Psychiatric disorders: diagnosis to therapy

Delayed Scaphoid Fracture Union in Patients With Comorbid Psychiatric Diagnoses: A Retrospective Analysis of 20 340 Patients

BACKGROUND

Psychiatric comorbidities have been shown to influence outcomes of various orthopedic pathologies. This study aimed to compare rates of delayed scaphoid union and surgical intervention for fractures in patients with and without comorbid psychiatric diagnoses.

METHODS

A matched retrospective cohort study was performed using the PearlDiver database to determine the association of depression, anxiety, bipolar disorder, and schizophrenia with delayed union rates within 3 and 6 months and rates of nonacute surgical intervention (fixation or grafting) within 6 and 12 months of scaphoid fracture. Analyses were completed using multivariate logistic regression.

RESULTS

Among 20 340 patients, a comorbid psychiatric diagnosis was associated with increased rates of delayed scaphoid union at 3 months (odds ratio [OR] = 1.29; 95% confidence interval [CI], 1.14-1.45) and 6 months (OR = 1.23; 95% CI, 1.10-1.38). At 3 months, women with any psychiatric disorder (OR = 1.58; 1.29-1.66), depression (OR = 1.68; 1.31-2.17), and schizophrenia (OR = 5.32; 95% CI, 1.06-26.79) were more likely to experience delayed union, with similar results at 6 months. Men with bipolar disorder experienced increased delayed union rates at 6 months (OR = 1.40; 1.03-1.91). A comorbid psychiatric diagnosis (OR = 1.10; 1.01-1.20) was associated with increased rates of surgical intervention, whereas schizophrenia was associated with decreased rates (OR = 0.58; 0.34-0.99).

CONCLUSION

Patients with comorbid psychiatric conditions experienced increased rates of delayed scaphoid union. These results underscore the importance of understanding factors that may place patients at risk of impaired recovery.

Characterizing the distinct imaging phenotypes, clinical behavior, and genetic vulnerability of brain maturational subtypes in mood disorders

BACKGROUND

Mood disorders are characterized by great heterogeneity in clinical manifestation. Uncovering such heterogeneity using neuroimaging-based individual biomarkers, clinical behaviors, and genetic risks, might contribute to elucidating the etiology of these diseases and support precision medicine.

METHODS

We recruited 174 drug-naïve and drug-free patients with major depressive disorder and bipolar disorder, as well as 404 healthy controls. T1 MRI imaging data, clinical symptoms, and neurocognitive assessments, and genetics were obtained and analyzed. We applied regional gray matter volumes (GMV) and quantile normative modeling to create maturation curves, and then calculated individual deviations to identify subtypes within the patients using hierarchical clustering. We compared the between-subtype differences in GMV deviations, clinical behaviors, cell-specific transcriptomic associations, and polygenic risk scores. We also validated the GMV deviations based subtyping analysis in a replication cohort.

RESULTS

Two subtypes emerged: subtype 1, characterized by increased GMV deviations in the frontal cortex, cognitive impairment, a higher genetic risk for Alzheimer's disease, and transcriptionally associated with Alzheimer's disease pathways, oligodendrocytes, and endothelial cells; and subtype 2, displaying globally decreased GMV deviations, more severe depressive symptoms, increased genetic vulnerability to major depressive disorder and transcriptionally related to microglia and inhibitory neurons. The distinct patterns of GMV deviations in the frontal, cingulate, and primary motor cortices between subtypes were shown to be replicable.

CONCLUSIONS

Our current results provide vital links between MRI-derived phenotypes, spatial transcriptome, genetic vulnerability, and clinical manifestation, and uncover the heterogeneity of mood disorders in biological and behavioral terms.

Circulating Metabolite Abundances Associated With Risks of Bipolar Disorder, Schizophrenia, and Depression: A Mendelian Randomization Study

BACKGROUND

Preventive measures and treatments for psychiatric disorders are limited. Circulating metabolites are potential candidates for biomarker and therapeutic target identification, given their measurability and essential roles in biological processes.

METHODS

Leveraging large-scale genome-wide association studies, we conducted Mendelian randomization analyses to assess the associations between circulating metabolite abundances and the risks of bipolar disorder, schizophrenia, and depression. Genetic instruments were selected for 94 metabolites measured in the Canadian Longitudinal Study on Aging cohort (N = 8299). We repeated Mendelian randomization analyses based on the UK Biobank, INTERVAL, and EPIC (European Prospective Investigation into Cancer)-Norfolk studies.

RESULTS

After validating Mendelian randomization assumptions and colocalization evidence, we found that a 1 SD increase in genetically predicted circulating abundances of eicosapentaenoate and docosapentaenoate was associated with odds ratios of 0.72 (95% CI, 0.65-0.79) and 0.63 (95% CI, 0.55-0.72), respectively, for bipolar disorder. Genetically increased Ω-3 unsaturated fatty acids abundance and Ω-3-to-total fatty acids ratio, as well as genetically decreased Ω-6-to-Ω-3 ratio, were negatively associated with the risk of bipolar disorder in the UK Biobank. Genetically increased circulating abundances of 3 N-acetyl-amino acids were associated with an increased risk of schizophrenia with a maximum odds ratio of 1.31 (95% CI, 1.18-1.44) per 1 SD increase. Furthermore, a 1 SD increase in genetically predicted circulating abundance of hypotaurine was associated with an odds ratio of 0.85 (95% CI, 0.78-0.93) for depression.

CONCLUSIONS

The biological mechanisms that underlie Ω-3 unsaturated fatty acids, NAT8-catalyzed N-acetyl-amino acids, and hypotaurine warrant exploration to identify new biomarkers and potential therapeutic targets.

Genetic evidence strengthens the bidirectional connection between oral health status and psychiatric disorders: A two-sample Mendelian randomization study

BACKGROUND

Observational studies cannot accurately infer the causal associations between oral health status and psychiatric disorders.

METHODS

We conducted univariate and multivariate Mendelian randomization (MR) analyses using single nucleotide polymorphisms (SNPs) associated with eight oral health statuses (periodontitis, DMFS, Nteeth, toothache, loose teeth, painful gums, bleeding gums, and mouth ulcers) and four psychiatric disorders (Schizophrenia, Major Depressive Disorder (MDD), anxiety and stress-related disorder (ASRD), and Bipolar Disorder (BIP)) as instrumental variables. Genetic data were sourced from the Gene-lifestyle interactions in dental endpoints (GLIDE), UK Biobank, Psychiatric Genomics Consortium (PGC), and Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH). The inverse variance-weighted (IVW) approach, supported by a comprehensive sensitivity analysis, was employed.

RESULTS

Genetically predicted mouth ulcers were significantly linked to higher MDD (OR = 2.17, 95 % CI: 1.33--3.54, P< 0.01) and BIP risks (OR = 2.25, 95 % CI: 1.22-4.15, P = 0.01). BIP heightened bleeding gums risk (OR = 1.01, 95 % CI: 1.00-1.01, P < 0.01). These associations were adjusted for smoking status and alcohol consumption. Painful gums were significantly associated with MDD risk (OR = 96.48, 95 % CI: 2.66-3495.28, P = 0.01), while MDD raised periodontitis risk (OR = 2.15, 95 % CI: 1.24-3.75, P = 0.01), both confounded by smoking and alcohol. Relatively small effects between several variables, while others could not withstand correction for multiple tests.

LIMITATIONS

The sample size and limitation to European populations limits the study generalizability.

CONCLUSIONS

This study provide evidence of possible causal relationships between several oral health conditions and mental illness. Focusing on oral health and valuing mental health are important for each other and overall health.

Causal effects of various types of physical activities on psychiatric disorders: a Mendelian randomization study

Background

Psychiatric disorders (PD) pose a significant burden, with vast prevalence and mortality, inflicting substantial costs on individuals and society. Despite its widespread prevalence, the complex pathogenesis of PD remains elusive, leading to limited and challenging therapeutic development. An emerging risk factor for chronic diseases, prolonged sedentary behavior, contrasts with the therapeutic potential of exercise, regardless of its intensity, for various ailments, including PD. Yet, the diversity in exercise modalities and intensities may offer varied impacts on health. This study, leveraging Mendelian Randomization (MR), seeks to investigate the causal relationship between exercise and PD, aiming to elucidate the optimal exercise modality and intensity for PD mitigation while addressing potential confounders.

Methods

This study employed a Mendelian randomization analysis using the genome-wide association study (GWAS) database to investigate the causal relationship between types of physical activity and psychiatric disorders. Sensitivity analysis was conducted to demonstrate the reliability and robustness of the results.

Results

In the past 4 weeks, engaging in a substantial amount of DIY physical activity was found to have a causal relationship with psychiatric disorders (IVW: OR = 0.228, 95% CI: 0.113-0.461, P = 0.000038). As for the types of exercises, there may be a potential causal association between aerobic training (including swimming, cycling, fitness, and bowling) and psychiatric disorders (IVW: OR = 0.322, 95% CI = 0.148-0.704, P = 0.004). However, there was no causal relationship found between mild DIY physical activity and psychiatric disorders (IVW: OR = 0.918, 95% CI = 0.417-2.021, P = 0.831). Furthermore, it seems that there is no causal relationship between vigorous exercise and psychiatric disorders (IVW: OR = 2.705, 95% CI = 0.081-3.419, P = 0.578).

Conclusion

Our study confirms that only a certain level of training activity can have a protective effect on psychiatric disorders, while mild physical activity or vigorous training does not have an impact on psychiatric disorders.

Usage and cost-effectiveness of anti-psychotics in psychiatry OPD among various clinical conditions: A prospective observational study in a tertiary care center

Background

Psychotropic medications are prescription drugs that are commonly used to control some symptoms associated with many different types of mental ill health. Although they cannot cure a patient illness, they can assist with the management of some extremely distressing symptoms and this in turn can facilitate individuals in leading a more fulfilled life.

Aims and Objective

This study attempts to collect the demographic details of the outpatient in the psychiatric department and to describe the usage, prescribing trends and cost-effectiveness of antipsychotics in psychiatry OPD among various clinical conditions to older and younger patients with psychiatry disorders.

Material and Methods

This study was a prospective observational study, conducted over a period of 6 months from November 2021 to April 2022 in the Department of Psychiatry, Chidambaram Government Medical College and Hospital, a 1400 bedded multi-specialty tertiary care teaching hospital, Tamil Nadu Dr. M.G.R. Medical University Chennai, Tamil Nadu. The recruitment of subjects was carried out with the help the physicians who had the knowledge of patient's history and caregivers consenting to the study protocol and patients with case sheets carrying antipsychotics prescription.

Observation and Results

A total number of 150 cases were enrolled in this study. All psychiatric OPD cases were studied and the results were taken. Based on the inclusion and exclusion criteria, 93 were the anti-psychotics prescribed cases and 57 were the non-anti-psychotics prescribed cases. Out of 93 patients, 69.89% (65 patients) were male and 30.10% (28 patients) were female. Out of non-psychotic 57 patients, 27 were male patients and 30 were female patients. Out of the 93 OPD patients, 40% of the patients have psychological based symptoms followed by 17% of patients have behavioral symptoms. 86.02% of patients received atypical antipsychotic medications in comparison with 36.55% of patients received the typical antipsychotic medications.

Conclusion

In this study, we had observed the usage and types of anti-psychotic drugs to control and minimize the different psychotic symptoms among the OPD patients. Maximum antipsychotics studied were cost-effective and cheaper in Pradhan Mantri Bhartiya Janaushadhi Pariyojana (PMBJP) as compared to Intas, Torrent, Sun, Linux, Cipla, Abbott, Alkem, and Sanofi.

Applying dimensional psychopathology: transdiagnostic prediction of executive cognition using brain connectivity and inflammatory biomarkers

BACKGROUND

The association between executive dysfunction, brain dysconnectivity, and inflammation is a prominent feature across major psychiatric disorders (MPDs), schizophrenia, bipolar disorder, and major depressive disorder. A dimensional approach is warranted to delineate their mechanistic interplay across MPDs.

METHODS

This single site study included a total of 1543 participants (1058 patients and 485 controls). In total, 1169 participants underwent diffusion tensor and resting-state functional magnetic resonance imaging (745 patients and 379 controls completed the Wisconsin Card Sorting Test). Fractional anisotropy (FA) and regional homogeneity (ReHo) assessed structural and functional connectivity, respectively. Pro-inflammatory cytokine levels [interleukin (IL)-1β, IL-6, and tumor necrosis factor-α] were obtained in 325 participants using blood samples collected with 24 h of scanning. Group differences were determined for main measures, and correlation and mediation analyses and machine learning prediction modeling were performed.

RESULTS

Executive deficits were associated with decreased FA, increased ReHo, and elevated IL-1β and IL-6 levels across MPDs, compared to controls. FA and ReHo alterations in fronto-limbic-striatal regions contributed to executive deficits. IL-1β mediated the association between FA and cognition, and IL-6 mediated the relationship between ReHo and cognition. Executive cognition was better predicted by both brain connectivity and cytokine measures than either one alone for FA-IL-1β and ReHo-IL-6.

CONCLUSIONS

Transdiagnostic associations among brain connectivity, inflammation, and executive cognition exist across MPDs, implicating common neurobiological substrates and mechanisms for executive deficits in MPDs. Further, inflammation-related brain dysconnectivity within fronto-limbic-striatal regions may represent a transdiagnostic dimension underlying executive dysfunction that could be leveraged to advance treatment.

G protein-coupled receptors in neurodegenerative diseases and psychiatric disorders

Neuropsychiatric disorders are multifactorial disorders with diverse aetiological factors. Identifying treatment targets is challenging because the diseases are resulting from heterogeneous biological, genetic, and environmental factors. Nevertheless, the increasing understanding of G protein-coupled receptor (GPCR) opens a new possibility in drug discovery. Harnessing our knowledge of molecular mechanisms and structural information of GPCRs will be advantageous for developing effective drugs. This review provides an overview of the role of GPCRs in various neurodegenerative and psychiatric diseases. Besides, we highlight the emerging opportunities of novel GPCR targets and address recent progress in GPCR drug development.

The promise and challenges of extracellular vesicles in the diagnosis of neurodegenerative diseases

Extracellular vesicles (EVs) have emerged as essential means of intercommunication for all cell types, and their role in CNS physiology is increasingly appreciated. Accumulating evidence has demonstrated that EVs play important roles in neural cell maintenance, plasticity, and growth. However, EVs have also been demonstrated to spread amyloids and inflammation characteristic of neurodegenerative disease. Such dual roles suggest that EVs may be prime candidates for neurodegenerative disease biomarker analysis. This is supported by several intrinsic properties of EVs: Populations can be enriched by capturing surface proteins from their cell of origin, their diverse cargo represent the complex intracellular states of the cells they derive from, and they can pass the blood-brain barrier. Despite this promise, there are important questions outstanding in this young field that will need to be answered before it can fulfill its potential. Namely, overcoming the technical challenges of isolating rare EV populations, the difficulties inherent in detecting neurodegeneration, and the ethical considerations of diagnosing asymptomatic individuals. Although daunting, succeeding to answer these questions has the potential to provide unprecedented insight and improved treatment of neurodegenerative disease in the future.

Machine learning within the Parkinson's progression markers initiative: Review of the current state of affairs

The Parkinson's Progression Markers Initiative (PPMI) has collected more than a decade's worth of longitudinal and multi-modal data from patients, healthy controls, and at-risk individuals, including imaging, clinical, cognitive, and 'omics' biospecimens. Such a rich dataset presents unprecedented opportunities for biomarker discovery, patient subtyping, and prognostic prediction, but it also poses challenges that may require the development of novel methodological approaches to solve. In this review, we provide an overview of the application of machine learning methods to analyzing data from the PPMI cohort. We find that there is significant variability in the types of data, models, and validation procedures used across studies, and that much of what makes the PPMI data set unique (multi-modal and longitudinal observations) remains underutilized in most machine learning studies. We review each of these dimensions in detail and provide recommendations for future machine learning work using data from the PPMI cohort.

The miR-124-AMPAR pathway connects polygenic risks with behavioral changes shared between schizophrenia and bipolar disorder

Schizophrenia (SZ) and bipolar disorder (BP) are highly heritable major psychiatric disorders that share a substantial portion of genetic risk as well as their clinical manifestations. This raises a fundamental question of whether, and how, common neurobiological pathways translate their shared polygenic risks into shared clinical manifestations. This study shows the miR-124-3p-AMPAR pathway as a key common neurobiological mediator that connects polygenic risks with behavioral changes shared between these two psychotic disorders. We discovered the upregulation of miR-124-3p in neuronal cells and the postmortem prefrontal cortex from both SZ and BP patients. Intriguingly, the upregulation is associated with the polygenic risks shared between these two disorders. Seeking mechanistic dissection, we generated a mouse model that upregulates miR-124-3p in the medial prefrontal cortex. We demonstrated that the upregulation of miR-124-3p increases GRIA2-lacking calcium-permeable AMPARs and perturbs AMPAR-mediated excitatory synaptic transmission, leading to deficits in the behavioral dimensions shared between SZ and BP.

Genome-wide association study of school grades identifies genetic overlap between language ability, psychopathology and creativity

Cognitive functions of individuals with psychiatric disorders differ from that of the general population. Such cognitive differences often manifest early in life as differential school performance and have a strong genetic basis. Here we measured genetic predictors of school performance in 30,982 individuals in English, Danish and mathematics via a genome-wide association study (GWAS) and studied their relationship with risk for six major psychiatric disorders. When decomposing the school performance into math and language-specific performances, we observed phenotypically and genetically a strong negative correlation between math performance and risk for most psychiatric disorders. But language performance correlated positively with risk for certain disorders, especially schizophrenia, which we replicate in an independent sample (n = 4547). We also found that the genetic variants relating to increased risk for schizophrenia and better language performance are overrepresented in individuals involved in creative professions (n = 2953) compared to the general population (n = 164,622). The findings together suggest that language ability, creativity and psychopathology might stem from overlapping genetic roots.

Circulating Proteins Influencing Psychiatric Disease: A Mendelian Randomization Study

BACKGROUND

There is a pressing need for novel drug targets for psychiatric disorders. Circulating proteins are potential candidates because they are relatively easy to measure and modulate and play important roles in signaling.

METHODS

We performed two-sample Mendelian randomization analyses to estimate the associations between circulating protein abundances and risk of 10 psychiatric disorders. Genetic variants associated with 1611 circulating protein abundances identified in 6 large-scale proteomic studies were used as genetic instruments. Effects of the circulating proteins on psychiatric disorders were estimated by Wald ratio or inverse variance-weighted ratio tests. Horizontal pleiotropy, colocalization, and protein-altering effects were examined to validate the assumptions of Mendelian randomization.

RESULTS

Nine circulating protein-to-disease associations withstood multiple sensitivity analyses. Among them, 2 circulating proteins had associations replicated in 3 proteomic studies. A 1 standard deviation increase in the genetically predicted circulating TIMP4 level was associated with a reduced risk of anorexia nervosa (minimum odds ratio [OR] = 0.83; 95% CI, 0.76-0.91) and bipolar disorder (minimum OR = 0.88; 95% CI, 0.82-0.94). A 1 standard deviation increase in the genetically predicted circulating ESAM level was associated with an increased risk of schizophrenia (maximum OR = 1.32; 95% CI, 1.22-1.43). In addition, 58 suggestive protein-to-disease associations warrant validation with observational or experimental evidence. For instance, a 1 standard deviation increase in the ERLEC1-201-to-ERLEC1-202 splice variant ratio was associated with a reduced risk of schizophrenia (OR = 0.94; 95% CI, 0.90-0.97).

CONCLUSIONS

Prioritized circulating proteins appear to influence the risk of psychiatric disease and may be explored as intervention targets.

Role of G-Proteins and GPCR-Mediated Signalling in Neuropathophysiology

G-protein-coupled receptors (GPCRs) are activated by manifold neurotransmitters, and their activation, in turn, evokes slow synaptic transmission. They are profoundly related to numerous psychiatric and neurological disorders such as schizophrenia and Parkinson's disease. The significant malady indications for GPCR modulators demonstrate a change towards obesity, diabetes, and Alzheimer's disease, while other central nervous system disorders persist highly represented. GPR52, GPR6, and GPR8 are recognised as orphan GPCRs, co-exist either with both the dopamine D2 and D1 receptors in neurons of the basal ganglia or with the dopamine D2 receptor alone, and recommend that between these orphan receptors, GPR52 has the maximum potential of being a therapeutic psychiatric receptor. Genetically modified creature models and molecular biological investigations have suggested that these improved GPCRs could be potential therapeutic psychiatric receptors. In this perspective, the role of molecular targets in GPCR-mediated signalling has been discussed that would be novel drug design and discovery options for a scientist to elaborate previous knowledge with modern techniques.

Psychotropic Drugs and Adverse Kidney Effects: A Systematic Review of the Past Decade of Research

BACKGROUND AND OBJECTIVE

Psychotropic drugs are a heterogenous group of treatments prescribed for many psychiatric disorders, often for long periods. Their effects on the kidney and its functioning are complex and a source of significant research and debate. This article aims to review recent evidence of the acute and chronic kidney adverse events of diverse psychotropes.

METHODS

A systematic search of randomized controlled trials and large observational studies (n ≥ 100) reporting the effects of psychotropic drugs on the kidney was conducted. The MEDLINE, PsycInfo, and EMBASE databases from 2011 to 2021, inclusive, were broadly searched with few restrictions and no prespecified outcomes. Two or more independent reviewers assessed and summarized all eligible studies, including risks of bias and levels of evidence.

RESULTS

In all, 1999 abstracts were screened for eligibility and 47 articles were included, which examined lithium (33), antiepileptics (10), antipsychotics (13), and antidepressants (9). No studies examining kidney adverse effects of other psychotropes, such as benzodiazepines, met inclusion criteria. Study populations were adult (8), geriatric (9), and mixed (30). Lithium was almost unanimously associated with (1) chronic kidney disease and (2) nephrogenic diabetes insipidus in methodologically diverse studies. The most supported risk factors for declining kidney functioning with lithium were advanced age, duration of lithium treatment, acute lithium toxicity, female sex, medications with known renal interactions, diabetes mellitus/hyperglycemia, and overall medical comorbidity. Supratherapeutic lithium concentrations are both the causes and consequences of acute kidney injury. Once significant chronic kidney disease has developed, four studies found that replacing lithium with other mood stabilizers does not slow progression, and the evolution to end-stage kidney disease is rare overall with modern practices. Compared to lithium, fewer studies examined antipsychotics and antiepileptics but found relatively less direct kidney harms. Antidepressants were not associated with acute or chronic kidney harms.

CONCLUSIONS

Despite the heterogeneity of findings, owing to varying methodologies and research challenges, recent studies strongly suggest that lithium is associated with an increased risk of chronic kidney disease and nephrogenic diabetes insipidus, especially in older adults and long-term lithium users. Clinicians should balance the harms of lithium against its established benefits, and ensure adequate monitoring and management of comorbidities in all patients. Weaker evidence suggests that antiepileptics such as valproate and antipsychotics result in comparatively less harm to the kidney than lithium, but warrant monitoring because of multiple direct and indirect mechanisms for potential kidney adverse events. Antidepressants do not have clear kidney adverse effects and appear safe (though potentially less effective) in the setting of kidney disease. Other classes of psychotropic drugs have received little research interest. Further research is warranted, particularly into specific antiepileptics and antipsychotics, and careful attention should be paid to mitigating important sources of bias such as confounding by indication.

Therapeutic Efficacies of Berberine against Neurological Disorders: An Update of Pharmacological Effects and Mechanisms

Neurological disorders are ranked as the leading cause of disability and the second leading cause of death worldwide, underscoring an urgent necessity to develop novel pharmacotherapies. Berberine (BBR) is a well-known phytochemical isolated from a number of medicinal herbs. BBR has attracted much interest for its broad range of pharmacological actions in treating and/or managing neurological disorders. The discoveries in basic and clinical studies of the effects of BBR on neurological disorders in the last decade have provided novel evidence to support the potential therapeutical efficacies of BBR in treating neurological diseases. In this review, we summarized the pharmacological properties and therapeutic applications of BBR against neurological disorders in the last decade. We also emphasized the major pathways modulated by BBR, which provides firm evidence for BBR as a promising drug candidate for neurological disorders.

Evaluating the Performance of Machine Learning Models for Automatic Diagnosis of Patients with Schizophrenia Based on a Single Site Dataset of 440 Participants

Background

Methods

Results

Conclusions

TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry

Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops "computational assays" for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In this paper, we present the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.

Model-based prediction of muscarinic receptor function from auditory mismatch negativity responses

Drugs affecting neuromodulation, for example by dopamine or acetylcholine, take centre stage among therapeutic strategies in psychiatry. These neuromodulators can change both neuronal gain and synaptic plasticity and therefore affect electrophysiological measures. An important goal for clinical diagnostics is to exploit this effect in the reverse direction, i.e., to infer the status of specific neuromodulatory systems from electrophysiological measures. In this study, we provide proof-of-concept that the functional status of cholinergic (specifically muscarinic) receptors can be inferred from electrophysiological data using generative (dynamic causal) models. To this end, we used epidural EEG recordings over two auditory cortical regions during a mismatch negativity (MMN) paradigm in rats. All animals were treated, across sessions, with muscarinic receptor agonists and antagonists at different doses. Together with a placebo condition, this resulted in five levels of muscarinic receptor status. Using a dynamic causal model - embodying a small network of coupled cortical microcircuits - we estimated synaptic parameters and their change across pharmacological conditions. The ensuing parameter estimates associated with (the neuromodulation of) synaptic efficacy showed both graded muscarinic effects and predictive validity between agonistic and antagonistic pharmacological conditions. This finding illustrates the potential utility of generative models of electrophysiological data as computational assays of muscarinic function. In application to EEG data of patients from heterogeneous spectrum diseases, e.g. schizophrenia, such models might help identify subgroups of patients that respond differentially to cholinergic treatments. SIGNIFICANCE STATEMENT: In psychiatry, the vast majority of pharmacological treatments affect actions of neuromodulatory transmitters, e.g. dopamine or acetylcholine. As treatment is largely trial-and-error based, one of the goals for computational psychiatry is to construct mathematical models that can serve as "computational assays" and infer the status of specific neuromodulatory systems in individual patients. This translational neuromodeling strategy has great promise for electrophysiological data in particular but requires careful validation. The present study demonstrates that the functional status of cholinergic (muscarinic) receptors can be inferred from electrophysiological data using dynamic causal models of neural circuits. While accuracy needs to be enhanced and our results must be replicated in larger samples, our current results provide proof-of-concept for computational assays of muscarinic function using EEG.

The levels problem in psychopathology

Psychiatric disorders are studied at multiple levels, but there is no agreement on how these levels are related to each other, or how they should be understood in the first place. In this paper, I provide an account of levels and their relationships that is suited for psychopathology, drawing from recent debates in philosophy of science. Instead of metaphysical issues, the focus is on delivering an understanding of levels that is relevant and useful for scientific practice. I also defend a pragmatic approach to the question of reduction, arguing that even in-principle reductionists should embrace pluralism in practice. Finally, I discuss the benefits and challenges in integrating explanations and models of different levels.

Clinical and Neurobiological Aspects of TAO Kinase Family in Neurodevelopmental Disorders

Despite the complexity of neurodevelopmental disorders (NDDs), from their genotype to phenotype, in the last few decades substantial progress has been made in understanding their pathophysiology. Recent accumulating evidence shows the relevance of genetic variants in thousand and one (TAO) kinases as major contributors to several NDDs. Although it is well-known that TAO kinases are a highly conserved family of STE20 kinase and play important roles in multiple biological processes, the emerging roles of TAO kinases in neurodevelopment and NDDs have yet to be intensively discussed. In this review article, we summarize the potential roles of the TAO kinases based on structural and biochemical analyses, present the genetic data from clinical investigations, and assess the mechanistic link between the mutations of TAO kinases, neuropathology, and behavioral impairment in NDDs. We then offer potential perspectives from basic research to clinical therapies, which may contribute to fully understanding how TAO kinases are involved in NDDs.

Genome-wide association study of psychiatric and substance use comorbidity in Mexican individuals

The combination of substance use and psychiatric disorders is one of the most common comorbidities. The objective of this study was to perform a genome-wide association study of this comorbidity (Com), substance use alone (Subs), and psychiatric symptomatology alone (Psych) in the Mexican population. The study included 3914 individuals of Mexican descent. Genotyping was carried out using the PsychArray microarray and genome-wide correlations were calculated. Genome-wide associations were analyzed using multiple logistic models, polygenic risk scores (PRSs) were evaluated using multinomial models, and vertical pleiotropy was evaluated by generalized summary-data-based Mendelian randomization. Brain DNA methylation quantitative loci (brain meQTL) were also evaluated in the prefrontal cortex. Genome-wide correlation and vertical pleiotropy were found between all traits. No genome-wide association signals were found, but 64 single-nucleotide polymorphism (SNPs) reached nominal associations (p < 5.00e-05). The SNPs associated with each trait were independent, and the individuals with high PRSs had a higher prevalence of tobacco and alcohol use. In the multinomial models all of the PRSs (Subs-PRS, Com-PRS, and Psych-PRS) were associated with all of the traits. Brain meQTL of the Subs-associated SNPs had an effect on the genes enriched in insulin signaling pathway, and that of the Psych-associated SNPs had an effect on the Fc gamma receptor phagocytosis pathway.

[AI-Based Self-Tracking of the Mind: Philosophical-Ethical Implications]

OBJECTIVE

AI-based applications are increasingly developed to support users to digitally record, manage and change their emotions, beliefs and behavior patterns. Such forms of self-tracking in the mental sphere are accompanied by a variety of medical benefits in diagnostics, prevention, and therapy. This article pursues the question of which philosophical-ethical implications must be taken into account when dealing with these advantages.

METHODS

First, some AI-based applications for self-tracking of mental characteristics and processes are outlined. Subsequently, relevant philosophical-ethical implications are presented.

RESULTS

The following aspects prove to be normatively relevant: improvement versus reduction of self-determination; improvement of self-knowledge versus alienation; positive versus negative aspects of self-responsible health care; epistemic challenges of AI applications; difficulties of conceptual and normative definitions in the applications.

Deficient or Excess Folic Acid Supply During Pregnancy Alter Cortical Neurodevelopment in Mouse Offspring

Folate is an essential micronutrient required for both cellular proliferation through de novo nucleotide synthesis and epigenetic regulation of gene expression through methylation. This dual requirement places a particular demand on folate availability during pregnancy when both rapid cell generation and programmed differentiation of maternal, extraembryonic, and embryonic/fetal tissues are required. Accordingly, prenatal neurodevelopment is particularly susceptible to folate deficiency, which can predispose to neural tube defects, or when effective transport into the brain is impaired, cerebral folate deficiency. Consequently, adequate folate consumption, in the form of folic acid (FA) fortification and supplement use, is widely recommended and has led to a substantial increase in the amount of FA intake during pregnancy in some populations. Here, we show that either maternal folate deficiency or FA excess in mice results in disruptions in folate metabolism of the offspring, suggesting diversion of the folate cycle from methylation to DNA synthesis. Paradoxically, either intervention causes comparable neurodevelopmental changes by delaying prenatal cerebral cortical neurogenesis in favor of late-born neurons. These cytoarchitectural and biochemical alterations are accompanied by behavioral abnormalities in FA test groups compared with controls. Our findings point to overlooked potential neurodevelopmental risks associated with excessively high levels of prenatal FA intake.

Behavioral and molecular effects of the antipsychotic drug blonanserin in the chronic mild stress model

Psychiatric disorders represent a critical challenge to our society, given their high global prevalence, complex symptomatology, elusive etiology and the variable effectiveness of pharmacological therapies. Recently, there has been a shift in investigating and redefining these diseases by integrating behavioral observations and multilevel neurobiological measures. Accordingly, endophenotype-oriented studies are needed to develop new therapeutic strategies, with the idea of targeting shared symptoms instead of one defined disease. With these premises, here we investigated the therapeutic properties of chronic treatment with the second-generation antipsychotic blonanserin in counteracting the alterations caused by 7 weeks of Chronic Mild Stress (CMS) in the rat. CMS is a well-established preclinical model able to induce depressive and anxiety-like alterations, which are shared by different psychiatric disorders. Our results demonstrated that the antipsychotic treatment normalizes the CMS-induced emotionality deficits, an effect that may be due to its ability in modulating, within the prefrontal cortex, redox mechanisms, a molecular dysfunction associated with several psychiatric disorders. These evidences provide new insights into the therapeutic properties and potential use of blonanserin as well as in its mechanisms of action and provide further support for the role of oxidative stress in the pathophysiology of psychiatric disorders.

An activity-dependent local transport regulation via degradation and synthesis of KIF17 underlying cognitive flexibility

Synaptic weight changes among postsynaptic densities within a single dendrite are regulated by the balance between localized protein degradation and synthesis. However, the molecular mechanism via these opposing regulatory processes is still elusive. Here, we showed that the molecular motor KIF17 was locally degraded and synthesized in an N-methyl-d-aspartate receptor (NMDAR)-mediated activity-dependent manner. Accompanied by the degradation of KIF17, its transport was temporarily dampened in dendrites. We also observed that activity-dependent local KIF17 synthesis driven by its 3' untranslated region (3'UTR) occurred at dendritic shafts, and the newly synthesized KIF17 moved along the dendrites. Furthermore, hippocampus-specific deletion of Kif17 3'UTR disrupted KIF17 synthesis induced by fear memory retrieval, leading to impairment in extinction of fear memory. These results indicate that the regulation of the KIF17 transport is driven by the single dendrite-restricted cycle of degradation and synthesis that underlies cognitive flexibility.

Understanding and Treating Niemann-Pick Type C Disease: Models Matter

Biomedical research aims to understand the molecular mechanisms causing human diseases and to develop curative therapies. So far, these goals have been achieved for a small fraction of diseases, limiting factors being the availability, validity, and use of experimental models. Niemann–Pick type C (NPC) is a prime example for a disease that lacks a curative therapy despite substantial breakthroughs. This rare, fatal, and autosomal-recessive disorder is caused by defects in NPC1 or NPC2. These ubiquitously expressed proteins help cholesterol exit from the endosomal–lysosomal system. The dysfunction of either causes an aberrant accumulation of lipids with patients presenting a large range of disease onset, neurovisceral symptoms, and life span. Here, we note general aspects of experimental models, we describe the line-up used for NPC-related research and therapy development, and we provide an outlook on future topics.

Novel Fluorescent Ligands Enable Single-Molecule Localization Microscopy of the Dopamine Transporter

The dopamine transporter (DAT) is critical for spatiotemporal control of dopaminergic neurotransmission and is the target for therapeutic agents, including ADHD medications, and abused substances, such as cocaine. Here, we develop new fluorescently labeled ligands that bind DAT with high affinity and enable single-molecule detection of the transporter. The cocaine analogue MFZ2-12 (1) was conjugated to novel rhodamine-based Janelia Fluorophores (JF₅₄₉ and JF₆₄₆). High affinity binding of the resulting ligands to DAT was demonstrated by potent inhibition of [³H]dopamine uptake in DAT transfected CAD cells and by competition radioligand binding experiments on rat striatal membranes. Visualization of binding was substantiated by confocal or TIRF microscopy revealing selective binding of the analogues to DAT transfected CAD cells. Single particle tracking experiments were performed with JF₅₄₉-conjugated DG3-80 (3) and JF₆₄₆-conjugated DG4-91 (4) on DAT transfected CAD cells enabling quantification and categorization of the dynamic behavior of DAT into four distinct motion classes (immobile, confined, Brownian, and directed). Finally, we show that the ligands can be used in direct stochastic optical reconstruction microscopy (dSTORM) experiments permitting further analyses of DAT distribution on the nanoscale. In summary, these novel fluorescent ligands are promising new tools for studying DAT localization and regulation with single-molecule resolution.

Magnetoencephalography resting-state spectral fingerprints distinguish bipolar depression and unipolar depression

OBJECTIVES

In clinical practice, bipolar depression (BD) and unipolar depression (UD) appear to have similar symptoms, causing BD being frequently misdiagnosed as UD, leading to improper treatment decision and outcome. Therefore, it is in urgent need of distinguishing BD from UD based on clinical objective biomarkers as early as possible. Here, we aimed to integrate brain neuroimaging data and an advanced machine learning technique to predict different types of mood disorder patients at the individual level.

METHODS

Eyes closed resting-state magnetoencephalography (MEG) data were collected from 23 BD, 30 UD, and 31 healthy controls (HC). Individual power spectra were estimated by Fourier transform, and statistic spectral differences were assessed via a cluster permutation test. A support vector machine classifier was further applied to predict different mood disorder types based on discriminative oscillatory power.

RESULTS

Both BD and UD showed decreased frontal-central gamma/beta ratios comparing to HC, in which gamma power (30-75 Hz) was decreased in BD while beta power (14-30 Hz) was increased in UD vs HC. The support vector machine model obtained significant high classification accuracies distinguishing three groups based on mean gamma and beta power (BD: 79.9%, UD: 81.1%, HC: 76.3%, P < .01).

CONCLUSIONS

In combination with resting-state MEG data and machine learning technique, it is possible to make an individual and objective prediction for mode disorder types, which in turn has implications for diagnosis precision and treatment decision of mood disorder patients.

Machine-learning classification using neuroimaging data in schizophrenia, autism, ultra-high risk and first-episode psychosis

Neuropsychiatric disorders are diagnosed based on behavioral criteria, which makes the diagnosis challenging. Objective biomarkers such as neuroimaging are needed, and when coupled with machine learning, can assist the diagnostic decision and increase its reliability. Sixty-four schizophrenia, 36 autism spectrum disorder (ASD), and 106 typically developing individuals were analyzed. FreeSurfer was used to obtain the data from the participant's brain scans. Six classifiers were utilized to classify the subjects. Subsequently, 26 ultra-high risk for psychosis (UHR) and 17 first-episode psychosis (FEP) subjects were run through the trained classifiers. Lastly, the classifiers' output of the patient groups was correlated with their clinical severity. All six classifiers performed relatively well to distinguish the subject groups, especially support vector machine (SVM) and Logistic regression (LR). Cortical thickness and subcortical volume feature groups were most useful for the classification. LR and SVM were highly consistent with clinical indices of ASD. When UHR and FEP groups were run with the trained classifiers, majority of the cases were classified as schizophrenia, none as ASD. Overall, SVM and LR were the best performing classifiers. Cortical thickness and subcortical volume were most useful for the classification, compared to surface area. LR, SVM, and DT's output were clinically informative. The trained classifiers were able to help predict the diagnostic category of both UHR and FEP Individuals.

A Caenorhabditis elegans Model for Integrating the Functions of Neuropsychiatric Risk Genes Identifies Components Required for Normal Dendritic Morphology

Analysis of patient-derived DNA samples has identified hundreds of variants that are likely involved in neuropsychiatric diseases such as autism spectrum disorder (ASD) and schizophrenia (SCZ). While these studies couple behavioral phenotypes to individual genotypes, the number and diversity of candidate genes implicated in these disorders highlights the fact that the mechanistic underpinnings of these disorders are largely unknown. Here, we describe a RNAi-based screening platform that uses C. elegans to screen candidate neuropsychiatric risk genes (NRGs) for roles in controlling dendritic arborization. To benchmark this approach, we queried published lists of NRGs whose variants in ASD and SCZ are predicted to result in complete or partial loss of gene function. We found that a significant fraction (>16%) of these candidate NRGs are essential for dendritic development. Furthermore, these gene sets are enriched for dendritic arbor phenotypes (>14 fold) when compared to control RNAi datasets of over 500 human orthologs. The diversity of PVD structural abnormalities observed in these assays suggests that the functions of diverse NRGs (encoding transcription factors, chromatin remodelers, molecular chaperones and cytoskeleton-related proteins) converge to regulate neuronal morphology and that individual NRGs may play distinct roles in dendritic branching. We also demonstrate that the experimental value of this platform by providing additional insights into the molecular frameworks of candidate NRGs. Specifically, we show that ANK2/UNC-44 function is directly integrated with known regulators of dendritic arborization and suggest that altering the dosage of ARID1B/LET-526 expression during development affects neuronal morphology without diminishing aspects of cell fate specification.

Emerging roles of the unfolded protein response (UPR) in the nervous system: A link with adaptive behavior to environmental stress?

Stressors elicit a neuroendocrine response leading to increased levels of glucocorticoids, allowing the organism to adapt to environmental changes and maintain homeostasis. Glucocorticoids have a broad effect in the body, modifying the activity of the immune system, metabolism, and behavior through the activation of receptors in the limbic system. Chronic exposition to stressors operates as a risk factor for psychiatric diseases such as depression and posttraumatic stress disorder. Among the cellular alterations observed as a consequence of environmental stress, alterations to organelle function at the level of mitochondria and endoplasmic reticulum (ER) are emerging as possible factors contributing to neuronal dysfunction. ER proteostasis alterations elicit the unfolded protein response (UPR), a conserved signaling network that re-establish protein homeostasis. In addition, in the context of brain function, the UPR has been associated to neurodevelopment, synaptic plasticity and neuronal connectivity. Recent studies suggest a role of the UPR in the adaptive behavior to stress, suggesting a mechanistic link between environmental and cellular stress. Here, we revise recent evidence supporting an evolutionary connection between the neuroendocrine system and the UPR to modulate behavioral adaptive responses.

Data-Driven Approaches to Neuroimaging Analysis to Enhance Psychiatric Diagnosis and Therapy

Combining advanced neuroimaging with novel computational methods in network science and machine learning has led to increasingly meaningful descriptions of structure and function in both the normal and the abnormal brain, thereby contributing significantly to our understanding of psychiatric disorders as circuit dysfunctions. Despite its marked potential for psychiatric care, this approach has not yet extended beyond the research setting to any clinically useful applications. Here we review current developments in the study of neuroimaging data using network models and machine learning methods, with a focus on their promise in offering a framework for clinical translation. We discuss 3 potential contributions of these methods to psychiatric care: 1) a better understanding of psychopathology beyond current diagnostic boundaries; 2) individualized prediction of treatment response and prognosis; and 3) formal theories to guide the development of novel interventions. Finally, we highlight current obstacles and sketch a forward-looking perspective of how the application of machine learning and network modeling methods should proceed to accelerate their potential transformation of clinically useful tools.

Endocannabinoid System Components as Potential Biomarkers in Psychiatry

The high heterogeneity of psychiatric disorders leads to a lack of diagnostic precision. Therefore, the search of biomarkers is a fundamental aspect in psychiatry to reach a more personalized medicine. The endocannabinoid system (ECS) has gained increasing interest due to its involvement in many different functional processes in the brain, including the regulation of emotions, motivation, and cognition. This article reviews the role of the main components of the ECS as biomarkers in certain psychiatric disorders. Studies carried out in rodents evaluating the effects of pharmacological and genetic manipulation of cannabinoid receptors or endocannabinoids (eCBs) degrading enzymes were included. Likewise, the ECS-related alterations occurring at the molecular level in animal models reproducing some behavioral and/or neuropathological aspects of psychiatric disorders were reviewed. Furthermore, clinical studies evaluating gene or protein alterations in post-mortem brain tissue or in vivo blood, plasma, and cerebrospinal fluid (CSF) samples were analyzed. Also, the results from neuroimaging studies using positron emission tomography (PET) or functional magnetic resonance (fMRI) were included. This review shows the close involvement of cannabinoid receptor 1 (CB1r) in stress regulation and the development of mood disorders [anxiety, depression, bipolar disorder (BD)], in post-traumatic stress disorder (PTSD), as well as in the etiopathogenesis of schizophrenia, attention deficit hyperactivity disorder (ADHD), or eating disorders (i.e. anorexia and bulimia nervosa). On the other hand, recent results reveal the potential therapeutic action of the endocannabinoid tone manipulation by inhibition of eCBs degrading enzymes, as well as by the modulation of cannabinoid receptor 2 (CB2r) activity on anxiolytic, antidepressive, or antipsychotic associated effects. Further clinical research studies are needed; however, current evidence suggests that the components of the ECS may become promising biomarkers in psychiatry to improve, at least in part, the diagnosis and pharmacological treatment of psychiatric disorders.

The Molecular Motor KIF21B Mediates Synaptic Plasticity and Fear Extinction by Terminating Rac1 Activation

Fear extinction is a component of cognitive flexibility that is relevant for important psychiatric diseases, but its molecular mechanism is still largely elusive. We established mice lacking the kinesin-4 motor KIF21B as a model for fear extinction defects. Postsynaptic NMDAR-dependent long-term depression (LTD) is specifically impaired in knockouts. NMDAR-mediated LTD-causing stimuli induce dynamic association of KIF21B with the Rac1GEF subunit engulfment and cell motility protein 1 (ELMO1), leading to ELMO1 translocation out of dendritic spines and its sequestration in endosomes. This process may essentially terminate transient activation of Rac1, shrink spines, facilitate AMPAR endocytosis, and reduce postsynaptic strength, thereby forming a mechanistic link to LTD expression. Antagonizing ELMO1/Dock Rac1GEF activity by the administration of 4-[3'-(2″-chlorophenyl)-2'-propen-1'-ylidene]-1-phenyl-3,5-pyrazolidinedione (CPYPP) significantly reverses the knockout phenotype. Therefore, we propose that KIF21B-mediated Rac1 inactivation is a key molecular event in NMDAR-dependent LTD expression underlying cognitive flexibility in fear extinction.

Brainstem Modulation of Large-Scale Intrinsic Cortical Activity Correlations

Brain activity fluctuates continuously, even in the absence of changes in sensory input or motor output. These intrinsic activity fluctuations are correlated across brain regions and are spatially organized in macroscale networks. Variations in the strength, topography, and topology of correlated activity occur over time, and unfold upon a backbone of long-range anatomical connections. Subcortical neuromodulatory systems send widespread ascending projections to the cortex, and are thus ideally situated to shape the temporal and spatial structure of intrinsic correlations. These systems are also the targets of the pharmacological treatment of major neurological and psychiatric disorders, such as Parkinson's disease, depression, and schizophrenia. Here, we review recent work that has investigated how neuromodulatory systems shape correlations of intrinsic fluctuations of large-scale cortical activity. We discuss studies in the human, monkey, and rodent brain, with a focus on non-invasive recordings of human brain activity. We provide a structured but selective overview of this work and distil a number of emerging principles. Future efforts to chart the effect of specific neuromodulators and, in particular, specific receptors, on intrinsic correlations may help identify shared or antagonistic principles between different neuromodulatory systems. Such principles can inform models of healthy brain function and may provide an important reference for understanding altered cortical dynamics that are evident in neurological and psychiatric disorders, potentially paving the way for mechanistically inspired biomarkers and individualized treatments of these disorders.

Plastic Adaptation to Pathology in Psychiatry: Are Patients with Psychiatric Disorders Pathological Experts?

Psychiatric disorders share the same pattern of longitudinal evolution and have courses that tend to be chronic and recurrent. These aspects of chronicity and longitudinal evolution are currently studied under the deficit-oriented neuroprogression framework. Interestingly, considering the plasticity of the brain, it is also necessary to emphasize the bidirectional nature of neuroprogression. We review evidence highlighting alterations of the brain associated with the longitudinal evolution of psychiatric disorders from the framework of neuroplastic adaptation to pathology. This new framework highlights that substantial plasticity and remodeling may occur beyond the classic deficit-oriented neuroprogressive framework, which has been associated with progressive loss of gray matter thickness, decreased brain connectivity, and chronic inflammation. We also integrate the brain economy concept in the neuroplastic adaptation to pathology framework, emphasizing that to preserve its economy, i.e. function, the brain learns how to cope with the disease by adapting its architecture. Neuroplastic adaptation to pathology is a proposition for a paradigm shift to overcome the shortcomings of traditional psychiatric diagnostic boundaries; this approach can disentangle both the specific pathophysiology of psychiatric symptoms and the adaptation to pathology, thus offering a new framework for both diagnosis and treatment.

Gene expression pattern of CCL2, CCL3, and CXCL8 in patients with bipolar disorder

Background

Bipolar disorder (BD) is one of the most important psychiatric disorders in the world. There is evidence suggesting the role of inflammatory mediators such as chemokines in the etiology of BD. The objective of the current study was to evaluate the gene expression of CCL2, CCL3, and CXCL8 in patients with BD and compare them to healthy controls.

Materials and Methods

A total of 48 patients with confirmed BD and 48 healthy controls enrolled in this study. All patients were recruited from April to August 2016 at Ibn-Sina Psychiatric Hospital, Mashhad University of Medical Sciences, Mashhad, Iran. RNA was extracted from the whole blood samples and then cDNA was synthesized. Gene expression of CCL2, CCL3, and CXCL8 was measured using SYBR^® Green real-time polymerase chain reaction. The difference of delta-CT values between patients and healthy controls was compared with the independent samples t-tests.

Results

CCL2 and CXCL8 genes expressed at higher levels in patients with BD as compared to healthy controls, but not significant. On the contrary, we found lower expression levels for CCL3 gene in our patients compared to healthy controls, but the difference was not statistically significant.

Conclusion

Our findings do not show an association between the gene expression of CCL2, CCL3 and CXCL8 and BD. Increasing the sample size and evaluation on the gene expression of other chemokines in depression and mania phases of BD might be helpful to get a better conclusion.

Modulation of anterior cingulate cortex reward and penalty signalling in medication-naive young-adult subjects with depressive symptoms following acute dose lurasidone

BACKGROUND

Aberrations in reward and penalty processing are implicated in depression and putatively reflect altered dopamine signalling. This study exploits the advantages of a placebo-controlled design to examine how a novel D2 antagonist with adjunctive antidepressant properties modifies activity in the brain's reward network in depression.

METHODS

We recruited 43 medication-naïve subjects across the range of depression severity (Beck's Depression Inventory-II score range: 0-43), including healthy volunteers, as well as people meeting full-criteria for major depressive disorder. In a double-blind placebo-controlled cross-over design, all subjects received either placebo or lurasidone (20 mg) across two visits separated by 1 week. Functional magnetic resonance imaging with the Monetary Incentive Delay (MID) task assessed reward functions via neural responses during anticipation and receipt of gains and losses. Arterial spin labelling measured cerebral blood flow (CBF) at rest.

RESULTS

Lurasidone altered fronto-striatal activity during anticipation and outcome phases of the MID task. A significant three-way Medication-by-Depression severity-by-Outcome interaction emerged in the anterior cingulate cortex (ACC) after correction for multiple comparisons. Follow-up analyses revealed significantly higher ACC activation to losses in high- v. low depression participants in the placebo condition, with a normalisation by lurasidone. This effect could not be accounted for by shifts in resting CBF.

CONCLUSIONS

Lurasidone acutely normalises reward processing signals in individuals with depressive symptoms. Lurasidone's antidepressant effects may arise from reducing responses to penalty outcomes in individuals with depressive symptoms.

Prioritization and comprehensive analysis of genes related to major depressive disorder

Background

Major depressive disorder (MDD) is a serious mental health problem in modern society, which is difficult to identify and diagnose in the early stages. Despite strong evidence supporting the heritability of MDD, progresses in large‐scale and individual genetic studies remain preliminary.

Methods

In this study, a multi‐data source‐based prioritization (MDSP) method was proposed, and an appropriate threshold was determined for the optimization of depression‐related genes (DEPgenes). Analyses on Gene Ontology biological processes, KEGG pathway and the specific pathway crosstalk network were further proposed.

Results

A total of 143 DEPgenes were identified and the MDD‐specific network was constructed for the pathogenesis investigation and therapeutic methods development of MDD. Comparing with existing research strategies, the genetic optimization and analysis results were confirmed to be reliable. Finally, the pathway enrichment and crosstalk analyses revealed two unique pathway interaction modules that were significantly enriched with MDD genes. The related core pathways of neuroactive ligand‐receptor interaction and dopaminergic synapse supported the neuropathology hypothesis of MDD. And the pathways of serotonergic synapse and morphine addiction indicated the mechanism of drug addiction caused by serotonin used in the treatment.

Conclusions

This work provided a reference for the study of MDD, although future validation by extensive experimentation is still required.

The Associations between Central Nervous System Diseases and Haemostatic Disorders

The aim of this review was to examine the relationship between the occurrence of central nervous system (CNS) diseases, the medicines used in their treatment and the blood coagulation process. The paper mainly focuses on the effects of antidepressant and antipsychotic drugs. Special attention has been paid to the influence of drugs on platelets, the vascular endothelium, plasma coagulation and fibrinolysis, regarding coagulation.

A genome-wide association study of shared risk across psychiatric disorders implicates gene regulation during fetal neurodevelopment

There is mounting evidence that seemingly diverse psychiatric disorders share genetic etiology, but the biological substrates mediating this overlap are not well characterized. Here we leverage the unique Integrative Psychiatric Research Consortium (iPSYCH) study, a nationally representative cohort ascertained through clinical psychiatric diagnoses indicated in Danish national health registers. We confirm previous reports of individual and cross-disorder single-nucleotide polymorphism heritability for major psychiatric disorders and perform a cross-disorder genome-wide association study. We identify four novel genome-wide significant loci encompassing variants predicted to regulate genes expressed in radial glia and interneurons in the developing neocortex during mid-gestation. This epoch is supported by partitioning cross-disorder single-nucleotide polymorphism heritability, which is enriched at regulatory chromatin active during fetal neurodevelopment. These findings suggest that dysregulation of genes that direct neurodevelopment by common genetic variants may result in general liability for many later psychiatric outcomes.

The Case for Adaptive Neuromodulation to Treat Severe Intractable Mental Disorders

Mental disorders are a leading cause of disability worldwide, and available treatments have limited efficacy for severe cases unresponsive to conventional therapies. Neurosurgical interventions, such as lesioning procedures, have shown success in treating refractory cases of mental illness, but may have irreversible side effects. Neuromodulation therapies, specifically Deep Brain Stimulation (DBS), may offer similar therapeutic benefits using a reversible (explantable) and adjustable platform. Early DBS trials have been promising, however, pivotal clinical trials have failed to date. These failures may be attributed to targeting, patient selection, or the “open-loop” nature of DBS, where stimulation parameters are chosen ad hoc during infrequent visits to the clinician’s office that take place weeks to months apart. Further, the tonic continuous stimulation fails to address the dynamic nature of mental illness; symptoms often fluctuate over minutes to days. Additionally, stimulation-based interventions can cause undesirable effects if applied when not needed. A responsive, adaptive DBS (aDBS) system may improve efficacy by titrating stimulation parameters in response to neural signatures (i.e., biomarkers) related to symptoms and side effects. Here, we present rationale for the development of a responsive DBS system for treatment of refractory mental illness, detail a strategic approach for identification of electrophysiological and behavioral biomarkers of mental illness, and discuss opportunities for future technological developments that may harness aDBS to deliver improved therapy.

Region-specific inhibition of 14-3-3 proteins induces psychomotor behaviors in mice

The 14-3-3 family of proteins is genetically linked to several psychiatric disorders, including schizophrenia. Our 14-3-3 functional knockout (FKO) mice, as well as other 14-3-3 knockout models, have been shown to exhibit behavioral endophenotypes related to schizophrenia. While specific forebrain regions, such as the prefrontal cortex (PFC) and hippocampus (HP), have been implicated in schizophrenic pathophysiology, the role of these brain regions in the top-down control of specific schizophrenia-associated behaviors has not been examined. Here, we used an adeno-associated virus (AAV) delivered shRNA to knock down the expression of the 14-3-3-inhibitor transgene, thus selectively restoring the function of 14-3-3 in the forebrain of the 14-3-3 FKO mice, we found that injection of the AAV-shRNA into both the PFC and the HP is necessary to attenuate psychomotor activity of the 14-3-3 FKO mice. Furthermore, we found that acute inhibition of 14-3-3, through the delivery of an AAV expressing the 14-3-3 inhibitor to both the PFC and HP, can trigger psychomotor agitation. Interestingly, when assessing the two brain regions separately, we determined that AAV-mediated expression of the 14-3-3 inhibitor specifically within the HP alone is sufficient to induce several behavioral deficits including hyperactivity, impaired associative learning and memory, and reduced sensorimotor gating. In addition, we show that post-synaptic NMDA receptor levels are regulated by acute 14-3-3 manipulations. Taken together, findings from this study directly link 14-3-3 inhibition in specific forebrain regions to certain schizophrenia-associated endophenotypes.

Artificial Intelligence in Psychiatry

Scientific findings over the past few decades have shaped our understanding of the underlying neurobiology associated with psychiatric illnesses. However, despite significant advances in research, there is widespread disappointment with the overall pace of progress in detecting and treating psychiatric disorders. Current approaches for the diagnosis of psychiatric disorders largely rely on physician-patient questionnaires that are most of the time inaccurate and ineffective in providing a reliable assessment of symptoms. These limitations can, however, be overcome by applying artificial intelligence (AI) to electronic medical database and health records. AI in psychiatry is a general term that implies the use of computerized techniques and algorithms for the diagnosis, prevention, and treatment of mental illnesses. Although the past few years have witnessed an increase in the use of AI in the medical practice, its role in psychiatry remains a complex and unanswered question. This chapter provides the current state of knowledge of AI's use in the diagnosis, prediction, and treatment of psychiatric disorders, and examines the challenges and limitations of this approach in the medical practise.

RETRACTED: Human Suicide, Modern Diagnosis Assistance and Magic Bullet Discovery

The article entitled “Human Suicide, Modern Diagnosis Assistance and Magic Bullet Discovery”, by Da-Yong Lu, Peng- Peng Zhu, Hong-Ying Wu, Nagendra Sastry Yarla, Bin Xu, Jian Ding, Ajit Varki and Ting-Ren Lu, has been retracted on the request of the co-authors, Dr. Ajit Varki, Dr. Nagendra Sastry Yarla and Dr. Jian Ding available at: Cent Nerv Syst Agents Med Chem 2019; 19(1): 15-23. http://www.eurekaselect.com/169003/article. The Corresponding Author Dr. Da-Yong Lu has included the names of the co-authors, Dr. Ajit Varki, Dr. Nagendra Sastry Yarla and Dr. Jian Ding without their consent and the manuscript has been published in the journal, Central Nervous System Agents in Medicinal Chemistry (CNSAMC). Kindly see Bentham Science Policy on Article retraction at the link given below: (https://benthamscience.com/journals/central-nervous-system-agents-in-medicinal-chemistry/author-guidelines/) Submission of a manuscript to the respective journals implies that all authors have read and agreed to the content of the Copyright Letter or the Terms and Conditions. As such, this article represents a severe abuse of the scientific publishing system. Bentham Science Publishers takes a very strong view on this matter and apologizes to the readers of the journal for any inconvenience this may cause.

The Psychiatric Cell Map Initiative: A Convergent Systems Biological Approach to Illuminating Key Molecular Pathways in Neuropsychiatric Disorders

Although gene discovery in neuropsychiatric disorders, including autism spectrum disorder, intellectual disability, epilepsy, schizophrenia, and Tourette disorder, has accelerated, resulting in a large number of molecular clues, it has proven difficult to generate specific hypotheses without the corresponding datasets at the protein complex and functional pathway level. Here, we describe one path forward-an initiative aimed at mapping the physical and genetic interaction networks of these conditions and then using these maps to connect the genomic data to neurobiology and, ultimately, the clinic. These efforts will include a team of geneticists, structural biologists, neurobiologists, systems biologists, and clinicians, leveraging a wide array of experimental approaches and creating a collaborative infrastructure necessary for long-term investigation. This initiative will ultimately intersect with parallel studies that focus on other diseases, as there is a significant overlap with genes implicated in cancer, infectious disease, and congenital heart defects.