetrical classification of anxiety subtypes was increased, since separation anxiety disorder and selective mutism were considered anxiety disorders, not neurodevelopmental disorders. Additionally, based on growing evidence, agoraphobia is distinct from panic disorder. Next, considering cultural syndromes including taijin kyofusho, khyal cap, trung gio attacks, and ataque de nervios, cultural influences are considered a significant factor for definitions and presentations of anxiety disorders. Controversies in the DSM-5 criteria for anxiety disorders are lowering the diagnostic thresholds of anxiety disorders and limiting the dichotomous view of anxiety and depression when defining generalized anxiety disorder. Further studies of alternative approaches to the restrictions of the DSM-5 criteria of anxiety disorders, including transdiagnostic specifiers and dimensional assessment tools, may be required.

Network-based approach for psychological phenotypes assumes the dynamical interactions among the psychiatric symptoms, psychological characteristics, and neurocognitive performances arise, as they coexist, propagate, and inhibit other components within the network of mental phenomena. For differential types of dataset from which the phenotype network is to be estimated, a Gaussian graphical model, an Ising model, a directed acyclic graph, or an intraindividual covariance network could be used. Accordingly, these network-based approaches for anxiety-related psychological phenomena have been helpful in quantitative and pictorial understanding of qualitative dynamics among the diverse psychological phenomena as well as mind-environment interactions. Brain structural covariance refers to the correlative patterns of diverse brain morphological features among differential brain regions comprising the brain, as calculated per participant or across the participants. These covarying patterns of brain morphology partly overlap with longitudinal patterns of brain cortical maturation and also with propagating pattern of brain morphological changes such as cortical thinning and brain volume reduction in patients diagnosed with neurologic or psychiatric disorders along the trajectory of disease progression. Previous studies that used the brain structural covariance network could show neural correlates of specific anxiety disorder such as panic disorder and also elucidate the neural underpinning of anxiety symptom severity in diverse psychiatric and neurologic disorder patients.

Although anxiety and depression have been considered as two distinct entities according to the diagnostic criteria, anxious depression (comorbid anxiety and depression) is relatively a common syndrome. According to the DSM-5 criteria, it uses "with anxious distress specifier" to define anxious depression in its MDD section. Anxious depression is known to have different neurobiological profiles compared to non-anxious depression. Several studies have revealed significant differences between anxious depression and non-anxious depression regarding the hypothalamic-pituitary-adrenal (HPA) axis function, structural and functional brain imaging findings, inflammation markers, etc. Patients with anxious depression were significantly more likely to be found in primary care setting and more likely to be associated with female gender, non-single, unemployed, less educated, and more severe depression. Previous reports also showed that patients with anxious depression had more frequent episodes of major depression and a higher risk of suicidal ideation and previous suicide attempts than those with non-anxious depression. Although anxious depression is known to be associated with poor treatment outcomes in several studies, recent researches have sought to find better treatment strategy to improve patients with anxious depression.

Oxytocin, a neuropeptide synthesized by the hypothalamus, plays a central role in human social behavior, social cognition, anxiety, mood, stress modulation, and fear learning and extinction. The relationships between oxytocin and psychiatric disorders including depression, anxiety, schizophrenia, and autism spectrum disorder have been extensively studied. In this chapter, we focus on the current knowledge about oxytocin and anxiety disorder. We discuss the anxiolytic effects of oxytocin in preclinical and clinical findings, possible related neurobehavioral mechanisms (social cognition, fear learning, and extinction), related neurotransmitter and neuroendocrine systems (hypothalamus-pituitary-adrenal axis, serotoninergic, and GABAergic systems), and studies regarding plasma levels of oxytocin, genetic and epigenetic findings, and effects of intranasal oxytocin in DSM-5 anxiety disorder (primarily social anxiety disorder and separation anxiety disorder) patients.

Chronic low-grade inflammation contributes to the pathophysiology of major depressive disorder (MDD). This study aimed to examine the association between serum levels of FAM19A5, a novel chemokine-like peptide that reflects reactive astrogliosis and inflammatory activation in the brain, and the neurodegenerative changes of MDD by investigating the correlation between serum FAM19A5 levels and cortical thickness changes in patients with MDD. We included 52 drug-naive patients with MDD and 60 healthy controls (HCs). Serum FAM19A5 levels were determined in peripheral venous blood samples using a sandwich enzyme-linked immunosorbent assay. All participants underwent T1-weighted structural magnetic resonance imaging. Serum FAM19A5 levels were greater in patients with MDD than in HCs. In the MDD group, there were significant inverse correlations between serum FAM19A5 levels and cortical thickness in the prefrontal regions (i.e., the left inferior and right medial superior frontal gyri), left posterior cingulate gyrus, right cuneus, and both precunei, which showed significantly reduced thickness in patients with MDD compared to HCs. However, no correlation between serum FAM19A5 level and cortical thickness was observed in the HC group. The results of our study indicate that serum FAM19A5 levels may reflect reactive astrogliosis and related neuroinflammation in MDD. Our findings also suggest that serum FAM19A5 may be a potential biomarker for the neurodegenerative changes of MDD.

Emotional labor is strongly correlated with negative consequences in psychological well-being and mental health status in workers. We investigated the associations of emotional labor with depressive mood and perceived usual stress level according to gender and its interactions with job autonomy in service and sales workers. The data from 2,055 service and sales workers from the Korea National Health and Nutrition Examination Surveys (KNHANES) conducted from 2007 to 2009 were analyzed. High emotional labor was associated with increased risk for depressive mood in female workers (adjusted odds ratio [aOR] = 2.19, 95%, confidence interval [CI] = 1.56–3.07). Emotional labor and job autonomy showed interactive effects on depressive mood in that high emotional labor was associated with depressive mood only in the presence of low job autonomy in male workers (aOR = 2.85, 95% CI = 1.13–7.17). A significant mediation pathway between high emotional demand and prevalence of depressive mood through higher stress level was observed in female workers. In conclusion, female workers had high vulnerability to depressive symptoms due to emotional labor, and high job autonomy can act as a buffer against the detrimental effect of emotional labor in male workers.

Cognitive behavioral therapy for insomnia (CBT-I) is the treatment of choice for insomnia; however, it is not widely used due to a lack of experienced therapists and its relatively high clinical cost. Recently, Internet and mobile CBT-I have been developed to replace face-to-face CBT-I, and research on this topic has been increasing. In addition, attempts have been made to use wearable devices for sleep-wake estimation. Studies on digital CBT-I thus far have shown favorable treatment effects in general, but the problem of a high dropout rate has not been sufficiently improved. In addition, more sophisticated technology is needed to develop fully automated digital CBT-I. As part of efforts to maximize the treatment effectiveness of future insomnia patients, research and development of mobile and Internet CBT-I and improvement of sleep tracker accuracy and validation studies are needed.

The modern society is a so-called era of big data. Whereas nearly everybody recognizes the "era of big data", no one can exactly define how big the data is a "big data". The reason for the ambiguity of the term big data mainly arises from the widespread of using that term. Along the widespread application of the digital technology in the everyday life, a large amount of data is generated every second in relation with every human behavior (i.e., measuring body movements through sensors, texts sent and received via social networking services). In addition, nonhuman data such as weather and Global Positioning System signals has been cumulated and analyzed in perspectives of big data (Kan et al. in Int J Environ Res Public Health 15(4), 2018 [1]). The big data has also influenced the medical science, which includes the field of psychiatry (Monteith et al. in Int J Bipolar Disord 3(1):21, 2015 [2]). In this chapter, we first introduce the definition of the term "big data". Then, we discuss researches which apply big data to solve problems in the clinical practice of psychiatry.

Psychiatric disorders are too multifactorial to be defined as a primarily inflammatory disorder, and increased inflammatory response is not specific to mental disorder only. Numerous factors are involved in neuroinflammation, and there also are many confounding variables, making it difficult to obtain consistent outcomes. Therefore, it is necessary to specify genetic, physiological, and epidemiological attributes of particular population groups vulnerable to inflammatory response as well as the disease subtypes. As of now, definitive inflammatory markers for psychiatric disorders have not been identified, but they could be very useful in patients with minimal vulnerability. In addition, it is possible to use inflammatory markers as depression biomarkers in subtypes of depression, which can serve as bases to develop medications to treat the disorder.

Suicide is a serious public health problem in many countries and has always been a source of apprehension and quest to human mankind, which can be resolved with due diligence. Suicide is a hidden and silent epidemic, with many causative factors. Studying and researching on various causative factors have always been the subject of significance for the researchers. Psychiatric illnesses happen to be the primary reasons for the majority of suicide mortality cases. Not only this, there has been a consistent increase in the no. of cases of mental disorders as well as attempted and completed suicide cases. If one looks at global scenario, an approximate 70,000 people commit suicide, and further alarming is the fact that the rate of suicide attempt cases has gone up to 250% during last 18 years in conflict zones. Poisoning, hanging and self-immolation are some of the common methods to commit suicide. Physical and mental illness, disturbed emotional relationships and economic difficulties were the major reasons for suicide with the predisposed population being women, student and farmers. As per WHO's approximation, there is one suicide every minute and an attempted suicide every third second. It implies that the number of killed due to suicide is greater than that of the ones killed due to the armed conflict.

The biopsychosocial approach is a model of eclecticism, which consists of multidisciplinary academic fields, reacting against the "neuroscientification" of psychiatry. The biopsychosocial approach was proposed by George Engel following Adolf Meyer's psychobiological and Roy R. Ginker's eclectic approach to psychiatry. Although the use of the biopsychosocial approach is increasing, it has several limitations: First, specific practices cannot be guided by the biopsychosocial approach because it is considered to be "boundless psychiatry." Second, unlike an initial intention, the symptomatic use of psychotropic medications may be justified by the biopsychosocial approach. Third, the economic forces to enhance biological psychiatry cannot be hindered by the biopsychosocial approach. Hence, to overcome the limitations of the current biopsychosocial approach, potential new paradigms including evolutionary psychiatry, pragmatism, integrationism, and pluralism have been proposed. Above all, Eric Kandel presented the link between neuroscience and psychiatry from the perspective of integrationism. In accordance with integrationism and/or pluralism, based on the paradigm shift of the theoretical construct from chemical imbalance to dysfunctional circuit, next-generation treatments for mental disorders have been proposed by Thomas Insel. Thus, a more integrated biopsychosocial approach to managing psychiatric disorders including schizophrenia and panic disorder may be proposed.

Because of the poor link between psychiatric diagnosis and neurobiological findings, it is difficult to classify mental disorders. The changes made to psychiatric diagnostic systems over the years can be understood in terms of "practical conservatism." The Diagnostic and Statistical Manual of Mental Disorders (DSM)-I and DSM-II were theoretically supported by the psychoanalytic and psychodynamic approach. Subsequently, psychiatric diagnoses of this kind were opposed by the anti-psychiatry movement, as well as by the findings of the Rosenhan experiment. Thus, the DSM-III revolution contained more empiricism, aligning psychiatry with biomedicine. Psychiatric diagnoses are classified and defined in terms of Kraepelinian dualism, using a categorical approach. The empirical trend was continued in the DSM-IV. To overcome the limitations of current psychiatric diagnostic systems and integrate fundamental genetic, neurobiological, behavioral, environmental, and experimental components into psychiatry, the Research Domain Criteria (RDoC) were established. To overcome the limitations of the categorical approach, psychiatrists have considered adopting a dimensional approach. However, their efforts were frustrated in the DSM-5 revision process. Thus, the DSM-5 is characterized by the rearrangement of psychiatric diagnoses, the partial adoption of a dimensional approach, the introduction of new diagnoses, and harmonization with the International Classification of Diseases.

Recently, noninvasive brain stimulation (NIBS) methodologies, including TMS and tDCS, have been considered as efficacious, safe, and innovative treatments and alternatives to conventional therapies for some psychiatric disorders. Developing evidence suggests that applying rTMS and tDCS over the cognitive control network (CCN), particularly the dorsolateral prefrontal cortex (DLPFC), may improve core symptoms in various psychiatric disorders via direct impact on the cognitive control processes involved in emotion regulation. Therefore, neuromodulation of brain regions involved in the cognitive control of emotion by NIBS approaches could contribute to a paradigm shift in psychiatry. The available evidence suggests that development of effective treatment alternatives to enhance cognition is critical for patients with psychiatric disorders. The purpose of this chapter is to review the cognition-enhancing properties of tDCS and TMS and the impact of these treatments on cognitive control processes, especially those related to emotion regulation in psychiatric disorders.

Background The human gut microbiome comprise a huge number of microorganisms with co-evolutionary associations with humans. It has been repeatedly revealed that bidirectional communication exists between the brain and the gut and involves neural, hormonal, and immunological pathways. Evidences from neuroscience researches over the past few years suggest that microbiota is essential for the development and maturation of brain systems that are associated to stress responses. Method This review provides that the summarization of the communication among microbiota, gut and brain and the results of preclinical and clinical studies on gut microbiota used in treatments for neuropsychiatric disorders. Result Recent studies have reported that diverse forms of neuropsychiatric disorders (such as autism, depression, anxiety, and schizophrenia) are associated with or modulated by variations in the microbiome, by microbial substrates, and by exogenous prebiotics, antibiotics, and probiotics. Conclusion The microbiota–gut–brain axis might provide novel targets for prevention and treatment of neuropsychiatric disorders. However, further studies are required to substantiate the clinical use of probiotics, prebiotics and FMT.

Background Recently, neuroinflammation and the immune-kynurenine pathway have received increased attention in the psychoimmunology field of major depressive disorder (MDD), while studies related to anxiety disorders have been very limited. Objective This study reviewed possible mechanisms by which stress or inflammation modulate anxiety through tryptophan metabolism and the kynurenine pathway. Methods Relevant literature was identified through a search of MEDLINE via PubMed. Results Accumulating evidence has indicated the modulatory effects of the immune-kynurenine pathway on anxiety. The tryptophan catabolites (TRYCATs) in the kynurenine pathway imbalanced by stress or inflammation induce serotonin and melatonin deficiency, making anxiety reactions more sensitive. In addition, TRYCATs cause or sustain anxiety by acting as endogenous anxiogens or anxiolytics, an NMDA agonist or antagonist, or a free radical generator. Conclusion We hope that our understanding of the psychoimmunological mechanisms of anxiety will be expanded and anxiety-related studies will receive greater attention.

Several studies have suggested a pathophysiological role of blood cell apoptosis in major depressive disorder (MDD). The aim of this study was to evaluate mRNA expression levels of Bcl-2, Bax, and Fas in peripheral blood mononuclear cells (PBMCs) of MDD patients with a high risk for suicide relative to those without a high risk for suicide as well as healthy subjects. The mRNA expression of Bcl-2, Bax, and Fas as well as the Bcl-2/Bax ratio was examined in the PBMCs of 30 MDD patients with a high risk for suicide, 30 MDD patients without a high risk for suicide, and 30 healthy controls. The mRNA expression of target genes was measured using real-time quantitative Polymerase Chain Reaction (PCR). FAS mRNA expression was significantly increased, and Bcl-2 mRNA expression and the Bcl-2/Bax expression ratio were significantly decreased, in the PBMCs of MDD patients with or without a high risk for suicide attempts compared to healthy controls (p

The serotonin-transporter-linked polymorphic region (5-HTTLPR) and brain-derived neurotrophic factor (BDNF) Val66Met polymorphism have been implicated in the pathophysiology of major depressive disorder (MDD). We aimed to investigate the effects of genetic variants of the 5-HTTLPR and BDNF Val66Met polymorphisms and their interactions with MDD on cortical volume and white matter integrity. Ninety-five patients with MDD and 65 healthy participants aged 20-65 years were recruited. The subjects were genotyped for the 5-HTTLPR and BDNF Val66Met polymorphisms and scanned with T1-weighted and diffusion tensor imaging. The gray matter volumes of 24 gyri in the prefrontal and anterior cingulate cortices and the fractional anisotropy values of nine white matter tracts in both hemispheres were determined. In the pooled sample of subjects from both groups, 5-HTTLPR L-allele carriers had significantly decreased cortical volume in the right anterior midcingulate gyrus compared to S-allele homozygotes. A significant effect of the interaction of the BDNF Val66Met polymorphism and MDD on the fractional anisotropy values of the right uncinate fasciculus was observed. Our results suggested that these genetic polymorphisms play important roles in the neurostructural changes of emotion-processing regions in subjects with MDD.

Although panic disorder (PD) and phobic disorders are independent anxiety disorders with distinct sets of diagnostic criteria, there is a high level of overlap between them in terms of pathogenesis and neural underpinnings. Functional connectivity research using resting-state functional magnetic resonance imaging (rsfMRI) shows great potential in identifying the similarities and differences between PD and phobias. Understanding common and distinct networks between PD and phobic disorders is critical for identifying both specific and general neural characteristics of these disorders. We review recent rsfMRI studies and explore the clinical relevance of resting-state functional connectivity (rsFC) in PD and phobias. Although findings differ between studies, there are some meaningful, consistent findings. Social anxiety disorder (SAD) and PD share common default mode network alterations. Alterations within the sensorimotor network are observed primarily in PD. Increased connectivity in the salience network is consistently reported in SAD. This review supports hypotheses that PD and phobic disorders share common rsFC abnormalities and that the different clinical phenotypes between the disorders come from distinct brain functional network alterations.

Mood disorders are a highly prevalent group of mental disorders causing substantial socioeconomic burden. There are various methodological approaches for identifying the underlying mechanisms of the etiology, symptomatology, and therapeutics of mood disorders; however, neuroimaging studies have provided the most direct evidence for mood disorder neural substrates by visualizing the brains of living individuals. The prefrontal cortex, hippocampus, amygdala, thalamus, ventral striatum, and corpus callosum are associated with depression and bipolar disorder. Identifying the distinct and common contributions of these anatomical regions to depression and bipolar disorder have broadened and deepened our understanding of mood disorders. However, the extent to which neuroimaging research findings contribute to clinical practice in the real-world setting is unclear. As traditional or non-machine learning MRI studies have analyzed group-level differences, it is not possible to directly translate findings from research to clinical practice; the knowledge gained pertains to the disorder, but not to individuals. On the other hand, a machine learning approach makes it possible to provide individual-level classifications. For the past two decades, many studies have reported on the classification accuracy of machine learning-based neuroimaging studies from the perspective of diagnosis and treatment response. However, for the application of a machine learning-based brain MRI approach in real world clinical settings, several major issues should be considered. Secondary changes due to illness duration and medication, clinical subtypes and heterogeneity, comorbidities, and cost-effectiveness restrict the generalization of the current machine learning findings. Sophisticated classification of clinical and diagnostic subtypes is needed. Additionally, as the approach is inevitably limited by sample size, multi-site participation and data-sharing are needed in the future.

Substance P (SP) has been implicated in major depressive disorder (MDD), with SP antagonists being studied as potential antidepressants. Although impaired neural plasticity is considered a key mechanism in MDD pathophysiology, the association between SP and brain structural changes in depression has not been investigated. We investigated the correlations between SP levels and white matter (WM) integrity in 42 medication-naive patients with MDD and 57 healthy controls (HCs). Plasma levels of SP were determined, and diffusion tensor imaging (DTI) was performed to investigate microstructural changes in WM tracts. In patients, negative correlations between SP levels and fractional anisotropy (FA) values of the forceps minor of the corpus callosum, and positive correlations between SP levels and radial diffusivity (RD) and mean diffusivity (MD) values of the right corticospinal tract (CST) were observed, with no significant correlations in HCs. Linear regression analyses showed SP levels to significantly predict FA values of the forceps minor, and RD and MD values of the right CST in patients, but not in HCs. We consider our findings to contribute to the neurobiological evidence on the association between SP and brain structural changes in depression, which may be related with the pathophysiology and treatment of MDD.

Major depressive disorder (MDD) is a condition which has often been associated with chronic stress. The sympathetic nervous system is continuously activated without the normal counteraction of the parasympathetic nervous system under the influence of chronic stress. As a result, epinephrine and norepinephrine levels are increased, and acetylcholine levels are decreased, which in turn can increase the levels of pro-inflammatory cytokines. Peripheral inflammatory responses can access the brain, with neuroinflammation contributing to the increase in neurotoxic kynurenine pathway metabolites such as 3-hydroxykynurenine, 3-hydroxyanthranilic acid and quinolinic acid, and decrease in neuroprotective metabolites such as kynurenic acid. Pro-inflammatory cytokines can also exert direct neurotoxic effects on specific brain regions. Previous imaging studies have reported associations between pro-inflammatory states and alterations in brain regions involved in emotional regulation, including the hippocampus, amygdala and anterior cingulate cortex. Alterations in structure and function of such brain areas due to the neurotoxic effects of increased inflammation may be associated with the pathophysiology of depression. This review focuses the influence of stress on neuroinflammation which may cause alterations in brain structure and function in MDD.

Recently, there has been a major shift in the field of psychiatry towards the exploration of complex relationships between blood-based biomarkers and the pathophysiology of psychiatric and neuropsychiatric disorders. However, issues with study reproducibility, validity and reliability have hindered progress towards the identification of clinically relevant biomarkers for psychiatry. The achievement of laboratory validity is a crucial first step for the posterior development of clinical validity. There is evidence that the variability observed in blood-based research studies may be minimised with the implementation of standardised pre-analytical methods and uniform clinical protocols (i.e., pre-venipuncture). It has been documented that errors made in the pre-analytical phase account for 46–68.2% of laboratory testing errors. Thus, standardising clinical assessment, ethical procedures and pre-analytical phase of clinical research is essential for the reproducibility, validity and reliability of blood marker assessment, and reducing the risk of invalid test results. Various other areas of research have already moved towards guidelines for the standardised collection of blood-based biomarkers. Here we aim to provide a set of guidelines that we believe would improve biomarker research: (1) pre-venipuncture information and documentation, (2) ethics of participant consent and (3) pre-analytical methods. Ultimately, we hope this will assist study planning and will improve data comparison across studies allowing for the discovery of biomarkers in psychiatry with both laboratorial and clinical validity.