Psychiatry remains in a permanent state of crisis, which fragmented psychiatry from the field of medicine. The crisis in psychiatry is evidenced by the many different competing approaches to psychiatric illness including psychodynamic, biological, molecular, pan-omics, precision, cognitive and phenomenological psychiatry, folk psychology, mind-brain dualism, descriptive psychopathology, and postpsychiatry. The current “gold standard” DSM/ICD taxonomies of mood disorders and schizophrenia are unreliable and preclude to employ a deductive reasoning approach. Therefore, it is not surprising that mood disorders and schizophrenia research was unable to revise the conventional classifications and did not provide more adequate therapeutic approaches. The aim of this paper is to explain the new nomothetic network psychiatry (NNP) approach, which uses machine learning methods to build data-driven causal models of mental illness by ensembling risk-resilience, adverse outcome pathways (AOP), cognitome, brainome, symptomatome, and phenomenome latent scores in a causal model. The latter may be trained, tested and validated with Partial Least Squares (PLS) analysis. This approach not only allows to compute pathway-phenotypes or biosignatures, but also to construct reliable and replicable nomothetic networks, which are, therefore, generalizable as disease models. After integrating the validated feature vectors into a well-fitting nomothetic network, clustering analysis may be applied on the latent variable scores of the R/R, AOP, cognitome, brainome, and phenome latent vectors. This pattern recognition method may expose new (transdiagnostic) classes of patients which if cross-validated in independent samples may constitute new (transdiagnostic) nosological categories.

Psychotic disorders occur as a result of pathobiochemical processes in the brain, which disrupt the central neurotransmission of molecules such as dopamine and glutamate. The dopamine hypothesis, adopted more than 2 decades ago, has repeatedly asserted its position as an etiopathogenetic 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. On the other hand, cognitive impairments in schizophrenia remain not fully understood but are thought to be present during all stages of the disease, as well as in the prodromal the interictal and residual phases. Over the last decade, functional magnetic resonance imaging has focused on research of brain networks like the Default mode network, the Salience network and Central executive network, enabling a deeper understanding of cognitive deficits, as well as other phenomena such as disorganization of thought and behavior. The study of the nodes of these networks, such as the precuneus and insula, informs about their complex significant roles as structures responsible for important cognitive domains such as concentration, attention, ability to understand and reproduce information, as well as memory functions. It is suggested that the neurotransmission of dopamine and glutamate play a key role in these processes and their successful modulation in the correct brain regions through psychopharmacological and biomedical instrumental methods may lead to a significant reversal of conventional paradigms. Pharmaco-magnetic resonance imaging is a neuroimaging method that can provide the 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.

Earliest critical context of the pandemic, preceding the first real epidemiological wave of contagion in Bulgaria, was examined in 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 International Collaboration on Social & Moral Psychology of COVID-19 (ICSMP) in April and May 2020. 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 less 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 less Conspiracy Theories beliefs, Collective Narcissism, Morality-as-Cooperation, Moral Identity, and Psychological Well-Being. Results revealed two polar trends of support and non-support of public health policies. The contribution of this study is in providing evidence for affective polarization and phenomenology of (non)precarity during the outbreak of the pandemic.

Major depression is accompanied by increased IgM-mediated autoimmune responses to oxidative specific epitopes (OSEs). Nevertheless, these responses have not been examined in bipolar disorder type 1 (BP1) and BP2. IgM responses to malondialdehyde (MDA), phosphatidinylinositol, oleic acid, and azelaic acid were determined in 35 healthy controls, and 101 mood disorder patients, namely 47 major depressed (MDD), 29 BP1, and 25 BP2 patients. We also measured serum total peroxides, IgG to oxidized LDL (oxLDL), IgM to nitroso-adducts, and IgM/IgA directed to lipopolysaccharides (LPS). IgM responses to OSEs were significantly higher in MDD and BP1 as compared with controls and 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, IgM directed to OSEs and nitroso-adducts, 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 OSEs, 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 OSEs and nitroso-adducts, 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.

Objective: 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.Methods: 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 Gram-negative bacteria, IgG responses to oxidized low-density lipoprotein (oxLDL) and serum peroxides.Results: 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 oxLDL and IgA/IgM responses to Gram-negative bacteria.Conclusions: 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, etc.

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 have 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 restricted to regions located in 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 the 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, as compared with healthy controls.

The aim of the presents study is to further expand the model of translational cross-validation by investigating the brain circuits or networks which underpin the same tasks by means of group independent component analysis for FMRI toolbox (GIFT). We hypothesized that there will be neural network patterns of activation and deactivation, which correspond to real time performance on clinical self-evaluation scales. We recruited 42 subjects: 20 healthy controls and 22 patients with major depressive episode. All subjects underwent functional MRI scanning using paradigm comprised 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. The results have demonstrated that there exist positively or negatively modulated brain networks during processing of diagnostic specific task questions for major 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 healthy controls. It is for the first time when potential neuroimaging state dependent biomarker 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 ability of both methods to concordantly distinguish groups by means of incremental translational cross-validation.

We have used Mass Multivariate Method on structural, resting state and task related fMRI data from two groups of patients with schizophrenia and depression, respectively, in order to define several regions of significant relevance to the differential diagnosis between those conditions. The regions included the left Planum polare, Left opercular part of the inferior frontal gyrus (OpIFG), Medial orbital gyrus (MOrG), Posterior Insula (PIns), and Parahippocampal gyrus (PHG). This study delivers evidence that multimodal neuroimaging approach can potentially enhance the validity of psychiatric diagnosis. Either structural, or resting state or task related functional MRI modality cannot provide independent biomarkers. Further studies need to consider and implement a model of incremental validity to combine 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.

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), bipolar disorder (BD) and the severity of these disorders. Toward this end, we have performed effective connectivity analysis of resting state functional MRI data in these three patient groups and healthy controls. We have used spectral Dynamic Causal Modeling (spDCM), and the derived connectome features were further subjected to machine learning. The results outlined a model of 5 connections, which discriminate 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), fronto-parietal and dorsal attention network (middle frontal gyrus (MFG) corresponding to dorsolateral prefrontal cortex, 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, 4 out of the 5 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.

Major depression (MDD) is accompanied by higher serum IgM/IgA responses to LPS of Gram-negative bacteria, suggesting increased bacterial translocation and gut dysbiosis. Gut dysbiosis may occur in bipolar disorder (BD) and 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 in 29 BP1, 37 BP2, 44 MDD and 30 healthy individuals. MDD plus BD was best discriminated from controls by increased IgM/IgA responses to Pseudomonas aeruginosa. 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, especially C. koseri. IgG responses to oxidized low-density lipoprotein were significantly associated with signs of increased bacterial translocation. In conclusion, not only MDD but also BP1 and BP2 are accompanied by an immune response due to the increased load of plasma LPS of gut commensal bacteria while these aberrations in the gut-brain axis are most pronounced in BP1 and patients with melancholic features. 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.

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 to reach 90 % accuracy of the prediction.

Aim. In the current study, we aimed at identifying resting-state brain networks, which are different in patients with depression compared to healthy individuals. Moreover, we analyzed the potential for clinical use of different network measures that could discriminate the two groups and thus help the diagnostic process. Method and subjects. We recruited 90 subjects: 49 healthy controls (HC) and 41 patients with a major depressive episode (MDE). All subjects underwent clinical evaluation and functional resting-state MRI. The data were processed to investigate functional connectivity network measures across the two groups using Brain Connectivity Toolbox. The statistical inferences were made at the functional networks level, using false discovery rate method. Independent samples t-test was performed on the network measures mean values to reveal differences between HC and MDE groups. Permutation-based statistical testing was used to test the significance of the difference between the distributions of the network measures by nodes for HC and MDE groups. Linear discriminant analysis was used to differentiate between the groups. Results and discussion. Significant differences in FC between depressed patients and healthy controls were found with the most prominent changes encompassing within-region as well as between-region connectivity of occipital lobe areas such as precuneus (PreCu), cuneus (Cu), superior occipital gyrus (SOG), lingual gyrus (LG), fusiform gyrus (FG), cerebellum, along with limbic structures including the hippocampus (Hipp) and cingulate gyrus. Linear discriminant analysis demonstrated that the full connectivity matrices, as well as those with only the significant connections identified in advance, were the most precise in differentiating between depression and health. These measures reached precision levels of 97% and 94%, respectively. Conclusion. Our study delivered further evidence about impairment of functional connectivity networks in MDE that may contribute to differentiate patients with depression from healthy subjects.

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 (paranoid and depressive) will have distinct connectivity patterns and structural differences.