Biomarkers in Psychiatry - A Critique

Advances, challenges, and prospects of electroencephalography-based biomarkers for psychiatric disorders: a narrative review

Owing to a lack of appropriate biomarkers for accurate diagnosis and treatment, psychiatric disorders cause significant distress and functional impairment, leading to social and economic losses. Biomarkers are essential for diagnosing, predicting, treating, and monitoring various diseases. However, their absence in psychiatry is linked to the complex structure of the brain and the lack of direct monitoring modalities. This review examines the potential of electroencephalography (EEG) as a neurophysiological tool for identifying psychiatric biomarkers. EEG noninvasively measures brain electrophysiological activity and is used to diagnose neurological disorders, such as depression, bipolar disorder (BD), and schizophrenia, and identify psychiatric biomarkers. Despite extensive research, EEG-based biomarkers have not been clinically utilized owing to measurement and analysis constraints. EEG studies have revealed spectral and complexity measures for depression, brainwave abnormalities in BD, and power spectral abnormalities in schizophrenia. However, no EEG-based biomarkers are currently used clinically for the treatment of psychiatric disorders. The advantages of EEG include real-time data acquisition, noninvasiveness, cost-effectiveness, and high temporal resolution. Challenges such as low spatial resolution, susceptibility to interference, and complexity of data interpretation limit its clinical application. Integrating EEG with other neuroimaging techniques, advanced signal processing, and standardized protocols is essential to overcome these limitations. Artificial intelligence may enhance EEG analysis and biomarker discovery, potentially transforming psychiatric care by providing early diagnosis, personalized treatment, and improved disease progression monitoring.

The LEADING Guideline: Reporting Standards for Expert Panel, Best-Estimate Diagnosis, and Longitudinal Expert All Data (LEAD) Studies

Background

Accurate assessments of symptoms and illnesses are essential for health research and clinical practice but face many challenges. The absence of a single error-free measure is currently addressed by assessment methods involving experts reviewing several sources of information to achieve a more accurate or best-estimate assessment. Three bodies of work spanning medicine, psychiatry, and psychology propose similar assessment methods: The Expert Panel, the Best-Estimate Diagnosis, and the Longitudinal Expert All Data (LEAD) method. However, the quality of such best-estimate assessments is typically very difficult to evaluate due to poor reporting of the assessment methods and when they are reported, the reporting quality varies substantially. Here, we tackle this gap by developing reporting guidelines for such best-estimate assessment studies.

Methods

The development of the reporting guidelines followed a four-stage approach: 1) drafting reporting standards accompanied by rationales and empirical evidence, which were further developed with a patient organization for depression, 2) incorporating expert feedback through a two-round Delphi procedure, 3) refining the guideline based on an expert consensus meeting, and 4) testing the guideline by i) having two researchers test it and ii) using it to examine the extent previously published studies report the standards. The last step also provides evidence for the need for the guideline: 10 to 63% (Mean = 33%) of the standards were not reported across thirty randomly selected studies.

Results

The LEADING guideline comprises 20 reporting standards related to four groups: The Longitudinal design (four standards); the Appropriate data (four standards); the Evaluation - experts, materials, and procedures (ten standards); and the Validity group (two standards).

Conclusions

We hope that the LEADING guideline will be useful in assisting researchers in planning, conducting, reporting, and evaluating research aiming to achieve best-estimate assessments.

Neuroimaging Biomarkers in Addiction

As a neurobiological process, addiction involves pathological patterns of engagement with substances and a range of behaviors with a chronic and relapsing course. Neuroimaging technologies assess brain activity, structure, physiology, and metabolism at scales ranging from neurotransmitter receptors to large-scale brain networks, providing unique windows into the core neural processes implicated in substance use disorders. Identified aberrations in the neural substrates of reward and salience processing, response inhibition, interoception, and executive functions with neuroimaging can inform the development of pharmacological, neuromodulatory, and psychotherapeutic interventions to modulate the disordered neurobiology. Based on our systematic search, 409 protocols registered on ClinicalTrials.gov include the use of one or more neuroimaging paradigms as an outcome measure in addiction, with the majority (N=268) employing functional magnetic resonance imaging (fMRI), followed by positron emission tomography (PET) (N=71), electroencephalography (EEG) (N=50), structural magnetic resonance imaging (MRI) (N=35) and magnetic resonance spectroscopy (MRS) (N=35). Furthermore, in a PubMed systematic review, we identified 61 meta-analyses including 30 fMRI, 22 structural MRI, 8 EEG, 7 PET, and 3 MRS meta-analyses suggesting potential biomarkers in addictions. These studies can facilitate the development of a range of biomarkers that may prove useful in the arsenal of addiction treatments in the coming years. There is evidence that these markers of large-scale brain structure and activity may indicate vulnerability or separate disease subtypes, predict response to treatment, or provide objective measures of treatment response or recovery. Neuroimaging biomarkers can also suggest novel targets for interventions. Closed or open loop interventions can integrate these biomarkers with neuromodulation in real-time or offline to personalize stimulation parameters and deliver the precise intervention. This review provides an overview of neuroimaging modalities in addiction, potential neuroimaging biomarkers, and their physiologic and clinical relevance. Future directions and challenges in bringing these putative biomarkers from the bench to the bedside are also discussed.

Deconstructing depression by machine learning: the POKAL-PSY study

Unipolar depression is a prevalent and disabling condition, often left untreated. In the outpatient setting, general practitioners fail to recognize depression in about 50% of cases mainly due to somatic comorbidities. Given the significant economic, social, and interpersonal impact of depression and its increasing prevalence, there is a need to improve its diagnosis and treatment in outpatient care. Various efforts have been made to isolate individual biological markers for depression to streamline diagnostic and therapeutic approaches. However, the intricate and dynamic interplay between neuroinflammation, metabolic abnormalities, and relevant neurobiological correlates of depression is not yet fully understood. To address this issue, we propose a naturalistic prospective study involving outpatients with unipolar depression, individuals without depression or comorbidities, and healthy controls. In addition to clinical assessments, cardiovascular parameters, metabolic factors, and inflammatory parameters are collected. For analysis we will use conventional statistics as well as machine learning algorithms. We aim to detect relevant participant subgroups by data-driven cluster algorithms and their impact on the subjects' long-term prognosis. The POKAL-PSY study is a subproject of the research network POKAL (Predictors and Clinical Outcomes in Depressive Disorders; GRK 2621).

Digital phenotyping data and anomaly detection methods to assess changes in mood and anxiety symptoms across a transdiagnostic clinical sample

INTRODUCTION

Clinical assessment of mood and anxiety change often relies on clinical assessment or self-reported scales. Using smartphone digital phenotyping data and resulting markers of behavior (e.g., sleep) to augment clinical symptom scores offers a scalable and potentially more valid method to understand changes in patients' state. This paper explores the potential of using a combination of active and passive sensors in the context of smartphone-based digital phenotyping to assess mood and anxiety changes in two distinct cohorts of patients to assess the preliminary reliability and validity of this digital phenotyping method.

METHODS

Participants from two different cohorts, each n = 76, one with diagnoses of depression/anxiety and the other schizophrenia, utilized mindLAMP to collect active data (e.g., surveys on mood/anxiety), along with passive data consisting of smartphone digital phenotyping data (geolocation, accelerometer, and screen state) for at least 1 month. Using anomaly detection algorithms, we assessed if statistical anomalies in the combination of active and passive data could predict changes in mood/anxiety scores as measured via smartphone surveys.

RESULTS

The anomaly detection model was reliably able to predict symptom change of 4 points or greater for depression as measured by the PHQ-9 and anxiety as measured for the GAD-8 for both patient populations, with an area under the ROC curve of 0.65 and 0.80 for each respectively. For both PHQ-9 and GAD-7, these AUCs were maintained when predicting significant symptom change at least 7 days in advance. Active data alone predicted around 52% and 75% of the symptom variability for the depression/anxiety and schizophrenia populations respectively.

CONCLUSION

These results indicate the feasibility of anomaly detection for predicting symptom change in transdiagnostic cohorts. These results across different patient groups, different countries, and different sites (India and the US) suggest anomaly detection of smartphone digital phenotyping data may offer a reliable and valid approach to predicting symptom change. Future work should emphasize prospective application of these statistical methods.

Brain cell-type shifts in Alzheimer's disease, autism, and schizophrenia interrogated using methylomics and genetics

Few neuropsychiatric disorders have replicable biomarkers, prompting high-resolution and large-scale molecular studies. However, we still lack consensus on a more foundational question: whether quantitative shifts in cell types-the functional unit of life-contribute to neuropsychiatric disorders. Leveraging advances in human brain single-cell methylomics, we deconvolve seven major cell types using bulk DNA methylation profiling across 1270 postmortem brains, including from individuals diagnosed with Alzheimer's disease, schizophrenia, and autism. We observe and replicate cell-type compositional shifts for Alzheimer's disease (endothelial cell loss), autism (increased microglia), and schizophrenia (decreased oligodendrocytes), and find age- and sex-related changes. Multiple layers of evidence indicate that endothelial cell loss contributes to Alzheimer's disease, with comparable effect size to APOE genotype among older people. Genome-wide association identified five genetic loci related to cell-type composition, involving plausible genes for the neurovascular unit (P2RX5 and TRPV3) and excitatory neurons (DPY30 and MEMO1). These results implicate specific cell-type shifts in the pathophysiology of neuropsychiatric disorders.

Unique Challenges in Biomarkers for Psychotic Disorders

Biomarkers are observations that provide information about the risk of certain conditions (predictive) or their underlying mechanisms (explanatory) [...].

Electrophysiological biomarkers in dual pathology

INTRODUCTION

The co-occurrence of substance use disorder with at least one other mental disorder is called dual pathology, which in turn is characterised by heterogeneous symptoms that are difficult to diagnose and have a poor response to treatment. For this reason, the identification and validation of biomarkers is necessary. Within this group, possible electroencephalographic biomarkers have been reported to be useful in diagnosis, treatment and follow-up, both in neuropsychiatric conditions and in substance use disorders. This article aims to review the existing literature on electroencephalographic biomarkers in dual pathology.

METHODS

A narrative review of the literature. A bibliographic search was performed on the PubMed, Science Direct, OVID, BIREME and Scielo databases, with the keywords: electrophysiological biomarker and substance use disorder, electrophysiological biomarker and mental disorders, biomarker and dual pathology, biomarker and substance use disorder, electroencephalography, and substance use disorder or comorbid mental disorder.

RESULTS

Given the greater amount of literature found in relation to electroencephalography as a biomarker of mental illness and substance use disorders, and the few articles found on dual pathology, the evidence is organised as a biomarker in psychiatry for the diagnosis and prediction of risk and as a biomarker for dual pathology.

CONCLUSIONS

Although the evidence is not conclusive, it suggests the existence of a subset of sites and mechanisms where the effects of psychoactive substances and the neurobiology of some mental disorders could overlap or interact.

Transdiagnostic biomarkers of mental illness across the lifespan: A systematic review examining the genetic and neural correlates of latent transdiagnostic dimensions of psychopathology in the general population

This systematic review synthesizes evidence from research investigating the biological correlates of latent transdiagnostic dimensions of psychopathology (e.g., the p-factor, internalizing, externalizing) across the lifespan. Eligibility criteria captured genomic and neuroimaging studies investigating general and/or specific dimensions in general population samples across all age groups. MEDLINE, Embase, and PsycINFO were searched for relevant studies published up to March 2023 and 46 studies were selected for inclusion. The results revealed several biological correlates consistently associated with transdiagnostic dimensions of psychopathology, including polygenic scores for ADHD and neuroticism, global surface area and global gray matter volume. Shared and unique associations between symptom dimensions are highlighted, as are potential age-specific differences in biological associations. Findings are interpreted with reference to key methodological differences across studies. The included studies provide compelling evidence that the general dimension of psychopathology reflects common underlying genetic and neurobiological vulnerabilities that are shared across diverse manifestations of mental illness. Substantive interpretations of general psychopathology in the context of genetic and neurobiological evidence are discussed.

Understanding anxiety symptoms as aberrant defensive responding along the threat imminence continuum

Threat-anticipatory defensive responses have evolved to promote survival in a dynamic world. While inherently adaptive, aberrant expression of defensive responses to potential threat could manifest as pathological anxiety, which is prevalent, impairing, and associated with adverse outcomes. Extensive translational neuroscience research indicates that normative defensive responses are organized by threat imminence, such that distinct response patterns are observed in each phase of threat encounter and orchestrated by partially conserved neural circuitry. Anxiety symptoms, such as excessive and pervasive worry, physiological arousal, and avoidance behavior, may reflect aberrant expression of otherwise normative defensive responses, and therefore follow the same imminence-based organization. Here, empirical evidence linking aberrant expression of specific, imminence-dependent defensive responding to distinct anxiety symptoms is reviewed, and plausible contributing neural circuitry is highlighted. Drawing from translational and clinical research, the proposed framework informs our understanding of pathological anxiety by grounding anxiety symptoms in conserved psychobiological mechanisms. Potential implications for research and treatment are discussed.

Serum metabolomic profiling revealed potential diagnostic biomarkers in patients with panic disorder

BACKGROUND

Currently, specific metabolites and diagnostic biomarkers of panic disorder (PD) patients have not been identified in clinical practice. The aim of this study was to explore metabolites and metabolic pathways in serum through a metabolomics method.

METHODS

Fifty-five PD patients who completed 2 weeks of inpatient treatment and 55 healthy control subjects (HCs) matched for age, sex and BMI were recruited. Ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) was used to detect metabolites in serum. Multivariate Statistical Analysis was used to identify differential metabolites. The relevant biometabolic pathways were further identified by the online tool MetaboAnalyst 5.0.

RESULTS

43 different metabolites in PD patients compared to HCs (P < 0.05) were screened. Pathway analysis showed that these small molecules were mainly associated with amino acid metabolism. 14 metabolites were significantly changed after 2 weeks of drug treatment (P < 0.05), which were mainly associated with tryptophan metabolism.

CONCLUSION

In conclusion, our analysis of metabolomics of PD patients at baseline and two weeks after treatment screened for differential metabolites that could be potential diagnostic biomarkers involved in PD pathogenesis and influence some biometabolic pathways such as phenylalanine metabolism and tryptophan metabolism. In the future, we can summarize and observe the dynamic changes of differential metabolites that appear more frequently in similar studies to further explore the underlying mechanisms of PD evolution.

Understanding mental health through computers: An introduction to computational psychiatry

Computational psychiatry recently established itself as a new tool in the study of mental disorders and problems. Integration of different levels of analysis is creating computational phenotypes with clinical and research values, and constructing a way to arrive at precision psychiatry are part of this new branch. It conceptualizes the brain as a computational organ that receives from the environment parameters to respond to challenges through calculations and algorithms in continuous feedback and feedforward loops with a permanent degree of uncertainty. Through this conception, one can seize an understanding of the cerebral and mental processes in the form of theories or hypotheses based on data. Using these approximations, a better understanding of the disorder and its different determinant factors facilitates the diagnostics and treatment by having an individual, ecologic, and holistic approach. It is a tool that can be used to homologate and integrate multiple sources of information given by several theoretical models. In conclusion, it helps psychiatry achieve precision and reproducibility, which can help the mental health field achieve significant advancement. This article is a narrative review of the basis of the functioning of computational psychiatry with a critical analysis of its concepts.

Nosology of behavioral addictions: Intersections with philosophy of psychiatry •

Writing in this journal, Brand and colleagues have proposed criteria for other specified disorders due to addictive behaviors. Their proposal intersects with key debates in philosophy of psychiatry, including how best to define mental disorders, to validate them, and to optimize their meta-structure. Review of these debates in the context of behavioral addictions suggests several conclusions. First, these debates involve "essentially contested" constructs that require ongoing consideration and judgment. Second, the complexity of psychopathology suggests multiple legitimate approaches to delineating traits and explicating mechanisms. Third, in optimizing meta-structure, non-psychobiological considerations are crucial - the overlapping public mental health approach to addictive disorders is paramount.

Electroencephalogram aperiodic power spectral slope can be reliably measured and predicts ADHD risk in early development

The aperiodic exponent of the electroencephalogram (EEG) power spectrum has received growing attention as a physiological marker of neurodevelopmental psychopathology, including attention-deficit/hyperactivity disorder (ADHD). However, its use as a marker of ADHD risk across development, and particularly in very young children, is limited by unknown reliability, difficulty in aligning canonical band-based measures across development periods, and unclear effects of treatment in later development. Here, we investigate the internal consistency of the aperiodic EEG power spectrum slope and its association with ADHD risk in both infants (n = 69, 1-month-old) and adolescents (n = 262, ages 11-17 years). Results confirm good to excellent internal consistency in infancy and adolescence. In infancy, a larger aperiodic exponent was associated with greater family history of ADHD. In contrast, in adolescence, ADHD diagnosis was associated with a smaller aperiodic exponent, but only in children with ADHD who had not received stimulant medication treatment. Results suggest that disruptions in cortical development associated with ADHD risk may be detectable shortly after birth via this approach. Together, findings imply a dynamic developmental shift in which the developmentally normative flattening of the EEG power spectrum is exaggerated in ADHD, potentially reflecting imbalances in cortical excitation and inhibition that could contribute to long-lasting differences in brain connectivity.

What we learn about bipolar disorder from large-scale neuroimaging: Findings and future directions from the ENIGMA Bipolar Disorder Working Group

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness.

Investigating the molecular genetic, genomic, brain structural, and brain functional correlates of latent transdiagnostic dimensions of psychopathology across the lifespan: Protocol for a systematic review and meta-analysis of cross-sectional and longitudinal studies in the general population

BACKGROUND

Research using latent variable modelling has identified a superordinate general dimension of psychopathology, as well as several specific/lower-order transdiagnostic dimensions (e.g., internalising and externalising) within the meta-structure of psychiatric symptoms. These models can facilitate discovery in genetic and neuroscientific research by providing empirically derived psychiatric phenotypes, offering greater validity and reliability than traditional diagnostic categories. The prospective review outlined in this protocol aims to integrate and assess evidence from research investigating the biological correlates of general psychopathology and specific/lower-order transdiagnostic symptom dimensions. Cross-sectional and longitudinal studies investigating general population samples of any age group or developmental period will be included to capture evidence from across the lifespan.

METHODS AND ANALYSIS

MEDLINE, Embase, and PsycINFO databases will be systematically searched for relevant literature. The review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Eligibility criteria were designed to capture psychiatric genetic (i.e., molecular genetic and genomic) and neuroimaging (i.e., brain structural and brain functional) studies investigating latent transdiagnostic dimension(s) or structural model(s) of psychopathology across any age group. Studies which include or exclude participants based on clinical symptoms, disorders, or relevant risk factors (e.g., history of abuse, neglect, and trauma) will be excluded. Biometric genetic research (e.g., twin and family studies), candidate gene studies, neurophysiology studies, and other non-imaging based neuroscientific studies (e.g., post-mortem studies) will be excluded. Study quality and risk of bias will be assessed using the Joanna Briggs Checklist for Analytical Cross-Sectional Studies, the Joanna Briggs Checklist for Cohort Studies, and the Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) system. Meta-analysis will be conducted if sufficient data is available.

DISCUSSION

This protocol outlines the first systematic review to examine evidence from studies investigating the latent structure and underlying biology of psychopathology and to characterise these relationships developmentally across the lifespan. The prospective review will cover a broad range of statistical techniques and models used to investigate latent transdiagnostic dimensions of psychopathology, as well as a numerous genetic and neuroscientific methods.

SYSTEMATIC REVIEW REGISTRATION

[https://www.crd.york.ac.uk/prospero/], identifier[CRD42021262717].

Methodological and clinical challenges associated with biomarkers for psychiatric disease: A scoping review

Over the past decade, psychiatric research has been on an important hunt for biomarkers of psychiatric disease. In psychiatry, the term "biomarker" is a broad umbrella term used to identify any biological variable that can be objectively measured and applied to a diagnosis; this includes genetic and epigenetic assessments, hormone levels, measures of neuro-anatomy and many other scientific modalities. However, despite hundreds of studies on the topic being published yearly and other medical specialties having success in discovering biomarkers, clinical psychiatric practice has not had the same success. This paper aims to consolidate the many opinions on the search for psychiatric biomarkers to suggest key methodological and clinical challenges that psychiatric biomarker research faces. Psychiatry as a specialty has many fundamental differences compared to other medical specialties in methods of diagnosing, underlying etiology and disease pathologies that may be limiting the success of biomarker research in itself and puts strict requirements on the research being conducted. The academic and clinical environment in which the research is being conducted also heavily influences the translation of the findings. Finally, once biomarkers are identified, more often than not they are inapplicable to clinical settings, unable to integrate into clinical practice and fail to outperform current diagnostic practices and guidelines. We also make six recommendations for more promising future research in psychiatric biomarkers.

Neurocognitive endophenotypes in pediatric OCD probands, their unaffected parents and siblings

BACKGROUND

Limited extant research on neurocognitive endophenotypes in obsessive-compulsive disorder (OCD) show inconsistent results. Limitations of this body of literature include small sample sizes, strict exclusion criteria, lack of objective standard normalized test scores, and significant lack of studies utilizing pediatric probands. This study aimed to address these limitations.

METHODS

A large carefully screened cohort of pediatric OCD (n = 102), their unaffected siblings (n = 78), and parents (n = 164), completed a neuropsychological battery. To compare participants at different ages and developmental stages, standard scores were computed using test norms. Cluster-robust regression with sample size-adjusted sandwich estimates of variance, and interclass correlations were computed. False Discovery Rate procedures were employed to correct for multiplicity.

RESULTS

Probands, siblings and parents demonstrated deficient task performance (Z < -0.5) on the 'number of trials to complete first category' on the Wisconsin Card Sorting Test, and on the Stroop color naming trials. Compared to test norms, the three groups exhibited medium to large effect sizes on these outcome measures. No other meaningful familial trends were found.

CONCLUSIONS

OCD probands, their unaffected siblings and parents exhibited deficiencies in specific subdomains of cognitive flexibility and inhibitory control, namely, initial concept formation and proactive control, which may be valid candidate neurocognitive endophenotypes of OCD. No other meaningful familial effect has been found on other functions, including other executive function indices such as perseverations and interference control. These results highlight the need to carefully examine individual outcomes from executive function tests instead of the tendency to focus largely on major outcome measures.

Towards Clinically Relevant Oculomotor Biomarkers in Early Schizophrenia

In recent years, psychiatric research has focused on the evaluation and implementation of biomarkers in the clinical praxis. Oculomotor function deviances are among the most consistent and replicable cognitive deficits in schizophrenia and have been suggested as viable candidates for biomarkers. In this narrative review, we focus on oculomotor function in first-episode psychosis, recent onset schizophrenia as well as individuals at high risk for developing psychosis. We critically discuss the evidence for the possible utilization of oculomotor function measures as diagnostic, susceptibility, predictive, monitoring, and prognostic biomarkers for these conditions. Based on the current state of research we conclude that there are not sufficient data to unequivocally support the use of oculomotor function measures as biomarkers in schizophrenia.

Augmenting Clinical Interventions in Psychiatric Disorders: Systematic Review and Update on Nutrition

There is a strong relationship between a healthy diet and mental well-being. Several foods and food compounds are known to modulate biomarkers and molecular mechanisms involved in the aetiogenesis of several mental disorders, and this can be useful in containing the disease progression, including its prophylaxis. This is an updated systematic review of the literature to justify the inclusion and recognition of nutrition in the management of psychiatric illnesses. Such foods and their compounds include dietary flavanols from fruits and vegetables, notable antioxidant and anti-inflammatory agents, probiotics (fermented foods) known to protect good gut bacteria, foods rich in polyunsaturated fatty acids (e.g., Omega-3), and avoiding diets high in saturated fats and refined sugars among others. While the exact mechanism(s) of mitigation of many nutritional interventions are yet to be fully understood, the evidence-based approach warrants the inclusion and co-recognition of nutrition in the management of psychiatric illnesses. For the greater public health benefit, there is a need for policy advocacy aimed at bridging the knowledge gap and encouraging the integration of nutritional intervention with contemporary therapies in clinical settings, as deficiencies of certain nutrients make therapy difficult even with appropriate medication.

Biological Markers in Anxiety Disorders

Anxiety disorders are one of the most commonly reported disorders in psychiatry, causing a high medical and socio-economic burden. Recently, there has been a soaring interest in the biological basis of anxiety disorders, which is reflected in an increasing number of articles related to the topic. Due to the ambiguity of the diagnosis and a large number of underdiagnosed patients, researchers are looking for laboratory tests that could facilitate the diagnosis of anxiety disorders in clinical practice and would allow for the earliest possible implementation of appropriate treatment. Such potential biomarkers may also be useable in monitoring the efficacy of pharmacological therapy for anxiety disorders. Therefore this article reviews the literature of potential biomarkers such as components of saliva, peripheral blood, cerebrospinal fluid (CSF), and neuroimaging studies. There are promising publications in the literature that can be useful. The most valuable and promising markers of saliva are cortisol, lysozyme, and α-amylase (sAA). In the blood, in turn, we can distinguish serotonin, brain-derived serum neurotrophic factor (BDNF), cortisol, and microRNA. Structural changes in the amygdala and hippocampus are promising neuroimaging markers, while in CSF, potential markers include oxytocin and 5-Hydroxyindoleacetic acid (5-HIAA). Unfortunately, research in the field of biomarkers is hampered by insufficient knowledge about the etiopathogenesis of anxiety disorders, the significant heterogeneity of anxiety disorders, frequent comorbidities, and low specificity of biomarkers. The development of appropriate biomarker panels and their assessment using new approaches may have the prospective to overcome the above-mentioned obstacles.

Back to Basics: The Importance of Measurement Properties in Biological Psychiatry

Biological psychiatry is a major funding priority for organizations that fund mental health research (e.g., National Institutes of Health). Despite this, some have argued that the field has fallen short of its considerable promise to meaningfully impact the classification, diagnosis, and treatment of psychopathology. This may be attributable in part to a paucity of research about key measurement properties ("physiometrics") of biological variables as they are commonly used in biological psychiatry research. Specifically, study designs informed by physiometrics are more likely to be replicable, avoid poor measurement that results in misestimation, and maximize efficiency in terms of time, money, and the number of analyses conducted. This review describes five key physiometric principles (internal consistency, dimensionality, method-specific variance, temporal stability, and temporal specificity), illustrates how lack of understanding about these characteristics imposes meaningful limitations on research, and reviews examples of physiometric studies featuring a variety of popular biological variables to illustrate how this research can be done and substantive conclusions drawn about the variables of interest.

The C Factor: Cognitive dysfunction as a transdiagnostic dimension in psychopathology

Research into cognitive functions across psychological disorders suggests that cognitive deficiencies may be present across multiple disorders, potentially pointing to a transdiagnostic phenomenon. More recently, a single dimension model of psychopathology, the p factor, has been proposed, in which cognitive deficits are thought to be an intrinsic construct, assumed to be transdiagnostic. However, no systematic investigation to date tested this hypothesis. The aim of the present study was to systematically review meta-analyses to assess the hypothesis that the C factor (cognitive dysfunction) is transdiagnostic in psychopathology and review potential moderators that may account for such a phenomenon. We conducted a systematic review of meta-analyses examining cognitive function across all disorders for which data were available. Included meta-analyses (n = 82), comprising 97 clinical samples, yielded 1,055 effect sizes. Twelve major disorders/categories (e.g., bipolar disorder, substance use disorders) were included, comprising 29 distinct clinical entities (e.g., euthymic bipolar disorder; alcohol use disorder). Results show that all disorders reviewed are associated with underperformance across cognitive domains, supporting the hypothesis that the C factor (or cognitive dysfunction) is a transdiagnostic factor related to p. To examine moderators that may explain or contribute to c, we first consider important interpretative limitations of neuropsychological data in psychopathology. More crucially, we review oft-neglected motivational and emotional transdiagnostic constructs of p, as prominent contributing constructs to the C factor. These constructs are offered as a roadmap for future research examining these constructs related to p, that contribute, and may account for cognitive dysfunctions in psychopathology.

IL-33 in Mental Disorders

Mental disorders are common in the general population; every year about 25% of the total European population is affected by a mental condition. The prevalence of psychiatric disorders might be underestimated. Emerging evidence highlights the role of immune response as a key factor in MDs. Immunological biomarkers seem to be related to illness progression and to treatment effectiveness; several studies suggest strong associations among IL-6, TNFa, S100b, IL 1b, and PCR with affective or schizophrenic disorders. The purpose of this review is to examine and to understand the possible link between mental disorders and interleukin 33 to clarify the role of this axis in the immune system. We found 13 research papers that evaluated interleukin 33 or interleukin 31 levels in subjects affected by mental disorders. Eight studies investigated cytokines in affective disorders. Three studies measured levels of IL-33 in schizophrenia and two studies focused on patients affected by autism spectrum disorders. Alterations in brain structure and neurodevelopmental outcome are affected by multiple levels of organization. Disorders of the autoimmune response, and of the IL-33/31 axis, may therefore be one of the factors involved in this process. These results support the evidence that alarmins, particularly the IL-33/31 axis, need more consideration among researchers and practitioners.

Resting State Functional Connectivity Biomarkers of Treatment Response in Mood Disorders: A Review

There are currently no validated treatment biomarkers in psychiatry. Resting State Functional Connectivity (RSFC) is a popular method for investigating the neural correlates of mood disorders, but the breadth of the field makes it difficult to assess progress toward treatment response biomarkers. In this review, we followed general PRISMA guidelines to evaluate the evidence base for mood disorder treatment biomarkers across diagnoses, brain network models, and treatment modalities. We hypothesized that no treatment biomarker would be validated across these domains or with independent datasets. Results are organized, interpreted, and discussed in the context of four popular analytic techniques: (1) reference region (seed-based) analysis, (2) independent component analysis, (3) graph theory analysis, and (4) other methods. Cortico-limbic connectivity is implicated across studies, but there is no single biomarker that spans analyses or that has been replicated in multiple independent datasets. We discuss RSFC limitations and future directions in biomarker development.

On Dimensionality, Measurement Invariance, and Suitability of Sum Scores for the PHQ-9 and the GAD-7

In psychiatry, severity of mental health conditions and their change over time are usually measured via sum scores of items on psychometric scales. However, inferences from such scores can be biased if psychometric properties such as unidimensionality and temporal measurement invariance for instruments are not met. Here, we aimed to evaluate these properties for common measures of depression (Patient Health Questionnaire-9) and anxiety (Generalized Anxiety Disorder Assessment-7) in a large clinical sample (N = 22,362) undergoing psychotherapy. In addition, we tested consistency in dimensionality results across different methods (parallel analysis, factor analysis, explained common variance, the partial credit model, and the Mokken model). Results showed that while both Patient Health Questionnaire-9 and Generalized Anxiety Disorder Assessment-7 are multidimensional instruments with highly correlated factors, there is justification for sum scores as measures of severity. Temporal measurement invariance across 10 therapy sessions was evaluated. Strict temporal measurement invariance was established in both scales, allowing researchers to compare sum scores as severity measures across time.

Neurobiological evidence of longer-term physical activity interventions on mental health outcomes and cognition in young people: A systematic review of randomised controlled trials

OBJECTIVE

To investigate putative neurobiological mechanisms that link longer-term physical activity interventions to mental health and cognitive outcomes using randomised controlled trials in children, adolescents and young adults.

DATA SOURCES

A range of medical and psychological science electronic databases were searched (MEDLINE, EMBASE, Scopus, Web of Science, PsychINFO).

REVIEW METHODS

Original research studies were selected, data were extracted and quality was appraised.

RESULTS

Sixteen primary papers were included, ranging from healthy and community samples to subclinical and clinical populations across a variety of age ranges and using different neurobiological measures (e.g. magnetic resonance imaging, electroencephalography, cortisol, brain-derived neurotropic factor).

DISCUSSION

The majority of studies report improvement in mental health and cognition outcomes following longer-term physical activity interventions which coincide with neurobiological alterations, especially neuroimaging alterations in activation and electrophysiological parameters in frontal areas. Future research should include measures of pre-existing fitness and target those who would benefit the most from this type of intervention (e.g. those with a lower level of fitness and at risk for or with mental health problems).

Biomarkers in psychiatry: a clinician's viewpoint

INTRODUCTION

The dearth of biomarkers limits the precision of our research into pathogenesis of psychiatric disorders and has slowed down the development of new drugs. In clinical practice, it undermines the validity of psychiatric diagnoses and hampers the delivery of personalized treatment.

SOURCES OF DATA

The data quoted in this paper are gathered from a range of sources encompassing scientific and journalistic both in print and electronic.

AREAS OF AGREEMENT

Availability of clinically useful biomarkers will improve the prognosis and outcome of psychiatric patients by helping in early diagnosis and delivery of individualized treatment.

AREAS OF CONTROVERSY

The cross-sectional and longitudinal observation of psychopathology is the bedrock of current clinical practice. Are psychiatric biomarkers advanced enough to supplant it?

GROWING POINTS

The need for biomarkers of psychiatric disorders has become more acute with the advent of new treatments which require precision and an individualized approach.

AREAS TIMELY FOR DEVELOPING RESEARCH

Identification and deployment of intermediate phenotypes in classification, research and clinical practice of psychiatry.

The role of Experiential Avoidance in transdiagnostic compulsive behavior: A structural model analysis

Compulsivity is recognized as a transdiagnostic phenotype, underlying a variety of addictive and obsessive-compulsive behaviors. However, current understanding of how it should be operationalized and the processes contributing to its development and maintenance is limited. The present study investigated if there was a relationship between the affective process Experiential Avoidance (EA), an unwillingness to tolerate negative internal experiences, and the frequency and severity of transdiagnostic compulsive behaviors. A large sample of adults (N = 469) completed online questionnaires measuring EA, psychological distress and the severity of seven obsessive-compulsive and addiction-related behaviors. Using structural equation modelling, results indicated a one-factor model of compulsivity was superior to the two-factor model (addictive- vs OCD-related behaviors). The effect of EA on compulsivity was fully mediated by psychological distress, which in turn had a strong direct effect on compulsivity. This suggests distress is a key mechanism in explaining why people with high EA are more prone to compulsive behaviors. The final model explained 41% of the variance in compulsivity, underscoring the importance of these constructs as likely risk and maintenance factors for compulsive behavior. Implications for designing effective psychological interventions for compulsivity are discussed.

Changes in Non-Coding RNA in Depression and Bipolar Disorder: Can They Be Used as Diagnostic or Theranostic Biomarkers?

The similarities between the depressive symptoms of Major Depressive Disorders (MDD) and Bipolar Disorders (BD) suggest these disorders have some commonality in their molecular pathophysiologies, which is not apparent from the risk genes shared between MDD and BD. This is significant, given the growing literature suggesting that changes in non-coding RNA may be important in both MDD and BD, because they are causing dysfunctions in the control of biochemical pathways that are affected in both disorders. Therefore, understanding the changes in non-coding RNA in MDD and BD will lead to a better understanding of how and why these disorders develop. Furthermore, as a significant number of individuals suffering with MDD and BD do not respond to medication, identifying non-coding RNA that are altered by the drugs used to treat these disorders offer the potential to identify biomarkers that could predict medication response. Such biomarkers offer the potential to quickly identify patients who are unlikely to respond to traditional medications so clinicians can refocus treatment strategies to ensure more effective outcomes for the patient. This review will focus on the evidence supporting the involvement of non-coding RNA in MDD and BD and their potential use as biomarkers for treatment response.

Exploiting Circulating MicroRNAs as Biomarkers in Psychiatric Disorders

Non-invasive peripheral biomarkers play a significant role in both disease diagnosis and progression. In the past few years, microRNA (miRNA) expression changes in circulating peripheral tissues have been found to be correlative with changes in neuronal tissues from patients with neuropsychiatric disorders. This is a notable quality of a biomolecule to be considered as a biomarker for both prognosis and diagnosis of disease. miRNAs, members of the small non-coding RNA family, have recently gained significant attention due to their ability to epigenetically influence almost every aspect of brain functioning. Empirical evidence suggests that miRNA-associated changes in the brain are often translated into behavioral changes. Current clinical understanding further implicates their role in the management of major psychiatric conditions, including major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ). This review aims to critically evaluate the potential advantages and disadvantages of miRNAs as diagnostic/prognostic biomarkers in psychiatric disorders as well as in treatment response.

Current challenges and possible future developments in personalized psychiatry with an emphasis on psychotic disorders

A personalized medicine approach seems to be particularly applicable to psychiatry. Indeed, considering mental illness as deregulation, unique to each patient, of molecular pathways, governing the development and functioning of the brain, seems to be the most justified way to understand and treat disorders of this medical category. In order to extract correct information about the implicated molecular pathways, data can be drawn from sampling phenotypic and genetic biomarkers and then analyzed by a machine learning algorithm. This review describes current difficulties in the field of personalized psychiatry and gives several examples of possibly actionable biomarkers of psychotic and other psychiatric disorders, including several examples of genetic studies relevant to personalized psychiatry. Most of these biomarkers are not yet ready to be introduced in clinical practice. In a next step, a perspective on the path personalized psychiatry may take in the future is given, paying particular attention to machine learning algorithms that can be used with the goal of handling multidimensional datasets.

The impact of sample processing on inflammatory markers in serum: Lessons learned

Objectives: To investigate the effect of sample handling on inflammatory cytokines in serum and highlight challenges with using samples pre-collected from biobanks for biomarker research.Methods: Cytokine concentrations (IL-1β, IL-2, IL-6, IL-8, IL-10, TNFα, and IFNγ) were measured in serum samples of 205 patients with bipoldar disorder (BD) from the Mayo Clinic Bipolar Disorder Biobank and 205 non-psychiatric controls from the Mayo Clinic Biobank. As cytokine concentrations varied by recruitment site, post-hoc models were used to test the effect of clinical variables and pre-processing time on cytokines. To evaluate the effect of pre-processing time experimentally, cytokines were assayed in serum and plasma from 6 healthy volunteers processed at different time points.Results: Cytokine levels were significantly higher in the BD group. However, both cytokine levels and pre-processing times differed by recruitment site, and post-hoc analyses revealed that pre-processing time was significantly associated with several cytokines. An experiment using samples from healthy volunteers confirmed that concentrations for most cytokines increased with longer pre-processing times.Conclusions: Delays in processing influence cytokine concentrations in blood samples. Given the increasing use of biobanks in research, this study highlights the need to carefully evaluate sample collection and handling methods when designing biomarker studies.

The Heterogeneity of Mental Health Assessment

Across the landscape of mental health research and diagnosis, there is a diverse range of questionnaires and interviews available for use by clinicians and researchers to determine patient treatment plans or investigate internal and external etiologies. Although individually, these tools have each been assessed for their validity and reliability, there is little research examining the consistency between them in terms of what symptoms they assess, and how they assess those symptoms. Here, we provide an analysis of 126 different questionnaires and interviews commonly used to diagnose and screen for 10 different disorder types including depression, anxiety, obsessive compulsive disorder (OCD), post-traumatic stress disorder (PTSD), attention deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), addiction, bipolar disorder, eating disorder, and schizophrenia, as well as comparator questionnaires and interviews that offer an all-in-one cross-disorder assessment of mental health. We demonstrate substantial inconsistency in the inclusion and emphasis of symptoms assessed within disorders as well as considerable symptom overlap across disorder-specific tools. Within the same disorder, similarity scores across assessment tools ranged from 29% for assessment of bipolar disorder to a maximum of 58% for OCD. Furthermore, when looking across disorders, 60% of symptoms were assessed in at least half of all disorders illustrating the extensive overlap in symptom profiles between disorder-specific assessment tools. Biases in assessment toward emotional, cognitive, physical or behavioral symptoms were also observed, further adding to the heterogeneity across assessments. Analysis of other characteristics such as the time period over which symptoms were assessed, as well as whether there was a focus toward frequency, severity or duration of symptoms also varied substantially across assessment tools. The consequence of this inconsistent and heterogeneous assessment landscape is that it hinders clinical diagnosis and treatment and frustrates understanding of the social, environmental, and biological factors that contribute to mental health symptoms and disorders. Altogether, it underscores the need for standardized assessment tools that are more disorder agnostic and span the full spectrum of mental health symptoms to aid the understanding of underlying etiologies and the discovery of new treatments for psychiatric dysfunction.

Developing Diagnostic Frameworks in Veterinary Behavioral Medicine: Disambiguating Separation Related Problems in Dogs

Diagnoses are widely used in both human and veterinary medicine to describe the nature of a condition; by contrast, syndromes are collections of signs that consistently occur together to form a characteristic presentation. Treatment of syndromes, due to either their lack of a clear biological cause or multiple causes, necessarily remains non-specific. However, the discovery of interventions may help refine the definition of a syndrome into a diagnosis. Within the field of veterinary behavioral medicine, separation related problems (SRPs) provide a good example of a syndrome. We describe here a comprehensive process to develop a diagnostic framework (including quality control assessments), for disambiguating the signs of SRPs as an example of a heterogeneous behavioral syndrome in non-human animals requiring greater diagnostic and treatment precision. To do this we developed an online questionnaire (243 items) that covered the full spectrum of theoretical bases to the syndrome and undertook a large-scale survey of the presenting signs of dogs with one or more of the signs of SRPs (n = 2,757). Principal components analysis (n1 = 345), replicated in a second sample (n2 = 417; total n = 762), was used to define the structure of variation in behavioral presentation, while hierarchical agglomerative cluster analysis cross checked with the partitioned around medoids method was used to determine sub-populations. A total of 54 signs were of value in defining a latent structure consisting of seven principal components (termed "exit frustration," "social panic," "elimination," "redirected frustration," "reactive communication," "immediate frustration," "noise sensitivity"), which divided the population in four clusters (termed "exit frustration," "redirected reactive," "reactive inhibited" and "boredom" related SRPs) with 11 sub-clusters (3, 3, 3, and 2, respectively). We used a bottom-up data-driven approach with numerous quality checks for the definition of robust clusters to provide a robust methodology for nosological studies in veterinary behavioral medicine, that can extend our understanding of the nature of problems beyond SRPs. This provides a solid foundation for future work examining aetiological, and differential treatment outcomes, that will allow both more effective treatment and prevention programmes, based on a fully appreciation of the nature of the problem of concern.

The Diagnostic Value of the Combination of Serum Brain-Derived Neurotrophic Factor and Insulin-Like Growth Factor-1 for Major Depressive Disorder Diagnosis and Treatment Efficacy

BACKGROUND

Last decades of psychiatric investigations have been marked by a search for biological markers that can clarify etiology and pathogenesis, confirm the diagnosis, screen individuals at risk, define the severity, and predict the course of mental disorders. In our study, we aimed to evaluate if BDNF and IGF-1 serum concentrations separately and in combination might be used as biomarkers for major depressive disorder (MDD) diagnosis and treatment efficacy and to evaluate the relationships among those proteins and clinical parameters of MDD.

METHODS

Forty-one MDD patients (according to DSM-5) and 32 healthy controls (HC) were included in this study. BDNF and IGF-1 serum concentrations, psychopathological (MADRS, CGI) and neuropsychological parameters (PDQ-5, RAVLT, TMT-B, DSST), functioning according to Sheehan Disability Scale were analyzed in all subjects at admission and 30 MDD patients after 8 weeks of vortioxetine treatment. Correlational analyses were performed to explore relationships between BDNF and IGF-1 and clinical characteristics. AUC-ROCs were calculated to determine if the value of serum BDNF and IGF-1 levels could serve for MDD diagnosis.

RESULTS

MDD patients had significantly lower serum BDNF (727.6 ± 87.9 pg/ml vs. 853.0 ± 93.9 pg/ml) and higher serum IGF-1 levels (289.15 ± 125.3 ng/ml vs. 170.2 ± 58.2 ng/ml) compared to HC. Significant correlations were obtained between BDNF levels and MDD status, depressive episode (DE) severity, precipitating factors, executive functions disruption (TMT-B, RAVLT immediate recall scores) and all subdomains of functioning. As for IGF-1, correlations were found between IGF-1 level and MDD status, DE severity, number and duration of DE, parameters of subjective and objective cognitive functioning (PDQ-5, RAVLT, TMT-B, DSST scores), and all subdomains of functioning. The associations between IGF-1 concentrations and cognitive tests' performance were stronger than those of BDNF. Separately both BDNF and IGF-1 demonstrated good discriminating ability for MDD diagnosis with AUC of 0.840 and 0.824, respectively. However, the combination of those neurotrophins had excellent diagnostic power to discriminate MDD patients from HC, providing an AUC of 0.916. Vortioxetine treatment significantly increased BDNF and attenuated IGF-1 serum concentrations, improved all psychopathological and neuropsychological parameters and functioning.

CONCLUSIONS

The combination of IGF-1 and BDNF might be considered as a diagnostic combination for MDD.

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.

Biological and Clinical Markers to Differentiate the Type of Anxiety Disorders

The present chapter is an overview of possible biomarkers which distinguish anxiety disorders as classified by the DSM-5. Structural or activity changes in the brain regions; changes in N-acetylaspartate/creatine, dopamine, serotonin, and oxytocin; hearth rate variability; hypothalamic-pituitary-adrenal axis activity; error-related negativity; respiratory regulation; and genetic variants are proposed. However, their clinical utility is questionable due to low specificity and sensitivity: the majority does not distinguish subjects with different anxiety disorders, and they might be influenced by stress, comorbidity, physical activity, and psychotropic medications. In this framework, the staging model, a clinimetric tool which allows to define the degree of progression of a disease at a point in time and where the patient is located on the continuum of the course of the disease, is proposed since several DSM anxiety disorders take place at different stages of the same syndrome according to the staging model. Thus, a stage-specific biomarker model for anxiety disorders is hypothesized and illustrated.

The duality of human cognition: operations and intentionality in mental life and illness

What people think about, the intentional aspect of cognition, is distinguished from its operational aspect, or how proficiently they think. Many psychiatric disorders as well as social problems like racism, are defined largely by specified thought contents, whereas neurological disorders including dementia are defined by low proficiency. Intentionality contrasts with operational cognition in resisting objectification and in being expressed primarily in verbal narratives and subjective self-disclosure. This yields insecure data that have slowed progress in fields where intentional cognition plays a key role. The question is how to produce more secure knowledge and open the intentional domain itself to objective investigation. The use of operational methods to infer intentionality has provided only partial answers. However, the science of reconstructing mental events with neural data is providing a new horizon for the study of intentional cognition. Reconstruction science must address major challenges related to fidelity and validity. Nevertheless, this approach is showing the first steps on the road to accessing and revealing objectively the contents of thought.

Pre-treatment peripheral biomarkers associated with treatment response in panic symptoms in patients with major depressive disorder and panic disorder: A 12-week follow-up study

OBJECTIVE

Peripheral biomarkers have been studied to predict treatment response of panic symptoms. We hypothesized that depressive disorder (MDD) vs. panic disorder (PD) would exhibit different peripheral biomarkers, and their correlation with severity of panic attacks (PA) would also differ.

METHODS

Forty-one MDD patients, 52 PD patients, and 59 healthy controls were followed for 12 weeks. We measured peripheral biomarkers along with the Panic Disorder Severity Scale (PDSS) at each visit-pre-treatment, 2, 4, 8, and 12 weeks on a regular schedule. Peripheral biomarkers including serum cytokines, plasma and serum brain-derived neurotrophic factor (BDNF), leptin, adiponectin, and C-reactive protein (CRP) were quantified using enzyme-linked immunosorbent assay (ELISA).

RESULTS

Patients with MDD and PD demonstrated significantly higher levels of pre-treatment IL-6 compared to controls, but no differences were seen in plasma and serum BDNF, leptin, adiponectin, and CRP. Pre-treatment leptin showed a significant clinical correlation with reduction of panic symptoms in MDD patients at visit 5 (p=0.011), whereas pre-treatment IL-6 showed a negative correlation with panic symptom reduction in PD patients (p=0.022). An improvement in three panic-related items was observed to be positively correlated with pre-treatment leptin in MDD patients: distress during PA, anticipatory anxiety, and occupational interference.

CONCLUSION

Higher pre-treatment leptin was associated with better response to treatment regarding panic symptoms in patients with MDD, while higher IL-6 was associated with worse response regarding panic symptoms in PD patients. Different predictive peripheral biomarkers observed in MDD and PD suggest the need for establishing individualized predictive biomarkers, even in cases of similar symptoms observed in different disorders.

Family-based analyses reveal novel genetic overlap between cytokine interleukin-8 and risk for suicide attempt

BACKGROUND

Suicide is major public health concern. It is imperative to find robust biomarkers so that at-risk individuals can be identified in a timely and reliable manner. Previous work suggests mechanistic links between increased cytokines and risk for suicide, but questions remain regarding the etiology of this association, as well as the roles of sex and BMI.

METHODS

Analyses were conducted using a randomly-ascertained extended-pedigree sample of 1882 Mexican-American individuals (60% female, mean age = 42.04, range = 18-97). Genetic correlations were calculated using a variance components approach between the cytokines TNF-α, IL-6 and IL-8, and Lifetime Suicide Attempt and Current Suicidal Ideation. The potentially confounding effects of sex and BMI were considered.

RESULTS

159 individuals endorse a Lifetime Suicide Attempt. IL-8 and IL-6 shared significant genetic overlap with risk for suicide attempt (ρg = 0.49, pFDR = 7.67 × 10-03; ρg = 0.53, pFDR = 0.01), but for IL-6 this was attenuated when BMI was included as a covariate (ρg = 0.37, se = 0.23, pFDR = 0.12). Suicide attempts were significantly more common in females (pFDR = 0.01) and the genetic overlap between IL-8 and risk for suicide attempt was significant in females (ρg = 0.56, pFDR = 0.01), but not in males (ρg = 0.44, pFDR = 0.30).

DISCUSSION

These results demonstrate that: IL-8 shares genetic influences with risk for suicide attempt; females drove this effect; and BMI should be considered when assessing the association between IL-6 and suicide. This finding represents a significant advancement in knowledge by demonstrating that cytokine alterations are not simply a secondary manifestation of suicidal behavior, but rather, the pathophysiology of suicide attempts is, at least partly, underpinned by the same biological mechanisms responsible for regulating inflammatory response.

REBUS and the Anarchic Brain: Toward a Unified Model of the Brain Action of Psychedelics

This paper formulates the action of psychedelics by integrating the free-energy principle and entropic brain hypothesis. We call this formulation relaxed beliefs under psychedelics (REBUS) and the anarchic brain, founded on the principle that-via their entropic effect on spontaneous cortical activity-psychedelics work to relax the precision of high-level priors or beliefs, thereby liberating bottom-up information flow, particularly via intrinsic sources such as the limbic system. We assemble evidence for this model and show how it can explain a broad range of phenomena associated with the psychedelic experience. With regard to their potential therapeutic use, we propose that psychedelics work to relax the precision weighting of pathologically overweighted priors underpinning various expressions of mental illness. We propose that this process entails an increased sensitization of high-level priors to bottom-up signaling (stemming from intrinsic sources), and that this heightened sensitivity enables the potential revision and deweighting of overweighted priors. We end by discussing further implications of the model, such as that psychedelics can bring about the revision of other heavily weighted high-level priors, not directly related to mental health, such as those underlying partisan and/or overly-confident political, religious, and/or philosophical perspectives. SIGNIFICANCE STATEMENT: Psychedelics are capturing interest, with efforts underway to bring psilocybin therapy to marketing authorisation and legal access within a decade, spearheaded by the findings of a series of phase 2 trials. In this climate, a compelling unified model of how psychedelics alter brain function to alter consciousness would have appeal. Towards this end, we have sought to integrate a leading model of global brain function, hierarchical predictive coding, with an often-cited model of the acute action of psychedelics, the entropic brain hypothesis. The resulting synthesis states that psychedelics work to relax high-level priors, sensitising them to liberated bottom-up information flow, which, with the right intention, care provision and context, can help guide and cultivate the revision of entrenched pathological priors.

Transdiagnostic neuroimaging in psychiatry: A review

Transdiagnostic approach has a long history in neuroimaging, predating its recent ascendance as a paradigm for new psychiatric nosology. Various psychiatric disorders have been compared for commonalities and differences in neuroanatomical features and activation patterns, with different aims and rationales. This review covers both structural and functional neuroimaging publications with direct comparison of different psychiatric disorders, including schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorder, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, conduct disorder, anorexia nervosa, and bulimia nervosa. Major findings are systematically presented along with specific rationales for each comparison.