Diagnostic value of structural, functional and effective connectivity in bipolar disorder.

INTRODUCTION: The aim of this systematic review is to assess the functional magnetic resonance imaging (fMRI) studies of bipolar disorder (BD) patients that characterize differences in terms of structural, functional, and effective connectivity between the patients with BD, patients with other psychiatric disorders and healthy controls as possible biomarkers for diagnosing the disorder using neuroimaging. METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), guidelines a systematic search for recent (since 2015) original studies on connectivity in bipolar disorder was conducted in PUBMED and SCOPUS. RESULTS: A total of 60 studies were included in this systematic review: 20 of the structural connectivity, 33 of the functional connectivity, and only 7 of the studies focused on effective connectivity complied with the inclusion and exclusion criteria. DISCUSSION: Despite the great heterogeneity in the findings, there are several trends that emerge. In structural connectivity studies, the main abnormalities in bipolar disorder patients were in the frontal gyrus, anterior, as well as posterior cingulate cortex and differences in emotion and reward-related networks. Cerebellum (vermis) to cerebrum functional connectivity was found to be the most common finding in BD. Moreover, prefrontal cortex and amygdala connectivity as part of the rich-club hubs were often reported to be disrupted. The most common findings based on effective connectivity were alterations in salience network, default mode network and executive control network. Although more studies with larger sample sizes are needed to ascertain altered brain connectivity as diagnostic biomarker, there is a perspective that the method could be used as a single marker of diagnosis in the future, and the process of adoption could be accelerated by using approaches such as semiunsupervised machine learning.

Group independent components underpin responses to items from a depression scale.

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.

Toward interpretability of machine learning methods for the classification of patients with major depressive disorder based on functional network measures.

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.

Topology switching during window thresholding fMRI-based functional networks of patients with major depressive disorder: Consensus network approach.

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.

Functional MRI Correlates of Stroop N-Back Test Underpin the Diagnosis of Major Depression.

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.

Construction of a nitro-oxidative stress-driven, mechanistic model of mood disorders: A nomothetic network approach.

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.

Does greenery experienced indoors and outdoors provide an escape and support mental health during the COVID-19 quarantine?

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Pharmaco-Magnetic Resonance as a Tool for Monitoring the Medication-Related Effects in the Brain May Provide Potential Biomarkers for Psychotic Disorders.

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.

Upregulation of the nitrosylome in bipolar disorder type 1 (BP1) and major depression, but not BP2: Increased IgM antibodies to nitrosylated conjugates are associated with indicants of leaky gut.

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 LPS of Gram-negative bacteria, IgG responses to oxidized low-density lipoprotein (ox-LDL) 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 ox-LDL 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.

Reduced grey matter volume in frontal and temporal areas in depression: contributions from voxel-based morphometry study.

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.

Response to Pharmacological Treatment in Major Depression Predicted by Electroencephalographic Alpha Power - a Pilot Naturalistic Study.

BACKGROUND: Pharmacological treatment of depression is currently led by the trial and error principle mainly because of lack of reliable biomarkers. Earlier findings suggest that baseline alpha power and asymmetry could differentiate between responders and non-responders to specific antidepressants. AIM: The current study investigated quantitative electroencephalographic (QEEG) measures before and early in treatment as potential response predictors to various antidepressants in a naturalistic sample of depressed patients. We were aiming at developing markers for early prediction of treatment response based on different QEEG measures. MATERIALS AND METHODS: EEG data from 25 depressed subjects were acquired at baseline and after one week of treatment. Mean and total alpha powers were calculated at eight electrode sites F3, F4, C3, C4, P3, P4, O1, O2. Response to treatment was defined as 50% decrease in MADRS score at week 4. RESULTS: Mean P3 alpha predicted response with sensitivity and specificity of 80%, positive and negative predictive values of 92.31% and 71.43%, respectively. The combined model of response prediction using mean baseline P3 alpha and mean week 1 C4 alpha values correctly identified 80% of the cases with sensitivity of 84.62%, and specificity of 71.43%. CONCLUSIONS: Simple QEEG measures (alpha power) acquired before initiation of antidepressant treatment could be useful in outcome prediction with an overall accuracy of about 80%. These findings add to the growing body of evidence that alpha power might be developed as a reliable biomarker for the prediction of antidepressant response.

The Anxious Brain: The Influence of Stress on the Nervous System.

Anxiety disorders, including generalized anxiety, panic disorder, and post-traumatic stress, constitute the most frequent mental disorders and occur in about 14-18% of the overall population [...].

Towards New Methodology for Cross-Validation of Clinical Evaluation Scales and Functional MRI in Psychiatry.

Objective biomarkers have been a critical challenge for the field of psychiatry, where diagnostic, prognostic, and theranostic assessments are still based on subjective narratives. Psychopathology operates with idiographic knowledge and subjective evaluations incorporated into clinical assessment inventories, but is considered to be a medical discipline and, as such, uses medical intervention methods (e.g., pharmacological, ECT; rTMS; tDCS) and, therefore, is supposed to operate with the language and methods of nomothetic networks. The idiographic assessments are provisionally "quantified" into "structured clinical scales" to in some way resemble nomothetic measures. Instead of fostering data merging and integration, this approach further encapsulates the clinical psychiatric methods, as all other biological tests (molecular, neuroimaging) are performed separately, only after the clinical assessment has provided diagnosis. Translational cross-validation of clinical assessment instruments and fMRI is an attempt to address the gap. The aim of this approach is to investigate whether there exist common and specific neural circuits, which underpin differential item responses to clinical self-rating scales during fMRI sessions in patients suffering from the two main spectra of mental disorders: schizophrenia and major depression. The current status of this research program and future implications to promote the development of psychiatry as a medical discipline are discussed.

The novel schizophrenia subgroup "major neurocognitive psychosis" is validated as a distinct class through the analysis of immune-linked neurotoxicity biomarkers and neurocognitive deficits.

Background: Using machine learning methods based on neurocognitive deficits and neuroimmune biomarkers, two distinct classes were discovered within schizophrenia patient samples. Increased frequency of psychomotor retardation, formal thought disorders, mannerisms, psychosis, hostility, excitation, and negative symptoms defined the first subgroup, major neurocognitive psychosis (MNP). Cognitive deficits in executive functions and memory and diverse neuroimmune aberrations were other MNP features. Simple neurocognitive psychosis (SNP) was the less severe phenotype. Aims: The study comprised a sample of 40 healthy controls and 90 individuals diagnosed with schizophrenia, divided into MNP and SNP based on previously determined criteria. Soft Independent Modelling of Class Analogy (SIMCA) was performed using neurocognitive test results and measurements of serum M1 macrophage and T helper-17 cytokines as discriminatory/modelling variables. The model-to-model distances between controls and MNP + SNP and between MNP and SNP were computed, and the top discriminatory variables were established. Results: A notable SIMCA distance of 146.1682 was observed between MNP + SNP and the control group. The top-3 discriminatory variables were lowered motor speed, an activated T helper-17 axis, and lowered working memory. This study successfully differentiated MNP from SNP yielding a SIMCA distance of 19.3. M1 macrophage activation, lowered verbal fluency, and executive functions were the prominent features of MNP versus SNP. Discussion: Based on neurocognitive assessments and the immune-linked neurotoxic M1 and T helper-17 profiles, we found that MNP and SNP are qualitatively distinct classes. Future biomarker research should focus on examining biomarkers specifically in the MNP and SNP subgroups, rather than in the schizophrenia group.

Advances in the Diagnosis and Management of Psychosis.

Psychosis research in the contemporary sense of scientific inquiry may be traced as far as the formulation of the "unitary psychosis" concept, or Einheitpsychose, which is usually attributed to Wilhelm Griesinger, Ernst von Zeller, and Heinrich Neumann [...].

Retrospective Analysis of the Psychological Predictors of Public Health Support in Bulgarians at the Beginning of the Coronavirus Pandemic.

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.

Functional magnetic resonance imaging study of group independent components underpinning item responses to paranoid-depressive scale.

BACKGROUND: Our study expand upon a large body of evidence in the field of neuropsychiatric imaging with cognitive, affective and behavioral tasks, adapted for the functional magnetic resonance imaging (MRI) (fMRI) experimental environment. There is sufficient evidence that common networks underpin activations in task-based fMRI across different mental disorders. AIM: To investigate whether there exist specific neural circuits which underpin differential item responses to depressive, paranoid and neutral items (DN) in patients respectively with schizophrenia (SCZ) and major depressive disorder (MDD). METHODS: 60 patients were recruited with SCZ and MDD. All patients have been scanned on 3T magnetic resonance tomography platform with functional MRI paradigm, comprised of block design, including blocks with items from diagnostic paranoid (DP), depression specific (DS) and DN from general interest scale. We performed a two-sample t-test between the two groups-SCZ patients and depressive patients. Our purpose was to observe different brain networks which were activated during a specific condition of the task, respectively DS, DP, DN. RESULTS: Several significant results are demonstrated in the comparison between SCZ and depressive groups while performing this task. We identified one component that is task-related and independent of condition (shared between all three conditions), composed by regions within the temporal (right superior and middle temporal gyri), frontal (left middle and inferior frontal gyri) and limbic/salience system (right anterior insula). Another component is related to both diagnostic specific conditions (DS and DP) e.g. It is shared between DEP and SCZ, and includes frontal motor/language and parietal areas. One specific component is modulated preferentially by to the DP condition, and is related mainly to prefrontal regions, whereas other two components are significantly modulated with the DS condition and include clusters within the default mode network such as posterior cingulate and precuneus, several occipital areas, including lingual and fusiform gyrus, as well as parahippocampal gyrus. Finally, component 12 appeared to be unique for the neutral condition. In addition, there have been determined circuits across components, which are either common, or distinct in the preferential processing of the sub-scales of the task. CONCLUSION: This study has delivers further evidence in support of the model of trans-disciplinary cross-validation in psychiatry.

Functional Connectivity of the Anterior Cingulate Cortex and the Right Anterior Insula Differentiates between Major Depressive Disorder, Bipolar Disorder and Healthy Controls.

Background: This study aimed to explore possible differences of the whole-brain functional connectivity of the anterior cingulate cortex (ACC) and anterior insula (AI), in a sample of depressed patients with major depressive disorder (MDD), bipolar disorder (BD) and healthy controls (HC). Methods: A hundred and three subjects (nMDD = 35, nBD = 25, and nHC = 43) between the ages of eighteen and sixty-five years old underwent functional magnetic resonance imaging. The CONN Toolbox was used to process and analyze the functional connectivity of the ACC and AI. Results: The comparison between the patients (MDD/BD) and HC yielded increased resting-state functional connectivity (rsFC) between the ACC and the motor and somatosensory cortices (SSC), superior parietal lobule (SPL), precuneus, and lateral occipital cortex, which was driven by the BD group. In addition, hyperconnectivity between the right AI and the motor and SSC was found in BD, as compared to HC. In MDD, as compared to HC, hyperconnectivity between ACC and SPL and the lateral occipital cortex was found, with no statistical rsFC differences for the AI seed. Compared to BD, the MDD group showed ACC-cerebellum hyperconnectivity and a trend for increased rsFC between the right AI and the bilateral superior frontal cortex. Conclusions: Considering the observed hyperconnectivity between the ACC/somatosensory cortex in the patient group, we suggest depression may be related to an impairment of the sensory-discriminative function of the SSC, which results in the phenomenological signature of mental pain in both MDD and BD. These findings suggest that future research should investigate this particular network with respect to motor functions and executive control, as a potential differential diagnostic biomarker for MDD and BD.

Degeneracy and disordered brain networks in psychiatric patients using multivariate structural covariance analyzes.

Introduction: In this study, we applied multivariate methods to identify brain regions that have a critical role in shaping the connectivity patterns of networks associated with major psychiatric diagnoses, including schizophrenia (SCH), major depressive disorder (MDD) and bipolar disorder (BD) and healthy controls (HC). We used T1w images from 164 subjects: Schizophrenia (n = 17), bipolar disorder (n = 25), major depressive disorder (n = 68) and a healthy control group (n = 54). Methods: We extracted regions of interest (ROIs) using a method based on the SHOOT algorithm of the SPM12 toolbox. We then performed multivariate structural covariance between the groups. For the regions identified as significant in t term of their covariance value, we calculated their eigencentrality as a measure of the influence of brain regions within the network. We applied a significance threshold of p = 0.001. Finally, we performed a cluster analysis to determine groups of regions that had similar eigencentrality profiles in different pairwise comparison networks in the observed groups. Results: As a result, we obtained 4 clusters with different brain regions that were diagnosis-specific. Cluster 1 showed the strongest discriminative values between SCH and HC and SCH and BD. Cluster 2 had the strongest discriminative value for the MDD patients, cluster 3 - for the BD patients. Cluster 4 seemed to contribute almost equally to the discrimination between the four groups. Discussion: Our results suggest that we can use the multivariate structural covariance method to identify specific regions that have higher predictive value for specific psychiatric diagnoses. In our research, we have identified brain signatures that suggest that degeneracy shapes brain networks in different ways both within and across major psychiatric disorders.

Conversion and Obsessive-Phobic Symptoms Predict IL-33 and IL-28A Levels in Individuals Diagnosed with COVID-19.

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.