How to construct neuroscience-informed psychiatric classification? Towards nomothetic networks psychiatry

MicroRNA classification and discovery for major depressive disorder diagnosis: Towards a robust and interpretable machine learning approach

BACKGROUND

Major depressive disorder (MDD) is notably underdiagnosed and undertreated due to its complex nature and subjective diagnostic methods. Biomarker identification would help provide a clearer understanding of MDD aetiology. Although machine learning (ML) has been implemented in previous studies to study the alteration of microRNA (miRNA) levels in MDD cases, clinical translation has not been feasible due to the lack of interpretability (i.e. too many miRNAs for consideration) and stability.

METHODS

This study applied logistic regression (LR) model to the blood miRNA expression profile to differentiate patients with MDD (n = 60) from healthy controls (HCs, n = 60). Embedded (L1-regularised logistic regression) feature selector was utilised to extract clinically relevant miRNAs, and optimized for clinical application.

RESULTS

Patients with MDD could be differentiated from HCs with the area under the receiver operating characteristic curve (AUC) of 0.81 on testing data when all available miRNAs were considered (which served as a benchmark). Our LR model selected miRNAs up to 5 (known as LR-5 model) emerged as the best model because it achieved a moderate classification ability (AUC = 0.75), relatively high interpretability (feature number = 5) and stability (ϕ̂Z=0.55) compared to the benchmark. The top-ranking miRNAs identified by our model have demonstrated associations with MDD pathways involving cytokine signalling in the immune system, the reelin signalling pathway, programmed cell death and cellular responses to stress.

CONCLUSION

The LR-5 model, which is optimised based on ML design factors, may lead to a robust and clinically usable MDD diagnostic tool.

Arthur Schopenhauer: An underappreciated philosopher in psychiatry and his applied epistemology of body and self- experience

BACKGROUND

Arthur Schopenhauer (1788-1860) was one of the most important thinkers of the 19th century. Although his writings have exerted great influence in many different disciplines, his epistemological concepts and analysis of the body and self-experience were rarely considered in the context of psychiatric research of schizophrenia spectrum disorders (SSD) and depression (MDD).

METHODS

The method applied for the study of anomalous self and body-experience first consists in the close reading and analysis of Schopenhauer's most influential writings, namely his opus magnus "The World as Will and Representation" and his dissertation "On the Fourfold Root of the Principle of Sufficient Reason". Second, psychopathological and phenomenological aspects of the anomalous body and self, as well as altered space and time experience, are discussed by means of Schopenhauer's philosophy and four patient cases.

RESULTS

Schopenhauer's insights contribute to contemporary psychiatry by (1) unifying materialistic (neurobiological) and idealistic (subjective) conceptions of psychiatric disorders and improving the awareness of methods in psychiatric research; (2) emphasizing the integral significance of the body as a gateway to the surrounding world and basal self-experience; (3) delineating subjective space and time-experience as crucial dimensions of the conditio humana in SSD and MDD; and (4) demonstrating the feasibility of transferring his theories directly to clinical case vignettes stemming from the daily clinical routine.

CONCLUSION

Close reading of Schopenhauer's texts might help bridge the gap between different scientific methods in psychiatry and overcome the translational crisis of contemporary psychiatry by unifying neurobiological and subjective approaches to SSD and MDD.

Towards a major methodological shift in depression research by assessing continuous scores of recurrence of illness, lifetime and current suicidal behaviors and phenome features

BACKGROUND

The binary major depressive disorder (MDD) diagnosis is inadequate and should never be used in research.

AIMS

The study's objective is to explicate our novel precision nomothetic strategy for constructing depression models based on adverse childhood experiences (ACEs), lifetime and current phenome, and biomarker (atherogenicity indices) scores.

METHODS

This study assessed recurrence of illness (ROI: namely recurrence of depressive episodes and suicidal behaviors, SBs), lifetime and current SBs and the phenome of depression, neuroticism, dysthymia, anxiety disorders, and lipid biomarkers including apolipoprotein (Apo)A, ApoB, free cholesterol and cholesteryl esters, triglycerides, high density lipoprotein cholesterol in 67 normal controls and 66 MDD patients. We computed atherogenic and reverse cholesterol transport indices.

RESULTS

We were able to extract one factor from a) the lifetime phenome of depression comprising ROI, and traits such as neuroticism, dysthymia and anxiety disorders, and b) the phenome of the acute phase (based on depression, anxiety and quality of life scores). PLS analysis showed that 55.7 % of the variance in the lifetime + current phenome factor was explained by increased atherogenicity, neglect and sexual abuse, while atherogenicity partially mediated the effects of neglect. Cluster analysis generated a cluster of patients with major dysmood disorder, which was externally validated by increased atherogenicity and characterized by increased scores of all clinical features.

CONCLUSIONS

The outcome of depression should not be represented as a binary variable (MDD or not), but rather as multiple dimensional scores based on biomarkers, ROI, subclinical depression traits, and lifetime and current phenome scores including SBs.

Systemic inflammation and oxidative stress markers in patients with unipolar and bipolar depression: A large-scale study

OBJECTIVE

Numerous studies have demonstrated that neutrophil/HDL ratio (NHR), lymphocyte/HDL ratio (LHR), monocyte/HDL (MHR) ratio, platelet/HDL ratio (PHR), neutrophil/ALB ratio (NAR) and platelet/ALB ratio (PAR) can serve as systemic inflammation and oxidative stress markers in a variety of diseases. However, few studies have estimated the associations of these markers with unipolar depression (UD) and bipolar depression (BD), as well as psychotic symptoms in UD and BD.

METHODS

6297 UD patients, 1828 BD patients and 7630 healthy subjects were recruited. The differences in these indicators among different groups were compared, and the influencing factors for the occurrence of UD or BD and psychotic symptoms were analyzed.

RESULTS

These ratios displayed unique variation patterns across different diagnostic groups. BD group exhibited higher NHR, LHR, MHR, NAR and lower PAR than UD and HC groups, UD group showed higher MHR than HC group. The psychotic UD group had higher NHR, LHR, MHR and NAR than non-psychotic UD group. Higher LHR, MHR, NAR and lower PAR were risk factors in BD when compared to UD group.

CONCLUSIONS

Our study demonstrated differences in inflammation and oxidative stress profile between UD and BD patients, as well as between subjects with or without psychotic symptom exist, highlighting the role of inflammation and oxidative stress in the pathophysiology of UD and BD.

The Gut-Brain Axis and the Microbiome in Anxiety Disorders, Post-Traumatic Stress Disorder and Obsessive-Compulsive Disorder

A large body of research supports the role of stress in several psychiatric disorders in which anxiety is a prominent symptom. Other research has indicated that the gut microbiome-immune system- brain axis is involved in a large number of disorders and that this axis is affected by various stressors. The focus of the current review is on the following stress-related disorders: generalized anxiety disorder, panic disorder, social anxiety disorder, post-traumatic stress disorder and obsessivecompulsive disorder. Descriptions of systems interacting in the gut-brain axis, microbiome-derived molecules and of pro- and prebiotics are given. Preclinical and clinical studies on the relationship of the gut microbiome to the psychiatric disorders mentioned above are reviewed. Many studies support the role of the gut microbiome in the production of symptoms in these disorders and suggest the potential for pro- and prebiotics for their treatment, but there are also contradictory findings and concerns about the limitations of some of the research that has been done. Matters to be considered in future research include longer-term studies with factors such as sex of the subjects, drug use, comorbidity, ethnicity/ race, environmental effects, diet, and exercise taken into account; appropriate compositions of pro- and prebiotics; the translatability of studies on animal models to clinical situations; and the effects on the gut microbiome of drugs currently used to treat these disorders. Despite these challenges, this is a very active area of research that holds promise for more effective, precision treatment of these stressrelated disorders in the future.

Imaging Transcriptomics of the Brain for Schizophrenia

Schizophrenia is a severe mental disorder with a neurodevelopmental origin. Although schizophrenia results from changes in the brain, the underlying biological mechanisms are unknown. Transcriptomics studies quantitative expression changes or qualitative changes of all genes and isoforms, providing a more meaningful biological insight. Magnetic resonance imaging (MRI) techniques play roles in revealing brain structure and function. We give a narrative focused review on the current transcriptome combined with MRI studies related to schizophrenia and summarize the research methodology and content of these studies to identify the research commonalities as well as the implications for future research, in an attempt to provide new insights into the mechanism, clinical diagnosis, and treatments of schizophrenia.

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.

Cognitive biases as an adaptive strategy in autism and schizophrenia spectrum: the compensation perspective on neurodiversity

This article presents a novel theoretical perspective on the role of cognitive biases within the autism and schizophrenia spectrum by integrating the evolutionary and computational approaches. Against the background of neurodiversity, cognitive biases are presented as primary adaptive strategies, while the compensation of their shortcomings is a potential cognitive advantage. The article delineates how certain subtypes of autism represent a unique cognitive strategy to manage cognitive biases at the expense of rapid and frugal heuristics. In contrast, certain subtypes of schizophrenia emerge as distinctive cognitive strategies devised to navigate social interactions, albeit with a propensity for overdetecting intentional behaviors. In conclusion, the paper emphasizes that while extreme manifestations might appear non-functional, they are merely endpoints of a broader, primarily functional spectrum of cognitive strategies. The central argument hinges on the premise that cognitive biases in both autism and schizophrenia spectrums serve as compensatory mechanisms tailored for specific ecological niches.

Metabolome subtyping reveals multi-omics characteristics and biological heterogeneity in major psychiatric disorders

Growing evidence suggests that major psychiatric disorders (MPDs) share common etiologies and pathological processes. However, the diagnosis is currently based on descriptive symptoms, which ignores the underlying pathogenesis and hinders the development of clinical treatments. This highlights the urgency of characterizing molecular biomarkers and establishing objective diagnoses of MPDs. Here, we collected untargeted metabolomics, proteomics and DNA methylation data of 327 patients with MPDs, 131 individuals with genetic high risk and 146 healthy controls to explore the multi-omics characteristics of MPDs. First, differential metabolites (DMs) were identified and we classified MPD patients into 3 subtypes based on DMs. The subtypes showed distinct metabolomics, proteomics and DNA methylation signatures. Specifically, one subtype showed dysregulation of complement and coagulation proteins, while the DNA methylation showed abnormalities in chemical synapses and autophagy. Integrative analysis in metabolic pathways identified the important roles of the citrate cycle, sphingolipid metabolism and amino acid metabolism. Finally, we constructed prediction models based on the metabolites and proteomics that successfully captured the risks of MPD patients. Our study established molecular subtypes of MPDs and elucidated their biological heterogeneity through a multi-omics investigation. These results facilitate the understanding of pathological mechanisms and promote the diagnosis and prevention of MPDs.

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 [...].

Neuroimmune, clinical and treatment challenges in multiple sclerosis-related psychoses

In recent years, epidemiological and genetic studies have shown an association between autoimmune diseases and psychosis. The question arises whether patients with schizophrenia are more likely to develop multiple sclerosis (MS) later in life. It is well known that the immune system plays an important role in the etiopathogenesis of both disorders. Immune disturbances may be similar or very different in terms of different types of immune responses, disturbed myelination, and/or immunogenetic predispositions. A psychotic symptom may be a consequence of the MS diagnosis itself or a separate entity. In this review article, we discussed the timing of onset of psychotic symptoms and MS and whether the use of corticosteroids as therapy for acute relapses in MS is unfairly neglected in patients with psychiatric comorbidities. In addition, we discussed that the anti-inflammatory potential of antipsychotics could be useful and should be considered, especially in the treatment of psychosis that coexists with MS. Autoimmune disorders could precipitate psychotic symptoms, and in this context, autoimmune psychosis must be considered as a persistent symptomatology that requires continuous and specific treatment.

Research and Diagnostic Algorithmic Rules (RADAR) for mood disorders, recurrence of illness, suicidal behaviours, and the patient's lifetime trajectory

The top-down Diagnostic and Statistical Manual/International Statistical Classification of Diseases categories of mood disorders are inaccurate, and their dogmatic nature precludes both deductive (as indisputable) and inductive (as top-down) remodelling of case definitions. In trials, psychiatric rating scale scores employed as outcome variables are invalid and rely on folk psychology-like narratives. Using machine learning techniques, we developed a new precision nomothetic model of mood disorders with a recurrence of illness (ROI) index, a new endophenotype class, namely Major Dysmood Disorder (MDMD), characterised by increased ROI, a more severe phenome, and more disabilities. Nonetheless, our previous studies did not compute Research and Diagnostic Algorithmic Rules (RADAR) to diagnose MDMD and score ROI, lifetime (LT), and current suicidal behaviours, as well as the phenome of mood disorders. Here, we provide rules to compute bottom-up RADAR scores for MDMD, ROI, LT and current suicidal ideation and attempts, the phenome of mood disorders, and the lifetime trajectory of mood disorder patients from a family history of mood disorders and substance abuse to adverse childhood experiences, ROI, and the phenome. We also demonstrate how to plot the 12 major scores in a single RADAR graph, which displays all features in a two-dimensional plot. These graphs allow the characteristics of a patient to be displayed as an idiomatic fingerprint, allowing one to estimate the key traits and severity of the illness at a glance. Consequently, biomarker research into mood disorders should use our RADAR scores to examine pan-omics data, which should be used to enlarge our precision models and RADAR graph.

Cytokine level in patients with mood disorder, alcohol use disorder and their comorbidity

OBJECTIVES

Because alcohol use disorder (AUD) is often accompanied by mood disorder (MD) and both alcoholism and depression result in activation of the immune system, this study compares serum cytokine levels in the presence of co-morbidity with those in either AUD or MD alone.

METHODS

In this naturalistic prospective study the levels of 15 different cytokines were measured in serum samples of patients with MD (n = 43), participants with combined AUD-MD (n = 44) and AUD without MD (n = 42). The levels were compared cross-sectionally among themselves and with those in 50 healthy volunteers.

RESULTS

Pro-inflammatory IFN-2α levels were consistently significantly higher and anti-inflammatory IL-1RA significantly lower in all study groups in comparison to healthy volunteers. In the MD only group we found increased IL-6 (p = 0.001), IL-7 (p = 0.001) and IL-13 (p = 0.006) levels, and decreased TNFα (p = 0.0001), IL-1RA (p = 0.012), IL-10 (p = 0.002) compared with group MD + AUD. Patients with AUD only showed elevated levels of IL-1β (p = 0.046), IL-2 (p = 0.004), IL-7 (p = 0.0001), IL-4 (p = 0.049) and IL-13 (p = 0.015) in contrast with MD + AUD group.

CONCLUSIONS

Because the interactions of alcohol with peripheral and cerebral immune systems are multifaceted, the pertinent connection to the mechanism how alcohol consumption contributes to the development of mood disorders cannot be properly explored.

In Schizophrenia, Chronic Fatigue Syndrome- and Fibromyalgia-Like Symptoms are Driven by Breakdown of the Paracellular Pathway with Increased Zonulin and Immune Activation-Associated Neurotoxicity

BACKGROUND

A meaningful part of schizophrenia patients suffer from physiosomatic symptoms (formerly named psychosomatic), which are reminiscent of chronic fatigue syndrome and fibromyalgia (FF) and are associated with signs of immune activation and increased levels of tryptophan catabolites (TRYCATs).

AIMS

The study aims to examine whether FF symptoms in schizophrenia are associated with the breakdown of the paracellular pathway, zonulin, lowered natural IgM responses to oxidative specific epitopes (OSEs); and whether FF symptoms belong to the behavioral-cognitive-physical-psychosocial- (BCPS)-worsening index consisting of indices of a general cognitive decline (G-CoDe), symptomatome of schizophrenia, and quality of life (QoL)-phenomenome.

METHODS

FF symptoms were assessed using the Fibromyalgia and Chronic Fatigue Rating scale in 80 schizophrenia patients and 40 healthy controls and serum cytokines/chemokines, IgA levels to TRYCATs, IgM to OSEs, zonulin and transcellular/paracellular (TRANS/PARA) molecules were assayed using ELISA methods.

RESULTS

A large part (42.3%) of the variance in the total FF score was explained by the regression on the PARA/TRANS ratio, pro-inflammatory cytokines, IgM to zonulin, IgA to TRYCATs (all positively), and IgM to OSEs (inversely). There were highly significant correlations between the total FF score and G-CoDe, symtopmatome, QoL phenomenome, and BCPS-worsening score. FF symptoms belong to a common core shared by G-CoDe, symtopmatome, and QoL phenomenome.

CONCLUSION

The physio-somatic symptoms of schizophrenia are driven by various pathways, including increased zonulin, breakdown of the paracellular tight-junctions pathway, immune activation with induction of the TRYCAT pathway, and consequent neurotoxicity. It is concluded that FF symptoms are part of the phenome of schizophrenia and BCPS-worsening as well.

Bipolar disorder in the International Classification of Diseases-Eleventh version: A review of the changes, their basis, and usefulness

The World Health Organization’s 11^th revision of the International Classification of Diseases (ICD-11) including the chapter on mental disorders has come into effect this year. This review focuses on the “Bipolar or Related Disorders” section of the ICD-11 draft. It describes the benchmarks for the new version, particularly the foremost principle of clinical utility. The alterations made to the diagnosis of bipolar disorder (BD) are evaluated on their scientific basis and clinical utility. The change in the diagnostic requirements for manic and hypomanic episodes has been much debated. Whether the current criteria have achieved an optimum balance between sensitivity and specificity is still not clear. The ICD-11 definition of depressive episodes is substantially different, but the lack of empirical support for the changes has meant that the reliability and utility of bipolar depression are relatively low. Unlike the Diagnostic and Statistical Manual of Mental Disorders, 5^th edition (DSM-5), the ICD-11 has retained the category of mixed episodes. Although the concept of mixed episodes in the ICD-11 is not perfect, it appears to be more inclusive than the DSM-5 approach. Additionally, there are some uncertainties about the guidelines for the subtypes of BD and cyclothymic disorder. The initial results on the reliability and clinical utility of BD are promising, but the newly created diagnostic categories also appear to have some limitations. Although further improvement and research are needed, the focus should now be on facing the challenges of implementation, dissemination, and education and training in the use of these guidelines.

How to define today a medical disorder? Biological and psychosocial disadvantages as the paramount criteria

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.

A pathway phenotype linking metabolic, immune, oxidative, and opioid pathways with comorbid depression, atherosclerosis, and unstable angina

BACKGROUND

There is strong comorbidity between atherosclerosis (ATS) and depression which is attributed to increased atherogenicity, insulin resistance (IR), and immune and oxidative stress.

AIM OF THE STUDY

To examine the role of the above pathways and mu-opioid receptor (MOR), β-endorphin levels, zinc, copper, vitamin D3, calcium, and magnesium in depression due to ATS/unstable angina (UA).

METHODS

Biomarkers were assayed in 58 controls and 120 ATS patients divided into those with moderate and severe depression according to the Beck Depression Inventory-II (BDI-II) scores >19 and >29, respectively.

RESULTS

Neural network and logistic regression models showed that severe depression due to ATS/UA was best predicted by interleukin-6 (IL-6), UA, MOR, zinc, β-endorphin, calcium and magnesium, and that moderate depression was associated with IL-6, zinc, MOR, β-endorphin, UA, atherogenicity, IR, and calcium. Neural networks yielded a significant discrimination of severe and moderate depression with an area under the receiver operating curves of 0.831 and 0.931, respectively. Using Partial Least Squares path analysis, we found that 66.2% of the variance in a latent vector extracted from ATS/UA clinical features, and the BDI-II scores, atherogenicity, and IR could be explained by the regression on IL-6, IL-10, zinc, copper, calcium, MOR, and age. The BDI-II scores increased from controls to ATS to UA class III to UA class IV.

CONCLUSIONS

Immune activation, the endogenous opioid system, antioxidants, trace elements, and macrominerals modulate a common core shared by increased depressive symptoms, ATS, UA, atherogenicity, and IR.

Integrity of the editing and publishing process is the basis for improving an academic journal’s Impact Factor

BACKGROUND

Journal Impact Factor™ (JIF) is often used to evaluate the relative reputation and quality of academic journals in their respective fields, and can greatly influence the quality and scope of subsequent manuscript submissions. Therefore, many if not all academic journals are interested in increasing their JIF, to improve their academic impact.

AIM

To determine the importance of the integrity of the editorial and publication process in improving the academic influence of academic journals and the JIF of academic journals.

METHODS

In this paper, we describe our statistical analysis of bibliometric factors - including the 2021 JIFs released in the Journal Citation Report™ 2022, discipline rankings, received and published articles in 2019-2021, and webpage visits and downloads - for seven journals published by Baishideng Publishing Group (Baishideng) and indexed in Science Citation Index Expanded™; ultimately, we introduce and discuss the editing and publishing processes of Baishideng’s journals in their entirety, as they form the basis for our objective of safeguarding and bolstering integrity in academic publication.

RESULTS

For the seven journals assessed, their 2021 JIFs were basically unchanged from 2020, with the current metric ranging from 5.374 for World Journal of Gastroenterology (WJG) to 1.534 for World Journal of Clinical Cases (WJCC). Further assessments of the journals’ bibliometrics from 2019 to 2020, showed that World Journal of Stem Cells has the highest self-citation rate (1.43%) and World Journal of Gastrointestinal Surgery has the lowest (0.21%). Additionally, the total 3012 articles published during this period were cited by more than 20000 articles in approximately 8000 academic journals. Of note, the 1102 articles published in WJG were cited by articles in 3059 journals, among which 171 journals have a JIF of > 10, including internationally renowned academic journals such as CA-A Cancer Journal for Clinicians (2021 JIF 286.130, record count: 1), Lancet (2021 JIF 202.731, record count: 4), Nature Reviews Immunology (2021 JIF 108.555, record count: 2), Nature Reviews Gastroenterology & Hepatology (2021 JIF 73.082, record count: 9), Lancet Gastroenterology & Hepatology (2021 JIF 45.042, record count: 8), Gastroenterology (2021 JIF 33.883, record count: 19), and Gut (2021 JIF 31.793, record count: 21). This suggests that Baishideng’s journals have been widely recognized for their academic quality. In the Reference Citation Analysis (RCA) database, all seven Baishideng-published journals obtained a 2022 Journal Article Influence Index (JAII). For example, WJG has a 2022 JAII of 22.048, ranking 18^th out of 102 journals in the field of gastroenterology & hepatology in the RCA, with 469909 total citations (6/102) and 21313 total articles (5/102). The numbers of manuscripts received and published in 2021 were both higher than those in 2019-2020. For example, WJCC received a total of 3650 manuscripts in 2021, which is 91.1% higher than those in 2019-2020 (average: 1910 papers/year). In 2021, WJCC published 1296 articles, representing an increase of 105.1% compared to those in 2019-2020 (average: 632 articles/year). The numbers of webpage visits and downloads received by the seven journals have increased year by year. For example, the number of total visits received by WJG in 2019-2021 was 1974052 in 2019, 2317835 in 2020 (increased by 17.4% compared with that in 2019), and 2652555 in 2021 (increased by 4.4% compared with that in 2020). The visitors were from more than 220 countries and regions worldwide, such as the United States, China, and the United Kingdom. Open access (OA) plays a vital role in improving the quality, efficiency, transparency, and integrity of academic journal publishing. From 2019 to 2021, a total of 5543 OA articles were published in the seven journals, of which 2083 (37.6%) were invited and published free-of-charge. During the same period, 1683 articles were published in WJG, and the authors were from more than 70 countries and regions. For the total 5543 articles published in the seven journals from 2019 to 2021, 3903 article quality tracking reports were received after the online publication of these articles. The quality of the articles was further evaluated through the Baishideng’s article quality and author evaluation tracking system, with 4655 articles (84.0%) having received author evaluation and feedback, which contributes to tracking metrics for authors’ satisfaction with the collective publication processes. From March 25, 2021 to June 28, 2022, the seven journals received a total of 424 reader evaluations and 229 letters from readers; this subsequent reader engagement demonstrates that the popularity of the published articles and the volume of their readership audience were improved through the reader evaluation system.

CONCLUSION

Ultimately, the findings from our bibliometric assessments indicate that establishing, promoting and actively practicing processes that safeguard and bolster the integrity of the editing and publication process also help to improve the academic influence of academic journals, which itself is the cornerstone for improving JIF.

Major neurocognitive psychosis: a novel schizophrenia endophenotype class that is based on machine learning and resembles Kraepelin's and Bleuler's conceptions

The purpose of this study is to describe how to use the precision nomothetic psychiatry approach to (a) delineate the associations between schizophrenia symptom domains, including negative symptoms, psychosis, hostility, excitation, mannerism, formal thought disorders, psychomotor retardation (PHEMFP), and cognitive dysfunctions and neuroimmunotoxic and neuro-oxidative pathways and (b) create a new endophenotype class based on these features. We show that all symptom domains (negative and PHEMFP) may be used to derive a single latent trait called overall severity of schizophrenia (OSOS). In addition, neurocognitive test results may be used to extract a general cognitive decline (G-CoDe) index, based on executive function, attention, semantic and episodic memory, and delayed recall scores. According to partial least squares analysis, the impacts of adverse outcome pathways (AOPs) on OSOS are partially mediated by increasing G-CoDe severity. The AOPs include neurotoxic cytokines and chemokines, oxidative damage to proteins and lipids, IgA responses to neurotoxic tryptophan catabolites, breakdown of the vascular and paracellular pathways with translocation of Gram-negative bacteria, and insufficient protection through lowered antioxidant levels and impairments in the innate immune system. Unsupervised machine learning identified a new schizophrenia endophenotype class, named major neurocognitive psychosis (MNP), which is characterised by increased negative symptoms and PHEMFP, G-CoDe and the above-mentioned AOPs. Based on these pathways and phenome features, MNP is a distinct endophenotype class which is qualitatively different from simple psychosis (SP). It is impossible to draw any valid conclusions from research on schizophrenia that ignores the MNP and SP distinctions.

Utilizing Pharmacogenomics Results to Determine Opioid Appropriateness and Improve Pain Management in a Patient with Osteoarthritis

The opioid epidemic in the United States has exposed the need for providers to limit opioid dispensing and identify at-risk patients prior to prescribing opioids. With pharmacogenomic testing, clinicians can analyze hundreds of medications—including commonly prescribed opioids—against genetic results to understand and predict risk and response. Moreover, knowledge of genotypic variants and altered function can help decrease trial and error prescribing, identify patients at-risk for adverse drug events, and improve pain control. This patient case demonstrates how pharmacogenomic test results identified drug–gene interactions and provided insight about a patient’s inadequate opioid therapy response. With pharmacogenomic information, the patient’s healthcare team discontinued opioid therapy and selected a more appropriate regimen for osteoarthritis (ie, celecoxib), resulting in improved pain control and quality of life.

Systems consolidation and fear memory generalisation as a potential target for trauma-related disorders

Fear memory generalisation is a central hallmark in the broad range of anxiety and trauma-related disorders. Recent findings suggest that fear generalisation is closely related to hippocampal dependency during retrieval. In this review, we describe the current understanding about memory generalisation and its potential influence in fear attenuation through pharmacological and behavioural interventions. In light of systems consolidation framework, we propose that keeping memory precision could be a key step to enhance therapeutic outcomes.

Depressive symptoms due to stroke are strongly predicted by the volume and location of the cerebral infarction, white matter hyperintensities, hypertension, and age: A precision nomothetic psychiatry analysis

OBJECTIVES

To delineate the effects of white matter hyperintensities (WMHs) as measured by Fluid-attenuated inversion recovery (FLAIR) and infarction volume as measured by Diffusion-weighted imaging (DWI) on post-stroke depression symptoms.

METHODS

Baseline National Institutes of Health Stroke Score (NIHSS) and Modified Rankin Scale (mRS) scores, and FLAIR and DWI MRIs to assess WMHs and acute infarct volumes, respectively, were assessed in 47 patients (≥55 years) with acute ischemic stroke and 17 normal controls. The Montgomery-Åsberg Depression Rating Scale (MDRS) was assessed three months after the stroke.

RESULTS

The MADRS score was significantly increased in stroke patients as compared with normal controls. The MADRS scale is not unidimensional and cannot be used as an accurate indicator of depression severity in stroke patients. Three months after stroke, key depressive (sadness and inability to feel) and concentration-tension symptoms, and lassitude are significantly predicted by the infarct volume. Right side infarction strongly predicts key depressive symptoms and left side infarction strongly predicts concentration-tension and lassitude scores. Total WMHs significantly predict key depressive and concentration-tension symptoms, and lassitude, with these effects being mediated by right and left DWI stroke volumes and associated disabilities.

CONCLUSIONS

Interactions between age, hypertension, a chronic atherosclerotic process, and acute stroke account for the onset of key depressive symptoms three months after the acute infarct. Chronic and acute neuro-immune and neuro-oxidative stress pathways associated with the formation of WMHs and acute stroke may explain the incidence of post-stroke key depressive and concentration-tension symptoms, and lassitude.

Interactions Among Brain-Derived Neurotrophic Factor and Neuroimmune Pathways Are Key Components of the Major Psychiatric Disorders

The purpose of this review is to summarize the current knowledge regarding the reciprocal associations between brain-derived neurotrophic factor (BDNF) and immune-inflammatory pathways and how these links may explain the involvement of this neurotrophin in the immune pathophysiology of mood disorders and schizophrenia. Toward this end, we delineated the protein-protein interaction (PPI) network centered around BDNF and searched PubMed, Scopus, Google Scholar, and Science Direct for papers dealing with the involvement of BDNF in the major psychosis, neurodevelopment, neuronal functions, and immune-inflammatory and related pathways. The PPI network was built based on the significant interactions of BDNF with neurotrophic (NTRK2, NTF4, and NGFR), immune (cytokines, STAT3, TRAF6), and cell-cell junction (CTNNB, CDH1) DEPs (differentially expressed proteins). Enrichment analysis shows that the most significant terms associated with this PPI network are the tyrosine kinase receptor (TRKR) and Src homology region two domain-containing phosphatase-2 (SHP2) pathways, tyrosine kinase receptor signaling pathways, positive regulation of kinase and transferase activity, cytokine signaling, and negative regulation of the immune response. The participation of BDNF in the immune response and its interactions with neuroprotective and cell-cell adhesion DEPs is probably a conserved regulatory process which protects against the many detrimental effects of immune activation and hyperinflammation including neurotoxicity. Lowered BDNF levels in mood disorders and schizophrenia (a) are associated with disruptions in neurotrophic signaling and activated immune-inflammatory pathways leading to neurotoxicity and (b) may interact with the reduced expression of other DEPs (CTNNB1, CDH1, or DISC1) leading to multiple aberrations in synapse and axonal functions.

False dogmas in mood disorders research: Towards a nomothetic network approach

The current understanding of major depressive disorder (MDD) and bipolar disorder (BD) is plagued by a cacophony of controversies as evidenced by competing schools to understand MDD/BD. The DSM/ICD taxonomies have cemented their status as the gold standard for diagnosing MDD/BD. The aim of this review is to discuss the false dogmas that reign in current MDD/BD research with respect to the new, data-driven, machine learning method to model psychiatric illness, namely nomothetic network psychiatry (NNP). This review discusses many false dogmas including: MDD/BD are mind-brain disorders that are best conceptualized using a bio-psycho-social model or mind-brain interactions; mood disorders due to medical disease are attributable to psychosocial stress or chemical imbalances; DSM/ICD are the gold standards to make the MDD/BD diagnosis; severity of illness should be measured using rating scales; clinical remission should be defined using threshold values on rating scale scores; existing diagnostic BD boundaries are too restrictive; and mood disorder spectra are the rule. In contrast, our NNP models show that MDD/BD are not mind-brain or psycho-social but systemic medical disorders; the DSM/ICD taxonomies are counterproductive; a shared core, namely the reoccurrence of illness (ROI), underpins the intertwined recurrence of depressive and manic episodes and suicidal behaviors; mood disorders should be ROI-defined; ROI mediates the effects of nitro-oxidative stress pathways and early lifetime trauma on the phenome of mood disorders; severity of illness and treatment response should be delineated using the NNP-derived causome, pathway, ROI and integrated phenome scores; and MDD and BD are the same illness.

Adverse Childhood Experiences Predict the Phenome of Affective Disorders and These Effects Are Mediated by Staging, Neuroimmunotoxic and Growth Factor Profiles

Adverse childhood experiences (ACEs) enhance pro-inflammatory and pro-oxidant responses. In affective disorders, recent precision nomothetic psychiatry studies disclosed new pathway phenotypes, including an ROI—reoccurrence of illness (ROI)—oxidative stress latent construct. The aim of the present study is to delineate a) whether ACEs sensitize the M1 macrophage, the T helper cells (Th)1, Th2, and Th17, the IRS (immune-inflammatory-responses system), the CIRS (compensatory immunoregulatory system), and the neuroimmunotoxic and growth factor (GF) profiles and whether they are associated with ROI and the phenome of affective disorders and b) the molecular pathways underpinning the effects of the ACEs. We collected supernatants of stimulated (5 μg/mL of PHA and 25 μg/mL of LPS) and unstimulated diluted whole blood in 20 healthy controls and 30 depressed patients and measured a panel of 27 cytokines/GF using a Luminex method. ACEs (comprising mental and physical trauma, mental neglect, domestic violence, family history of mental disease, and parent loss) are accompanied by the increased stimulated, but not unstimulated, production of M1, Th1, Th2, Th17, IRS, neuroimmunotoxic, and GF profiles and are strongly correlated with ROI and the phenome. A latent vector extracted from the ROI features (recurrent episodes and suicidal behaviors) and the IRS/neuroimmunotoxic/GF profiles explains 66.8% of the variance in the phenome and completely mediates the effects of ACEs on the phenome. Enrichment analysis showed that the ACE-associated sensitization of immune/GF profiles involves JAK-STAT, nuclear factor-κB, tumor necrosis factor-α, G-protein coupled receptor, PI3K/Akt/RAS/MAPK, and hypoxia signaling. In summary, the ACE-induced sensitization of immune pathways and secondary immune hits predicts the phenome of affective disorders.

A Novel Pathway Phenotype of Temporal Lobe Epilepsy and Comorbid Psychiatric Disorders: Results of Precision Nomothetic Medicine

No precision medicine models of temporal lobe epilepsy (TLE) and associated mental comorbidities have been developed to date. This observational study aimed to develop a precision nomothetic, data-driven comorbid TLE model with endophenotype classes and pathway phenotypes that may have prognostic and therapeutical implications. We recruited forty healthy controls and 108 TLE patients for this research and assessed TLE and psychopathology (PP) features as well as oxidative stress (OSTOX, e.g., malondialdehyde or MDA, lipid hydroperoxides, and advanced oxidation protein products) and antioxidant (paraoxonase 1 or PON1 status, -SH groups, and total radical trapping potential or TRAP) biomarkers. A large part (57.2%) of the variance in a latent vector (LV) extracted from the above TLE and PP features was explained by these OSTOX and antioxidant biomarkers. The PON1 Q192R genetic variant showed indirect effects on this LV, which were completely mediated by PON1 activity and MDA. Factor analysis showed that a common core could be extracted from TLE, PP, OSTOX and antioxidant scores, indicating that these features are manifestations of a common underlying construct, i.e., a novel pathway phenotype of TLE. Based on the latter, we constructed a new phenotype class that is characterized by increased severity of TLE, PP and OSTOX features and lowered antioxidant defenses. A large part of the variance in episode frequency was explained by increased MDA, lowered antioxidant, and nitric oxide metabolite levels. In conclusion, (a) PP symptoms belong to the TLE phenome, and the signal increased severity; and (b) cumulative effects of aldehyde formation and lowered antioxidants determine epileptogenic kindling.

In COVID-19, NLRP3 inflammasome genetic variants are associated with critical disease and these effects are partly mediated by the sickness symptom complex: a nomothetic network approach

In coronavirus disease (COVID-19), the nucleotide-binding domain, leucine-rich repeat and pyrin domain-containing protein 3 (NLRP3) inflammasome is activated in response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Acute infections are accompanied by a sickness symptom complex (SSC) which is highly conserved and protects against infections and hyperinflammation. The aim of this study is to delineate the associations of COVID-19, SSC and NLPR3 rs10157379 T > C and NLPR3 rs10754558 C > G variants; and the protective role of SSC in SARS-CoV-2 infection. We recruited COVID-19 patients, 308 with critical, 63 with moderate and 157 with mild disease. Increased SSC protects against SARS, critical disease, and death due to COVID-19. Increasing age, male sex and rs10754558 CG significantly reduce SSC protection. The rs10157379 CT and rs10754558 GG genotypes are positively associated with SARS. Partial Least Squares analysis shows that a) 41.8% of the variance in critical COVID-19 symptoms is explained by SSC and oxygen saturation (inversely associated), inflammation, chest computed tomography abnormalities, increased body mass index, SARS and age (positively associated); and b) the effects of the NLRP3 rs10157379 and rs10754558 variants on critical COVID-19 are mediated via SSC (protective) and SARS (detrimental). SSC includes anosmia and dysgeusia, and maybe gastrointestinal symptoms. In conclusion, intersections among the rs10754558 variant, age, and sex increase risk towards critical COVID-19 by attenuating SSC. NLRP3 variants play an important role in SARS, and severe and critical COVID-19 especially in elderly male individuals with reduced SSC and with increased BMI, hypertension, and diabetes type 2.

The Immune Profile of Major Dysmood Disorder: Proof of Concept and Mechanism Using the Precision Nomothetic Psychiatry Approach

Major depressive disorder and a major depressive episode (MDD/MDE) are characterized by activation of the immune-inflammatory response system (IRS) and the compensatory immune-regulatory system (CIRS). In MDD/MDE, recent precision nomothetic psychiatry studies discovered a new endophenotype class, namely major dysmood disorder (MDMD), a new pathway phenotype, namely reoccurrence of illness (ROI), and a new model of the phenome of depression. The aim of the present study is to examine the association between ROI, the phenome of depression, and MDMD’s features and IRS, CIRS, macrophages (M1), T helper (Th)1, Th2, Th17, T regulatory, and growth factor (GF) profiles. Culture supernatants of unstimulated and stimulated (5 μg/mL of PHA and 25 μg/mL of LPS) diluted whole blood of 30 MDD/MDE patients and 20 controls were assayed for cytokines/GF using the LUMINEX assay. MDMD was characterized by increased M1, Th1, Th2, Th17, Treg, IRS, CIRS, neurotoxicity, and GF profiles. Factor analysis shows that ROI features and immune-GF profiles may be combined into a new pathway phenotype (an extracted latent vector). ROI, lifetime and recent suicidal behaviors, and severity of depression are significantly associated with immunotoxicity and GF profiles. Around 80.0% of the variance in the phenome is predicted by ROI and neurotoxicity or the IRS/CIRS ratio. The molecular pathways underpinning ROI-associated sensitization of immune/growth networks are transmembrane receptor protein kinase-triggered STAT protein phosphorylation, TLR/NF-κB, JAK-STAT, and the main proliferation/survival PI3K/Akt/RAS/MAPK pathway. In conclusion, MDMD’s heightened immune responses are the consequence of ROI-associated sensitization combined with immunostimulatory triggers.

Pathway Phenotypes Underpinning Depression, Anxiety, and Chronic Fatigue Symptoms Due to Acute Rheumatoid Arthritis: A Precision Nomothetic Psychiatry Analysis

Rheumatoid arthritis (RA) is a chronic inflammatory and autoimmune disorder which affects the joints in the wrists, fingers, and knees. RA is often associated with depressive and anxiety symptoms as well as chronic fatigue syndrome (CFS)-like symptoms. This paper examines the association between depressive symptoms (measured with the Beck Depression Inventory, BDI), anxiety (Hamilton Anxiety Rating Scale, HAMA), CFS-like (Fibro-fatigue Scale) symptoms and immune–inflammatory, autoimmune, and endogenous opioid system (EOS) markers, and lactosylcer-amide (CD17) in RA. The serum biomarkers were assayed in 118 RA and 50 healthy controls. Results were analyzed using the new precision nomothetic psychiatry approach. We found significant correlations between the BDI, FF, and HAMA scores and severity of RA, as assessed with the DAS28-4, clinical and disease activity indices, the number of tender and swollen joints, and patient and evaluator global assessment scores. Partial least squares analysis showed that 69.7% of the variance in this common core underpinning psychopathology and RA symptoms was explained by immune–inflammatory pathways, rheumatoid factor, anti-citrullinated protein antibodies, CD17, and mu-opioid receptor levels. We constructed a new endophenotype class comprising patients with very high immune–inflammatory markers, CD17, RA, affective and CF-like symptoms, and tobacco use disorder. We extracted a reliable and replicable latent vector (pathway phenotype) from immune data, psychopathology, and RA-severity scales. Depression, anxiety, and CFS-like symptoms due to RA are manifestations of the phenome of RA and are mediated by the effects of the same immune–inflammatory, autoimmune, and other pathways that underpin the pathophysiology of RA.

Precision Nomothetic Medicine in Depression Research: A New Depression Model, and New Endophenotype Classes and Pathway Phenotypes, and A Digital Self

Machine learning approaches, such as soft independent modeling of class analogy (SIMCA) and pathway analysis, were introduced in depression research in the 1990s (Maes et al.) to construct neuroimmune endophenotype classes. The goal of this paper is to examine the promise of precision psychiatry to use information about a depressed person’s own pan-omics, environmental, and lifestyle data, or to tailor preventative measures and medical treatments to endophenotype subgroups of depressed patients in order to achieve the best clinical outcome for each individual. Three steps are emerging in precision medicine: (1) the optimization and refining of classical models and constructing digital twins; (2) the use of precision medicine to construct endophenotype classes and pathway phenotypes, and (3) constructing a digital self of each patient. The root cause of why precision psychiatry cannot develop into true sciences is that there is no correct (cross-validated and reliable) model of clinical depression as a serious medical disorder discriminating it from a normal emotional distress response including sadness, grief and demoralization. Here, we explain how we used (un)supervised machine learning such as partial least squares path analysis, SIMCA and factor analysis to construct (a) a new precision depression model; (b) a new endophenotype class, namely major dysmood disorder (MDMD), which is a nosological class defined by severe symptoms and neuro-oxidative toxicity; and a new pathway phenotype, namely the reoccurrence of illness (ROI) index, which is a latent vector extracted from staging characteristics (number of depression and manic episodes and suicide attempts), and (c) an ideocratic profile with personalized scores based on all MDMD features.

Application of Mass Multivariate Analysis on Neuroimaging Data Sets for Precision Diagnostics of Depression

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.

Intersections between Copper, β-Arrestin-1, Calcium, FBXW7, CD17, Insulin Resistance and Atherogenicity Mediate Depression and Anxiety Due to Type 2 Diabetes Mellitus: A Nomothetic Network Approach

Type 2 diabetes mellitus (T2DM) is frequently accompanied by affective disorders with a prevalence of comorbid depression of around 25%. Nevertheless, the biomarkers of affective symptoms including depression and anxiety due to T2DM are not well established. The present study delineated the effects of serum levels of copper, zinc, β-arrestin-1, FBXW7, lactosylceramide (LacCer), serotonin, calcium, magnesium on severity of depression and anxiety in 58 men with T2DM and 30 healthy male controls beyond the effects of insulin resistance (IR) and atherogenicity. Severity of affective symptoms was assessed using the Hamilton Depression and Anxiety rating scales. We found that 61.7% of the variance in affective symptoms was explained by the multivariate regression on copper, β-arrestin-1, calcium, and IR coupled with atherogenicity. Copper and LacCer (positive) and calcium and BXW7 (inverse) had significant specific indirect effects on affective symptoms, which were mediated by IR and atherogenicity. Copper, β-arrestin-1, and calcium were associated with affective symptoms above and beyond the effects of IR and atherogenicity. T2DM and affective symptoms share common pathways, namely increased atherogenicity, IR, copper, and β-arrestin-1, and lowered calcium, whereas copper, β-arrestin-1, calcium, LacCer, and FBXW7 may modulate depression and anxiety symptoms by affecting T2DM.

Effective Connectivity between Major Nodes of the Limbic System, Salience and Frontoparietal Networks Differentiates Schizophrenia and Mood Disorders from Healthy Controls

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.

In (deficit) schizophrenia, a general cognitive decline partly mediates the effects of neuro-immune and neuro-oxidative toxicity on the symptomatome and quality of life

BACKGROUND

Schizophrenia and deficit schizophrenia are accompanied by neurocognitive impairments. The aim of this study was to examine whether a general factor underpins impairments in key Cambridge Neuropsychological Test Automated Battery (CANTAB) probes, verbal fluency test (VFT), world list memory (WLM), True Recall, and mini mental state examination (MMSE).

METHODS

We recruited 80 patients with schizophrenia and 40 healthy controls. All patients were assessed using CANTAB tests, namely paired-association learning, rapid visual information processing, spatial working memory, one touch stockings of Cambridge, intra/extradimensional set-shifting (IED), and emotional recognition test.

RESULTS

We found that a general factor, which is essentially unidimensional, underlies those CANTAB, VFT, WLM, True Recall, and MMSE scores. This common factor shows excellent psychometric properties and fits a reflective model and, therefore, reflects a general cognitive decline (G-CoDe) comprising deficits in semantic and episodic memory, recall, executive functions, strategy use, rule acquisition, visual sustained attention, attentional set-shifting, and emotional recognition. Partial least squares analysis showed that 40.5% of the variance in G-CoDe is explained by C-C motif ligand 11, IgA to tryptophan catabolites, and increased oxidative toxicity, and that G-CoDe explains 44.8% of the variance in a general factor extracted from psychosis, hostility, excitation, mannerism, negative symptoms, formal thought disorders, and psychomotor retardation, and 40.9% in quality-of-life scores. The G-CoDe is significantly greater in deficit than in nondeficit schizophrenia.

CONCLUSIONS

A common core shared by a multitude of neurocognitive impairments (G-CoDe) mediates the effects of neurotoxic pathways on the phenome of (deficit) schizophrenia.