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Objective: The aim of the present study is to investigate the brain circuits or networks that underpin diagnostically specific tasks by means of group independent component analysis for FMRI toolbox (GIFT). We hypothesised that there will be neural network patterns of activation and deactivation, which correspond to real-time performance on clinical self-evaluation scales. Methods: In total, 20 healthy controls (HC) and 22 patients with major depressive episode have been included. All subjects were scanned with functional magnetic resonance imaging (fMRI) with paradigm composed of diagnostic clinical self-assessment depression scale contrasted to neutral scale. The data were processed with group independent component analysis for functional MRI toolbox and statistical parametric mapping. Results: The results have demonstrated that there exist positively or negatively modulated brain networks during processing of diagnostic specific task questions for depressive disorder. There have also been confirmed differences in the networks processing diagnostic versus off blocks between patients and controls in anterior cingulate cortex and middle frontal gyrus. Diagnostic conditions (depression scale) when contrasted to neutral conditions demonstrate differential activity of right superior frontal gyrus and right middle cingulate cortex in the comparison of patients with HC. Conclusion: Potential neuroimaging of state-dependent biomarkers has been directly linked with clinical assessment self-evaluation scale, administered as stimuli simultaneously with the fMRI acquisition. It may be regarded as further evidence in support of the convergent capacity of both methods to distinguish groups by means of incremental translational cross-validation. [ABSTRACT FROM AUTHOR]

This document is an editorial from the journal "Brain Sciences" discussing the influence of stress on the nervous system and its impact on anxiety and affective disorders. The editorial highlights the prevalence of anxiety disorders and their effect on quality of life, particularly in young adults. It explores the interrelations between environmental stress factors, such as psychological and biological stressors, substance misuse, and their effects on brain structure and function. The editorial also discusses the shared clinical features and underlying mechanisms of anxiety and mood disorders, as well as the role of stress in modern society. It introduces a special issue that includes nine manuscripts covering various aspects of stress and its interactions with affective disorders. The manuscripts explore topics such as suicide risk assessment, immune regulatory mechanisms, genetic factors, diagnostic validity in psychiatry, and neuroimaging as a tool for treatment response monitoring. The editorial emphasizes the need for multidisciplinary perspectives in psychiatry and the importance of considering social and cultural influences in diagnostic frameworks. Overall, the articles in this special issue provide insights into the mechanisms and pathways of anxiety and affective disorders in relation to stress. [Extracted from the article]

The first epidemiological wave of the incidence of COVID-19 in Bulgaria was registered in June 2020. After the wave peak, we conducted a study in persons diagnosed with COVID-19 (N = 52). They were assessed with the anxiety–depressive scale (ADS), including basic (BS), vegetative (VS), conversion (CS), obsessive–phobic (OPS), and depressive (DS) symptoms. ADS assessment of individuals diagnosed with SARS-CoV-2 indicated a correlation between OPS and IL-33 values. IL-10 levels were higher than reference ranges in all patients. Multiple linear regression analyses demonstrated that combination of CS and OPS explained 28% of IL-33 levels, while combination of symptoms from all ADS dimensions explained 24% of IL-33 levels. It was also found that 21% of IL-28A levels was explained from the combination by all ADS dimensions, whereas OPS was the predictor for lower concentrations. The obtained results revealed meaningful correlations between psycho neuro–immunological factors in pathogenesis of illness from the coronavirus infection. [ABSTRACT FROM AUTHOR]

The earliest critical context of the pandemic, preceding the first real epidemiological wave of contagion in Bulgaria, was examined using a socio-affective perspective. A retrospective and agnostic analytical approach was adopted. Our goal was to identify traits and trends that explain public health support (PHS) of Bulgarians during the first two months of the declared state of emergency. We investigated a set of variables with a unified method within an international scientific network named the International Collaboration on Social & Moral Psychology of COVID-19 (ICSMP) in April and May 2020. A total of 733 Bulgarians participated in the study (67.3% females), with an average age of 31.8 years (SD = 11.66). Conspiracy Theories Beliefs were a significant predictor of lower PHS. Psychological Well-Being was significantly associated with Physical Contact and Anti-Corona Policy Support. Physical Contact was significantly predicted by fewer Conspiracy Theories Beliefs, higher Collective Narcissism, Open-mindedness, higher Trait Self-Control, Moral Identity, Risk Perception and Psychological Well-Being. Physical Hygiene compliance was predicted by fewer Conspiracy Theories Beliefs, Collective Narcissism, Morality-as-Cooperation, Moral Identity and Psychological Well-Being. The results revealed two polar trends of support and non-support of public health policies. The contribution of this study is in providing evidence for the affective polarization and phenomenology of (non)precarity during the outbreak of the pandemic. [ABSTRACT FROM AUTHOR]

Background: The 20th century has seen great developments in the concept of disease. Marked by the biopsychosocial paradigm, several strategies for disease definition were added to previous descriptive organic views, but a final concept is still out of reach. Method: A critical review was carried out on thorough analysis of articles and textbooks to describe the main concepts and definitions of disease. Results: The concept 'disease' is a pragmatic construct, not a natural kind. Three main ways to define disease were identified, and characterized: Biological (disease as a lesion, disadvantage/deviation from normal and dysfunction), Psychosocial (distress and disability, existential potentials, descriptive prototype, and prototype typification), and values‐based definition. Conclusion: All the paradigms have advantages and flaws, but progressive use of all criteria in disease definition adds validity and reliability to diagnostic constructs. Such constructs must be, above all, useful for practice and research. Biological paradigm is relevant, but fails to cover all the complexity that involves human illness and the treatment process. An emphasis on distress, dysfunction, and carefully selected value‐laden characteristics might be the right direction for useful diagnostic construct conceptions. [ABSTRACT FROM AUTHOR]

The article provides information on the significance of insight in mental illnesses and its impact on therapy adherence and treatment outcomes. It explores whether insight should be viewed as a qualitative aspect or a continuous spectrum. It mentions the relationship between depression and insight is influenced by stigma and the individual's adaptation to their illness.

The present essay examines the COVID-19 pandemic as a persistent psychosocial crisis through in a shared perspective by analytical psychotherapy, psychophysical interactions, existential phenomenology, and the polymorphic contexts of the social sciences. There is proposed a critical interpretation of trauma and its consequences at individual and group level, as well as a reconciliation of changes which occurred in human relationships. The classical analytical framework in the paradigm of modern psychotherapy and phylosophy may motivate research to expand and penetrate scientific knowledge leading to approaches for coping with the emotional pandemic co-occurring with COVID-19. [ABSTRACT FROM AUTHOR]

Major depression is accompanied by increased IgM-mediated autoimmune responses to oxidative specific epitopes (OSEs) and nitric oxide (NO)-adducts. These responses were not examined in bipolar disorder type 1 (BP1) and BP2. IgM responses to malondialdehyde (MDA), phosphatidinylinositol, oleic acid, azelaic acid, and NO-adducts were determined in 35 healthy controls, and 47 major depressed (MDD), 29 BP1, and 25 BP2 patients. We also measured serum peroxides, IgG to oxidized LDL (oxLDL), and IgM/IgA directed to lipopolysaccharides (LPS). IgM responses to OSEs and NO-adducts (OSENO) were significantly higher in MDD and BP1 as compared with controls, and IgM to OSEs higher in MDD than in BP2. Partial Least Squares (PLS) analysis showed that 57.7% of the variance in the clinical phenome of mood disorders was explained by number of episodes, a latent vector extracted from IgM to OSENO, IgG to oxLDL, and peroxides. There were significant specific indirect effects of IgA/IgM to LPS on the clinical phenome, which were mediated by peroxides, IgM OSENO, and IgG oxLDL. Using PLS we have constructed a data-driven nomothetic network which ensembled causome (increased plasma LPS load), adverse outcome pathways (namely neuro-affective toxicity), and clinical phenome features of mood disorders in a data-driven model. Based on those feature sets, cluster analysis discovered a new diagnostic class characterized by increased plasma LPS load, peroxides, autoimmune responses to OSENO, and increased phenome scores. Using the new nomothetic network approach, we constructed a mechanistically transdiagnostic diagnostic class indicating neuro-affective toxicity in 74.3% of the mood disorder patients. [ABSTRACT FROM AUTHOR]

There is delivered in this paper a conceptual background of a research program to re-validate psychopathological constructs by means of functional neuroimaging. The philosophical concepts employed are identity and supervenience theory of mind, and eliminative materialism. Our empirical research is then mapped on those theoretical frameworks. There are reported diff erential activations of clinical depression assessment scale between healthy controls and depressed patients on one hand and between depression and schizophrenia clinical populations of the other. Further there has been explored the possibility to discriminate the two nosological groups by means of machine learning experiment. Our bottom line message is that the complexity and inter-disciplinary nature of psychiatric diagnosis demands profound understanding of mind-brain interactions. This understanding calls for pluralist account concerning possible solutions of the psycho-physical problem and refraining from attempts to impose universal, monopolistic and paradigmatic decisions. [ABSTRACT FROM AUTHOR]

Major depressive disorder (MDD) is accompanied by higher serum IgM/IgA responses to LPS of Gram-negative bacteria, suggesting increased bacterial translocation and gut dysbiosis while the latter may occur in bipolar disorder (BD). There are differences between MDD and BD type 1 (BP1) and 2 (BP2) in nitro-oxidative stress biomarkers associated with leaky gut. This study examines serum IgM/IgA responses directed to LPS of 6 Gram-negative bacteria as well as IgG responses to oxidized LDL (oxLDL) in 29 BP1, 37 BP2, 44 MDD, and 30 healthy individuals. Increased IgM/IgA responses to Pseudomonas aeruginosa significantly discriminated patients with affective disorders (MDD plus BD) from controls. BP1 patients showed higher IgM responses to Morganella morganii as compared with MDD and BP2 patients. Patients with melancholia showed higher IgA responses to Citrobacter koseri as compared to controls and non-melancholic depression. The total score on the Hamilton Depression Rating Scale was significantly associated with IgA responses to C. koseri. IgG to oxLDL was significantly associated with increased bacterial translocation. In conclusion, MDD, BP1, and BP2 are accompanied by an immune response due to the increased load of LPS while these aberrations in the gut-brain axis are most pronounced in BP1 and melancholia. Activated oxidative stress pathways and autoimmune responses to oxidative specific epitopes in mood disorders may be driven by a breakdown in gut paracellular, transcellular, and/or vascular pathways. If replicated, drugs that protect the integrity of the gut barrier may offer novel therapeutic opportunities for BP1 and MDD. [ABSTRACT FROM AUTHOR]

Objective: The aim of the current study was to examine whether and to what extent mood disorders, comprising major depression and bipolar disorder, are accompanied by structural changes in the brain as measured using voxel-based morphometry (VBM). Methods: We performed a VBM study using a 3Т MRI system (GE Discovery 750w) in patients with mood disorders (n=50), namely, 39 with major depression and 11 with bipolar disorder compared to 42 age-, sex- and education-matched healthy controls. Results: Our results show that depression was associated with significant decreases in grey matter (GM) volume of the regions located within the medial frontal and anterior cingulate cortex on the left side and middle frontal gyrus, medial orbital gyrus, inferior frontal gyrus (triangular and orbital parts) and middle temporal gyrus (extending to the superior temporal gyrus) on the right side. When the patient group was separated into bipolar disorder and major depression, the reductions remained significant only for patients with major depressive disorder. Conclusions: Using VBM the present study was able to replicate decreases in GM volume restricted to frontal and temporal regions in patients with mood disorders, mainly major depression, compared with healthy controls. [ABSTRACT FROM AUTHOR]

Abstract: Rationale, aims, and objectives: In this study, we have attempted to replicate the findings of altered emotional processing in depressed patients compared with healthy controls by means of functional magnetic resonance imaging during passive viewing of positive, negative, and neutral pictures from the International Affective Pictures System. Methods: Nineteen medicated depressed patients and 19 sex and age‐matched healthy controls underwent functional magnetic resonance imaging during presentation of affective pictures in a block design. The differences between the blood oxygen level dependent signal elicited in the tree conditions were compared. Within‐group and between‐group analyses were performed with stringent criteria for statistical inference (P < .05 with family‐wise error correction). Results: In medicated depressed patients, positive pictures compared with neutral pictures activated predominantly the posterior cingulate cortex and precuneus, as well as occipital and middle temporal areas mainly on the left side, while in healthy controls, only the occipito‐temporal areas demonstrated significant activation. The negative pictures elicited stronger activation of occipital and temporal regions in both groups and of inferior frontal gyrus only in control subjects. The difference between the groups did not reach statistical significance. Positive correlation was demonstrated between activation levels of clusters located in left precuneus/posterior cingulate cortex and left inferior/middle occipital gyrus and Montgomery‐Asberg Depression Rating Scale scores in patients while viewing positive compared with neutral pictures. Conclusions: Although the within‐group analysis demonstrated significant activations in both groups with apparent discrepancies, the between‐group analysis did not reach statistical significance under the stringent criteria for statistical inference. These results are further contextualized in the critical debate on the methodological issues of clinical evaluation in psychiatry, more specifically the validity and consistency of the applied methods and the limitations existing in the attempts to provide sound cross‐disciplinary validation of the diagnostic tools by means of neuroscience. [ABSTRACT FROM AUTHOR]

Invited commentary on: The neuro-immune and neurotoxic fingerprint of major neuro-cognitive psychosis or deficit schizophrenia: a supervised machine learning study, by Maes et Al. [ABSTRACT FROM AUTHOR]

Translation as a notion and procedure is deeply embodied in medical science and education. Translation includes the possibility to translate data across disciplines to improve diagnosis and treatment procedures. The evidence accumulated using translation serves as a vehicle for reification of medical diagnoses. There are promising, established post hoc correlations between the different types of clinical tools (interviews and inventories) and neuroscience. The various measures represent statistical correlations that must now be integrated into diagnostic standards and procedures but this, as a whole, is a step forward towards a better understanding of the mechanisms underlying psychopathology in general and depression in particular. Here, we focus on functional magnetic resonance imaging studies using a trans-disciplinary approach and attempt to establish bridges between the different fields. We will selectively highlight research areas such as imaging genetics, imaging immunology and multimodal imaging, as related to the diagnosis and management of depression. [ABSTRACT FROM AUTHOR]

Abstract In this article we aim at conceptual reconstruction of the historical background behind RDoC project. It incorporates some elements that have not heretofore been included in frameworks for psychopathology research. At the same time, however, RDoC – like any approach to mental illness – must grapple with longstanding challenges in addressing issues about the roles and relationships of mind, brain, and patients' reports in considering the nature of disorder. In this respect, the historical roots of psychopathology remain as relevant as ever. [ABSTRACT FROM AUTHOR]

We address the interpretability of the machine learning algorithm in the context of the relevant problem of discriminating between patients with major depressive disorder (MDD) and healthy controls using functional networks derived from resting-state functional magnetic resonance imaging data. We applied linear discriminant analysis (LDA) to the data from 35 MDD patients and 50 healthy controls to discriminate between the two groups utilizing functional networks' global measures as the features. We proposed the combined approach for feature selection based on statistical methods and the wrapper-type algorithm. This approach revealed that the groups are indistinguishable in the univariate feature space but become distinguishable in a three-dimensional feature space formed by the identified most important features: mean node strength, clustering coefficient, and the number of edges. LDA achieves the highest accuracy when considering the network with all connections or only the strongest ones. Our approach allowed us to analyze the separability of classes in the multidimensional feature space, which is critical for interpreting the results of machine learning models. We demonstrated that the parametric planes of the control and MDD groups rotate in the feature space with increasing the thresholding parameter and that their intersection increases with approaching the threshold of 0.45, for which classification accuracy is minimal. Overall, the combined approach for feature selection provides an effective and interpretable scenario for discriminating between MDD patients and healthy controls using measures of functional connectivity networks. This approach can be applied to other machine learning tasks to achieve high accuracy while ensuring the interpretability of the results. [ABSTRACT FROM AUTHOR]

This study was conducted to examine whether there are quantitative or qualitative differences in the connectome between psychiatric patients and healthy controls and to delineate the connectome features of major depressive disorder (MDD), schizophrenia (SCZ) and bipolar disorder (BD), as well as the severity of these disorders. Toward this end, we performed an effective connectivity analysis of resting state functional MRI data in these three patient groups and healthy controls. We used spectral Dynamic Causal Modeling (spDCM), and the derived connectome features were further subjected to machine learning. The results outlined a model of five connections, which discriminated patients from controls, comprising major nodes of the limbic system (amygdala (AMY), hippocampus (HPC) and anterior cingulate cortex (ACC)), the salience network (anterior insula (AI), and the frontoparietal and dorsal attention network (middle frontal gyrus (MFG), corresponding to the dorsolateral prefrontal cortex, and frontal eye field (FEF)). Notably, the alterations in the self-inhibitory connection of the anterior insula emerged as a feature of both mood disorders and SCZ. Moreover, four out of the five connectome features that discriminate mental illness from controls are features of mood disorders (both MDD and BD), namely the MFG→FEF, HPC→FEF, AI→AMY, and MFG→AMY connections, whereas one connection is a feature of SCZ, namely the AMY→SPL connectivity. A large part of the variance in the severity of depression (31.6%) and SCZ (40.6%) was explained by connectivity features. In conclusion, dysfunctions in the self-regulation of the salience network may underpin major mental disorders, while other key connectome features shape differences between mood disorders and SCZ, and can be used as potential imaging biomarkers. [ABSTRACT FROM AUTHOR]

The neurodegenerative and neurodevelopmental hypotheses represent the basic etiological framework for the origin of schizophrenia. Additionally, the dopamine hypothesis, adopted more than two decades ago, has repeatedly asserted the position of dopamine as a pathobiochemical substrate through the action of psychostimulants and neuroleptics on the mesolimbic and mesocortical systems, giving insight into the origin of positive and negative schizophrenic symptoms. Meanwhile, cognitive impairments in schizophrenia remain incompletely understood but are thought to be present during all stages of the disease, as well as in the prodromal, interictal and residual phases. On the other hand, observations on the effects of NMDA antagonists, such as ketamine and phencyclidine, reveal that hypoglutamatergic neurotransmission causes not only positive and negative but also cognitive schizophrenic symptoms. This review aims to summarize the different hypotheses about the origin of psychoses and to identify the optimal neuroimaging method that can serve to unite them in an integral etiological framework. We systematically searched Google scholar (with no concern to the date published) to identify studies investigating the etiology of schizophrenia, with a focus on impaired central neurotransmission. The complex interaction between the dopamine and glutamate neurotransmitter systems provides the long-needed etiological concept, which combines the neurodegenerative hypothesis with the hypothesis of impaired neurodevelopment in schizophrenia. Pharmaco-magnetic resonance imaging is a neuroimaging method that can provide a translation of scientific knowledge about the neural networks and the disruptions in and between different brain regions, into clinically applicable and effective therapeutic results in the management of severe psychotic disorders. [ABSTRACT FROM AUTHOR]

We constructed a novel design integrating the administration of a clinical self-assessment scale with simultaneous acquisition of functional Magnetic Resonance Imaging (fMRI), aiming at cross-validation between psychopathology evaluation and neuroimaging techniques. We hypothesized that areas demonstrating differential activation in two groups of patients (the first group exhibiting paranoid delusions in the context of paranoid schizophrenia—SCH—and second group with a depressive episode in the context of major depressive disorder or bipolar disorder—DEP) will have distinct connectivity patterns and structural differences. Fifty-one patients with SCH (n = 25) or DEP (n = 26) were scanned with three different MRI sequences: a structural and two functional sequences—resting-state and task-related fMRI (the stimuli represent items from a paranoid-depressive self-evaluation scale). While no significant differences were found in gray matter volumes, we were able to discriminate between the two clinical entities by identifying two significant clusters of activations in the SCH group—the left Precuneus (PreCu) extending to the left Posterior Cingulate Cortex (PCC) and the right Angular Gyrus (AG). Additionally, the effective connectivity of the middle frontal gyrus (MFG), a part of the Dorsolateral Prefrontal Cortex (DLPFC) to the Anterior Insula (AI), demonstrated a significant difference between the two groups with inhibitory connection demonstrated only in SCH. The observed activations of PreCu, PCC, and AG (involved in the Default Mode Network DMN) might be indirect evidence of the inhibitory connection from the DLPFC to AI, interfering with the balancing function of the insula as the dynamic switch in the DMN. The findings of our current study might suggest that the connectivity from DLPFC to the anterior insula can be interpreted as evidence for the presence of an aberrant network that leads to behavioral abnormalities, the manifestation of which depends on the direction of influence. The reduced effective connectivity from the AI to the DLPFC is manifested as depressive symptoms, and the inhibitory effect from the DLPFC to the AI is reflected in the paranoid symptoms of schizophrenia. [ABSTRACT FROM AUTHOR]

Traditional psychiatric diagnosis has been overly reliant on either self-reported measures (introspection) or clinical rating scales (interviews). This produced the so-called explanatory gap with the bio-medical disciplines, such as neuroscience, which are supposed to deliver biological explanations of disease. In that context the neuro-biological and clinical assessment in psychiatry remained discrepant and incommensurable under conventional statistical frameworks. The emerging field of translational neuroimaging attempted to bridge the explanatory gap by means of simultaneous application of clinical assessment tools and functional magnetic resonance imaging, which also turned out to be problematic when analyzed with standard statistical methods. In order to overcome this problem our group designed a novel machine learning technique, multivariate linear method (MLM) which can capture convergent data from voxel-based morphometry, functional resting state and task-related neuroimaging and the relevant clinical measures. In this paper we report results from convergent cross-validation of biological signatures of disease in a sample of patients with schizophrenia as compared to depression. Our model provides evidence that the combination of the neuroimaging and clinical data in MLM analysis can inform the differential diagnosis in terms of incremental validity. [ABSTRACT FROM AUTHOR]

Major depression (MDD) and a lifetime history of MDD are characterized by increased nitrosylation, while bipolar disorder type 1 (BP1), but not BP2, is accompanied by highly increased levels of oxidative stress and nitric oxide (NO) production. Nevertheless, it is unknown whether nitrosylation is involved in BP and whether there are differences in nitrosylation between BP1 and BP2. Serum IgM antibodies directed against nitroso (NO)-adducts were examined in MDD, BP1, BP2 and healthy controls, namely IgM responses to NO-cysteine, NO-tryptophan (NOW), NO-arginine and NO-albumin (SBA) in association with IgA/IgM responses to LPS of Gram-negative bacteria, IgG responses to oxidized low-density lipoprotein (ox-LDL) and serum peroxides. Serum IgM levels against NO adducts were significantly higher in BP1 and MDD as compared with healthy controls, whereas BP2 patients occupied an intermediate position. IgM responses to NO-albumin were significantly higher in BP1 and MDD than in BP2 patients. There were highly significant associations between the IgM responses to NO-adducts and IgG responses to ox-LDL and IgA/IgM responses to Gram-negative bacteria. BP1 and MDD are characterized by an upregulation of the nitrosylome (the proteome of nitrosylated proteins) and increased IgM responses to nitrosylated conjugates. Increased nitrosylation may be driven by increased bacterial translocation and is associated with lipid peroxidation processes. Innate-like (B1 and marginal zone) B cells and increased nitrosylation may play a key role in the major affective disorders through activation of immune-inflammatory and oxidative pathways, cardiovascular comorbidity and impairments in antioxidant defenses, neuro-glial interactions, synaptic plasticity, neuroprotection, neurogenesis. • Serum IgM levels against NO adducts are significantly increased in bipolar type 1 (BP1) and major depression (MDD). • There are significant associations between these IgM responses and IgG responses to oxidized LDL and IgA/IgM responses to Gram-negative bacteria. • BP1 and MDD are characterized by an upregulation of the nitrosylome and increased IgM responses to nitrosylated conjugates. [ABSTRACT FROM AUTHOR]

We present a novel method for analyzing brain functional networks using functional magnetic resonance imaging data, which involves utilizing consensus networks. In this study, we compare our approach to a standard group-based method for patients diagnosed with major depressive disorder (MDD) and a healthy control group, taking into account different levels of connectivity. Our findings demonstrate that the consensus network approach uncovers distinct characteristics in network measures and degree distributions when considering connection strengths. In the healthy control group, as connection strengths increase, we observe a transition in the network topology from a combination of scale-free and random topologies to a small-world topology. Conversely, the MDD group exhibits uncertainty in weak connections, while strong connections display small-world properties. In contrast, the group-based approach does not exhibit significant differences in behavior between the two groups. However, it does indicate a transition in topology from a scale-free-like structure to a combination of small-world and scale-free topologies. The use of the consensus network approach also holds immense potential for the classification of MDD patients, as it unveils substantial distinctions between the two groups. [ABSTRACT FROM AUTHOR]

Introduction: In the current study, we used the Stroop Color and Word Test (SCWT) combined with an n-back component in functional magnetic resonance imaging (fMRI) in order to activate the working memory and cognitive interference in patients with Major Depressive Disorder (MDD) as compared to healthy controls. Our hypothesis was that there would be significant alterations in the selective visual attention processing regions of the brain which may identify mechanisms underlying major depression. Materials and Methods: Fifty participants, of which 24 were patients with depression and 26 healthy controls were recruited. Results: The first major finding of the current study was hypoactivation in the lingual gyrus during the condition with instructions to track the sequence of the words (word>color) of the Stroop n-back task and hyperactivation of the same structure in the opposite (color>word) condition where subjects had to focus on the order of the word color in depressed patients as compared to healthy controls. Conclusions: Changes in these regions have been consistently reported across studies with different fMRI techniques in both adolescent and adult patients with MDD reinforcing the role of the region in the pathophysiology of depression. Further studies are needed to examine possible longitudinal changes in the region and its activity in remission. [ABSTRACT FROM AUTHOR]

Accumulating evidence shows that nitro-oxidative pathways play an important role in the pathophysiology of major depressive disorder (MDD) and bipolar disorder (BD) and maybe anxiety disorders. The current study aims to examine superoxide dismutase (SOD1), catalase, lipid hydroperoxides (LOOH), nitric oxide metabolites (NOx), advanced oxidation protein products (AOPP), malondialdehyde (MDA), glutathione (GSH), paraoxonase 1 (PON1), high-density lipoprotein cholesterol (HDL), and uric acid (UA) in participants with and without generalized anxiety disorder (GAD) co-occurring or not with BD, MDD, or tobacco use disorder. Z unit-weighted composite scores were computed as indices of nitro-oxidative stress driving lipid and protein oxidation. SOD1, LOOH, NOx, and uric acid were significantly higher and HDL and PON1 significantly lower in participants with GAD than in those without GAD. GAD was more adequately predicted by increased SOD + LOOH + NOx and lowered HDL + PON1 composite scores. Composite scores of nitro-oxidative stress coupled with aldehyde and AOPP production were significantly increased in participants with comorbid GAD + MDD as compared with all other study groups, namely MDD, GAD + BD, BD, GAD, and healthy controls. In conclusion, GAD is characterized by increased nitro-oxidative stress and lipid peroxidation and lowered lipid-associated antioxidant defenses, while increased uric acid levels in GAD may protect against aldehyde production and protein oxidation. This study suggests that increased nitro-oxidative stress and especially increased SOD1 activity, NO production, and lipid peroxidation as well as lowered HDL-cholesterol and PON1 activity could be novel drug targets for GAD especially when comorbid with MDD. [ABSTRACT FROM AUTHOR]

Abstract: Rationale: Deficit schizophrenia, as defined by the Schedule for Deficit Syndrome, may represent a distinct diagnostic class defined by neurocognitive impairments coupled with changes in IgA/IgM responses to tryptophan catabolites (TRYCATs). Adequate classifications should be based on supervised and unsupervised learning rather than on consensus criteria. Methods: This study used machine learning as means to provide a more accurate classification of patients with stable phase schizophrenia. Results: We found that using negative symptoms as discriminatory variables, schizophrenia patients may be divided into two distinct classes modelled by (A) impairments in IgA/IgM responses to noxious and generally more protective tryptophan catabolites, (B) impairments in episodic and semantic memory, paired associative learning and false memory creation, and (C) psychotic, excitation, hostility, mannerism, negative, and affective symptoms. The first cluster shows increased negative, psychotic, excitation, hostility, mannerism, depression and anxiety symptoms, and more neuroimmune and cognitive disorders and is therefore called “major neurocognitive psychosis” (MNP). The second cluster, called “simple neurocognitive psychosis” (SNP) is discriminated from normal controls by the same features although the impairments are less well developed than in MNP. The latter is additionally externally validated by lowered quality of life, body mass (reflecting a leptosome body type), and education (reflecting lower cognitive reserve). Conclusions: Previous distinctions including “type 1” (positive)/“type 2” (negative) and DSM‐IV‐TR (eg, paranoid) schizophrenia could not be validated using machine learning techniques. Previous names of the illness, including schizophrenia, are not very adequate because they do not describe the features of the illness, namely, interrelated neuroimmune, cognitive, and clinical features. Stable‐phase schizophrenia consists of 2 relevant qualitatively distinct categories or nosological entities with SNP being a less well‐developed phenotype, while MNP is the full blown phenotype or core illness. Major neurocognitive psychosis and SNP should be added to the DSM‐5 and incorporated into the Research Domain Criteria project. [ABSTRACT FROM AUTHOR]

A large body of evidence indicates that major affective disorders are accompanied by activated neuro-immune, neuro-oxidative and neuro-nitrosative stress (IO&NS) pathways. Postpartum depression is predicted by end of term prenatal depressive symptoms whilst a lifetime history of mood disorders appears to increase the risk for both prenatal and postpartum depression. This review provides a critical appraisal of available evidence linking IO&NS pathways to prenatal and postpartum depression. The electronic databases Google Scholar, PubMed and Scopus were sources for this narrative review focusing on keywords, including perinatal depression, (auto)immune, inflammation, oxidative, nitric oxide, nitrosative, tryptophan catabolites (TRYCATs), kynurenine, leaky gut and microbiome. Prenatal depressive symptoms are associated with exaggerated pregnancy-specific changes in IO&NS pathways, including increased C-reactive protein, advanced oxidation protein products and nitric oxide metabolites, lowered antioxidant levels, such as zinc, as well as lowered regulatory IgM-mediated autoimmune responses. The latter pathways coupled with lowered levels of endogenous anti-inflammatory compounds, including ω3 polyunsaturated fatty acids, may also underpin the pathophysiology of postpartum depression. Although increased bacterial translocation, lipid peroxidation and TRYCAT pathway activation play a role in mood disorders, similar changes do not appear to be relevant in perinatal depression. Some IO&NS biomarker characteristics of mood disorders are found in prenatal depression indicating that these pathways partly contribute to the association of a lifetime history of mood disorders and perinatal depression. However, available evidence suggests that some IO&NS pathways differ significantly between perinatal depression and mood disorders in general. This review provides a new IO&NS model of prenatal and postpartum depression. [ABSTRACT FROM AUTHOR]

We used the Mass Multivariate Method on structural, resting-state, and task-related fMRI data from two groups of patients with schizophrenia and depression in order to define several regions of significant relevance to the differential diagnosis of those conditions. The regions included the left planum polare (PP), the left opercular part of the inferior frontal gyrus (OpIFG), the medial orbital gyrus (MOrG), the posterior insula (PIns), and the parahippocampal gyrus (PHG). This study delivered evidence that a multimodal neuroimaging approach can potentially enhance the validity of psychiatric diagnoses. Structural, resting-state, or task-related functional MRI modalities cannot provide independent biomarkers. Further studies need to consider and implement a model of incremental validity combining clinical measures with different neuroimaging modalities to discriminate depressive disorders from schizophrenia. Biological signatures of disease on the level of neuroimaging are more likely to underpin broader nosological entities in psychiatry. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has profoundly changed people's ability to recreate in public green spaces, which is likely to exacerbate the psychological impacts of the pandemic. In the current study, we seek to understand whether greenery can support mental health even with insufficient outdoor exposure in times of physical isolation from the outdoor environment. Between 17 May and 10 June, 2020, we conducted an online survey among 323 students (21.99 ± 3.10 years; 31% male) in health-related programs from two universities in the city of Plovdiv, Bulgaria. Severities of depressive and anxiety symptoms over the past two weeks were measured with the Patient Health Questionnaire 9-item and the Generalized Anxiety Disorder 7-item scale. We employed two self-reported measures of greenery experienced indoors (number of houseplants in the home and proportion of exterior greenery visible from inside the home) and two measures of greenery experienced outdoors (presence/absence of a domestic garden and availability of neighborhood greenery). Restorative quality of the home (the "being away" dimension of the Perceived Restorativeness Scale; PRS) and the neighborhood (the "being away" and "fascination" dimensions of the PRS), engagement with outdoor greenery (frequency of different types of interaction) and perceived social support were treated as mediators. Associations between greenery and mental health were tested using generalized linear regression and logistic regression. Structural equation modelling (SEM) techniques were used to test the theoretically-indicated relations among the variables. Clinically-meaningful symptoms of moderate depression and anxiety were reported by approximately 33% and 20% of the students, respectively. The relative abundance of greenery visible from the home or in the neighborhood was associated with reduced depressive/anxiety symptoms and lower depression/anxiety rates. Having more houseplants or a garden was also associated with some of these markers of mental health. As hypothesized, the mental health-supportive effects of indoor greenery were largely explained by increased feelings of being away while at home. Neighborhood greenery contributed to neighborhood restorative quality, which in turn facilitated social support and more frequent engagement with greenery, and that led to better mental health. Students who spent most of their time at home during the COVID-19 epidemic experienced better mental health when exposed to more greenery. Our findings support the idea that exposure to greenery may be a valuable resource during social isolation in the home. However, causal interpretation of these associations is not straightforward. • The COVID-19 pandemic has changed people's ability to recreate in public green spaces. • We surveyed 323 students spending on average 20 h/day at home. • Houseplants/green window views were associated with increased feelings of being away while at home. • Restorative quality, social support and engagement with greenery were mediators. • Greenery experienced both indoors and outdoors supported mental health. [ABSTRACT FROM AUTHOR]