Stratified medicine for mental disorders

Neural circuit basis of pathological anxiety

Anxiety disorders are the most prevalent mental health conditions worldwide. Unfortunately, the understanding of the precise neurobiological mechanisms that underlie these disorders remains limited. Current diagnostic classifications, based on observable symptoms rather than underlying pathophysiology, do not capture the heterogeneity within and across anxiety disorders. Recent advances in functional neuroimaging have provided new insights into the neural circuits implicated in pathological anxiety, revealing dysfunctions that cut across traditional diagnostic boundaries. In this Review, we synthesize evidence that highlights abnormalities in neurobehavioural systems related to negative valence, positive valence, cognitive systems and social processes. We emphasize that pathological anxiety arises not only from heightened reactivity in acute threat ('fear') circuits but also from alterations in circuits that mediate distant (potential) and sustained threat, reward processing, cognitive control and social processing. We discuss how circuit vulnerabilities can lead to the emergence and maintenance of pathological anxiety. Once established, these neural abnormalities can be exacerbated by maladaptive behaviours that prevent extinction learning and perpetuate anxiety disorders. By delineating the specific neural mechanisms in each neurobiological system, we aim to contribute to a more comprehensive understanding of the neurobiology of anxiety disorders, potentially informing future research directions in this field.

How are illness representations of gambling disorder associated with gambling behaviors? A latent profile analysis

Gambling disorder (GD) is a behavioral addiction that causes serious harm to individuals' mental health and social functioning. This study aimed to explore whether gamblers' illness representations (IR) for GD are related to disordered and/or controlled patterns of gambling under the framework of commonsense model of self-regulation (CSM). Based on CSM, different facets of IR do not exist in isolation but together form a total illness schema; we hence used a person-centered analysis method, latent profile analysis (LPA), to classify gamblers by their overall IR for GD (including consequences, personal and treatment control, timeline cyclical, emotional representations, and coherence) into groups and then tested whether these groups differed in GD symptoms, controlled gambling behaviors, and help-seeking intention. An anonymous online survey was conducted on a social media platform, and valid responses from 589 past-year adult gamblers were collected. LPA results supported a four-profile model, revealing four profiles of GD representations: the weak-perception profile, average profile, tensed-up profile, and rational profile. Significant differences were found in all outcome variables in these four profile groups. In particular, the rational group exhibited the lowest levels of GD symptoms and impaired behavioral control, the highest levels of responsible and controlled gambling, and the highest levels of help-seeking intention, whereas the weak-perception group reported the lowest levels of responsible and controlled gambling behaviors. The findings provide empirical support for the application of CSM to addictive behaviors, as well as preliminary evidence for the potential use of CSM-based interventions for GD prevention.

Efficacy, tolerability, and safety of xanomeline-trospium chloride for schizophrenia: A systematic review and meta-analysis

The United States Food and Drug Administration approved xanomeline-trospium combination for schizophrenia on September-26-2024. We conducted a PRISMA 2020-compliant systematic review with random-effects meta-analysis on the efficacy and safety of xanomeline-trospium in randomized controlled trials in patients with schizophrenia (MEDLINE, EMBASE, Cochrane, PsycINFO, October-01-2024). Co-primary outcomes were Positive And Negative Syndrome Scale (PANSS) total score (standardized mean difference=SMD), and all-cause discontinuation (risk ratio=RR). Cochrane's Risk of Bias (RoB) Tool 2 and GRADE were used. Xanomeline-trospium (k = 3, schizophrenia acute exacerbation, RoB=low, baseline N = 690, males=75.5 %, age=44.3 + 11.0, duration=5 weeks) outperformed placebo on PANSS total (SMD=-0.56, 95 % confidence interval/CI=-0.72/-0.40), positive (SMD=-0.59, 95 %CI=-0.75/-0.43), negative (SMD=-0.33, 95 %CI=-0.49/-0.17), and Marder Factor negative symptom score (SMD=-0.36, 95 %CI=-0.60/-0.13), Clinical Global Impression-Severity (SMD=-0.54, 95 %CI=-0.71/-0.37) (GRADE=moderate), and response (≥30 % reduction from baseline: RR=2.13, 95 %CI=1.66-2.75). Risk of ≥7 % weight gain (RR=0.46, 95 %CI=0.25-0.87, NNT=19), low-density lipoprotein-cholesterol and high-density lipoprotein-cholesterol levels were reduced, while risk was increased for vomiting, hypertension, nausea, dry mouth, dyspepsia, constipation (RR=7.60, 95 %CI=1.50-38.57 to RR=2.72, 95 %CI=1.63-4.55), any adverse event (RR=1.33, 95 %CI=1.18-1.51, NNT=6), triglyceride levels and supine heart rate (GRADE=moderate to high). Conversely, the risk was not increased for any other, serious, or severe adverse events or all-cause discontinuation. In post-hoc analyses, xanomeline-trospium outperformed placebo regarding response (≥20 % and ≥30 % threshold) starting at week 2, negative symptoms in patients with prominent negative symptoms, and cognitive symptoms in patients ≥1 standard deviation below the general population norm. Further, pro-/anti-cholinergic side effects were mild-moderate and mostly transient. Xanomeline-trospium is an effective treatment for schizophrenia with a unique tolerability profile, potentially addressing unmet needs.

Neuroimaging for precision medicine in psychiatry

Although the lifetime burden due to mental disorders is increasing, we lack tools for more precise diagnosing and treating prevalent and disabling disorders such as major depressive disorder. We lack strategies for selecting among available treatments or expediting access to new treatment options. This critical review concentrates on functional neuroimaging as a modality of measurement for precision psychiatry, focusing on major depressive and anxiety disorders. We begin by outlining evidence for the use of functional neuroimaging to stratify the heterogeneity of these disorders, based on underlying circuit dysfunction. We then review the current landscape of how functional neuroimaging-derived circuit predictors can predict treatment outcomes and clinical trajectories in depression and anxiety. Future directions for advancing clinically appliable neuroimaging measures are considered. We conclude by considering the opportunities and challenges of translating neuroimaging measures into practice. As an illustration, we highlight one approach for quantifying brain circuit function at an individual level, which could serve as a model for clinical translation.

Integrating dynamic psychophysiological indices across time and contexts: Elucidating mechanisms, risk markers, and intervention targets

Why should researchers measure psychophysiological processes repeatedly over time? The study of psychophysiology inherently involves sampling biological processes as they manifest over time. The most common approach is to use a brief sample to make conclusions about how individuals or groups differ. Although these types of between-subject comparisons have utility for understanding individual and group differences, many of the important conceptual questions in the field involve processes that are dynamic, varying within individuals over time. Using examples from the literature on affect regulation, this conceptual review contrasts three types of study designs: the classic single-observation design and the aggregated and temporally linked repeated observation designs, which have great promise for measuring variables that fluctuate dynamically over time. Importantly, these designs can be integrated to elucidate research questions about risk (when and for whom will the likelihood of an unwanted outcome occurring increase?), mechanisms (how and why does a change in psychophysiology contribute to a change in another process of interest?), and interventions (how and when should interventions take place to modify an outcome?). Researchers are encouraged to implement intensive sampling in their research, which can be conducted in traditional laboratory settings (e.g., fMRI, event-related brain potentials, and heart rate variability) and in ecologically valid contexts in everyday life using ambulatory assessment.

Brain Circuit-Derived Biotypes for Treatment Selection in Mood Disorders: A Critical Review and Illustration of a Functional Neuroimaging Tool for Clinical Translation

Although the lifetime burden due to major depressive disorder is increasing, we lack tools for selecting the most effective treatments for each patient. One-third to one-half of patients with major depressive disorder do not respond to treatment, and we lack strategies for selecting among available treatments or expediting access to new treatment options. This critical review concentrates on functional neuroimaging as a modality of measurement for precision psychiatry. We begin by summarizing the current landscape of how functional neuroimaging-derived circuit predictors can forecast treatment outcomes in depression. Then, we outline the opportunities and challenges in integrating circuit predictors into clinical practice. We highlight one standardized and reproducible approach for quantifying brain circuit function at an individual level, which could serve as a model for clinical translation. We conclude by evaluating the prospects and practicality of employing neuroimaging tools, such as the one that we propose, in routine clinical practice.

Uncovering the determinants of brain functioning, behavior and their interplay in the light of context

Notwithstanding the huge progress in molecular and cellular neuroscience, our ability to understand the brain and develop effective treatments promoting mental health is still limited. This can be partially ascribed to the reductionist, deterministic and mechanistic approaches in neuroscience that struggle with the complexity of the central nervous system. Here, I introduce the Context theory of constrained systems proposing a novel role of contextual factors and genetic, molecular and neural substrates in determining brain functioning and behavior. This theory entails key conceptual implications. First, context is the main driver of behavior and mental states. Second, substrates, from genes to brain areas, have no direct causal link to complex behavioral responses as they can be combined in multiple ways to produce the same response and different responses can impinge on the same substrates. Third, context and biological substrates play distinct roles in determining behavior: context drives behavior, substrates constrain the behavioral repertoire that can be implemented. Fourth, since behavior is the interface between the central nervous system and the environment, it is a privileged level of control and orchestration of brain functioning. Such implications are illustrated through the Kitchen metaphor of the brain. This theoretical framework calls for the revision of key concepts in neuroscience and psychiatry, including causality, specificity and individuality. Moreover, at the clinical level, it proposes treatments inducing behavioral changes through contextual interventions as having the highest impact to reorganize the complexity of the human mind and to achieve a long-lasting improvement in mental health.

From Lab to Life: Exploring Cutting-Edge Models for Neurological and Psychiatric Disorders

Neuroscience, neurology, and psychiatry are rapidly evolving fields that aim to understand the complex mechanisms underlying brain function and dysfunction, as well as to develop effective interventions for various neurological and psychiatric disorders [...].

A mouse model of the 3-hit effects of stress: Genotype controls the effects of life adversities in females

Helplessness is a dysfunctional coping response to stressors associated with different psychiatric conditions. The present study tested the hypothesis that early and adult adversities cumulate to produce helplessness depending on the genotype (3-hit hypothesis of psychopathology). To this aim, we evaluated whether Chronic Unpredictable Stress (CUS) differently affected coping and mesoaccumbens dopamine (DA) responses to stress challenge by adult mice of the C57BL/6J (B6) and DBA/2J (D2) inbred strains depending on early life experience (Repeated Cross Fostering, RCF). Three weeks of CUS increased the helplessness expressed in the Forced Swimming Test (FST) and the Tail Suspension Test by RCF-exposed female mice of the D2 strain. Moreover, female D2 mice with both RCF and CUS experiences showed inhibition of the stress-induced extracellular DA outflow in the Nucleus Accumbens, as measured by in vivo microdialysis, during and after FST. RCF-exposed B6 mice, instead, showed reduced helplessness and increased mesoaccumbens DA release. The present results support genotype-dependent additive effects of early experiences and adult adversities on behavioral and neural responses to stress by female mice. To our knowledge, this is the first report of a 3-hit effect in an animal model. Finally, the comparative analyses of behavioral and neural phenotypes expressed by B6 and D2 mice suggest some translationally relevant hypotheses of genetic risk factors for psychiatric disorders.

Pre-Molecular Assessment of Self-Processes in Neurotypical Subjects Using a Single Cognitive Behavioral Intervention Evoking Autobiographical Memory

In the last 20 years, several contributions have been published on what concerns the conceptual and empirical connections between self-processes. However, only a limited number of publications addressed the viability of those processes to characterize mental health in neurotypical subjects with a normative pattern of neurodevelopment. Furthermore, even fewer experiments focused explicitly on the complexity of studying neurotypical phenomenal data. On the one hand, this normative pattern is commonly associated with mental health and a multifaceted self-concept and well-being. On the other hand, well-being is often related to a healthy cognitive life. However, how such intricate and complex relation between self-processes is established in neurotypical subjects requires further evidence. The novelty of this work is thus studying the first-person experience, which is correlated with the mental events aroused by a cognitive behavioral intervention. The prior methodology that led to the complete characterization of a neurotypical sample was already published by the authors, although the materials, the methods, the sample screening, and the sample size study required further explanation and exploration. This paper's innovation is hence the phenomenological assessment of subjects' self-regulation, which is used for mental health profiling, providing the basis for subsequent molecular typing. For that matter, a convenience sample of 128 (19-25-year-old) neurotypical young adults, healthy university students at the University of Lisbon, non-medicated and with no serious, uncontrolled, or chronic diseases, are characterized according to their cognitive functioning and self-concept. The procedure comprised (i) a mental status examination (psychological assessment) and (ii) a psychological intervention, i.e., a single cognitive behavioral intervention (intervention protocol). The psychological assessment was a standardized and structured clinical interview, which comprised the use of 4 psychological scales complementary to the classical Mental Status Examination (MSE). The intervention protocol applied a combined exercise of psychophysical training and autobiographical-self memory-recalling. The results permitted identifying and isolating four different subgroups (self awareness, self consciousness, reflective self, and pre-reflective self) in neurotypical subjects with discrete self-processes. The outcome of this study is screening four different aspects of self-reflection and the isolation between various forms of self-directed attention and their interconnections in these four mental health strata. The practical implication of this study is to fulfill an a priori pre-molecular assessment of self-regulation with separate cognitive characteristics. The reliability of these mental strata, their distinct neurophysiology, and discrete molecular fingerprint will be tested in a future publication by in silico characterization, total protein profiling, and simultaneous immunodetection of the neuropeptide and neuroimmune response of the same participants.

Cocaine Use Disorder (CUD): Current Clinical Perspectives

Cocaine use disorder (CUD) is a devastating disorder, impacting both individuals and society. Individuals with CUD face many barriers in accessing treatment for CUD, and most individuals with CUD never receive treatment. In this review, we provide an overview of CUD, including risk factors for CUD, common co-occurring disorders, acute and chronic effects of cocaine use, and currently available pharmacological and behavioral treatments. There are no FDA-approved pharmacological treatments for CUD. Future studies with larger sample sizes and testing treatment combinations are warranted. However, individuals with CUD and co-occurring disorders (eg, a mood or anxiety disorder) may benefit from medication treatments. There are behavioral interventions that have demonstrated efficacy in treating CUD – contingency management (CM) and cognitive-behavioral therapy for substance use disorders (CBT-SUD) in particular – however many barriers remain in delivering these treatments to patients. Following the discussion of current treatments, we highlight some promising emerging treatments, as well as offer a framework that can be used in building a treatment plan for individuals with CUD.

The normative modeling framework for computational psychiatry

Normative modeling is an emerging and innovative framework for mapping individual differences at the level of a single subject or observation in relation to a reference model. It involves charting centiles of variation across a population in terms of mappings between biology and behavior, which can then be used to make statistical inferences at the level of the individual. The fields of computational psychiatry and clinical neuroscience have been slow to transition away from patient versus 'healthy' control analytic approaches, probably owing to a lack of tools designed to properly model biological heterogeneity of mental disorders. Normative modeling provides a solution to address this issue and moves analysis away from case-control comparisons that rely on potentially noisy clinical labels. Here we define a standardized protocol to guide users through, from start to finish, normative modeling analysis using the Predictive Clinical Neuroscience toolkit (PCNtoolkit). We describe the input data selection process, provide intuition behind the various modeling choices and conclude by demonstrating several examples of downstream analyses that the normative model may facilitate, such as stratification of high-risk individuals, subtyping and behavioral predictive modeling. The protocol takes ~1-3 h to complete.

Recentering neuroscience on behavior: The interface between brain and environment is a privileged level of control of neural activity

Despite the huge and constant progress in the molecular and cellular neuroscience fields, our capability to understand brain alterations and treat mental illness is still limited. Therefore, a paradigm shift able to overcome such limitation is warranted. Behavior and the associated mental states are the interface between the central nervous system and the living environment. Since, in any system, the interface is a key regulator of system organization, behavior is proposed here as a unique and privileged level of control and orchestration of brain structure and activity. This view has relevant scientific and clinical implications. First, the study of behavior represents a singular starting point for the investigation of neural activity in an integrated and comprehensive fashion. Second, behavioral changes, accomplished through psychotherapy or environmental interventions, are expected to have the highest impact to specifically reorganize the complexity of the human mind and thus achieve a solid and long-lasting improvement in mental health.

Serotonin and Consciousness-A Reappraisal

The serotonergic system of the brain is a major modulator of behaviour. Here we describe a re-appraisal of its function for consciousness based on anatomical, functional and pharmacological data. For a better understanding, the current model of consciousness is expanded. Two parallel streams of conscious flow are distinguished. A flow of conscious content and an affective consciousness flow. While conscious content flow has its functional equivalent in the activity of higher cortico-cortical and cortico-thalamic networks, affective conscious flow originates in segregated deeper brain structures for single emotions. It is hypothesized that single emotional networks converge on serotonergic and other modulatory transmitter neurons in the brainstem where a bound percept of an affective conscious flow is formed. This is then dispersed to cortical and thalamic networks, where it is time locked with conscious content flow at the level of these networks. Serotonin acts in concert with other modulatory systems of the brain stem with some possible specialization on single emotions. Together, these systems signal a bound percept of affective conscious flow. Dysfunctions in the serotonergic system may not only give rise to behavioural and somatic symptoms, but also essentially affect the coupling of conscious affective flow with conscious content flow, leading to the affect-stained subjective side of mental disorders like anxiety, depression, or schizophrenia. The present model is an attempt to integrate the growing insights into serotonergic system function. However, it is acknowledged, that several key claims are still at a heuristic level that need further empirical support.

An electroencephalography connectome predictive model of major depressive disorder severity

Emerging evidence showed that major depressive disorder (MDD) is associated with disruptions of brain structural and functional networks, rather than impairment of isolated brain region. Thus, connectome-based models capable of predicting the depression severity at the individual level can be clinically useful. Here, we applied a machine-learning approach to predict the severity of depression using resting-state networks derived from source-reconstructed Electroencephalography (EEG) signals. Using regression models and three independent EEG datasets (N = 328), we tested whether resting state functional connectivity could predict individual depression score. On the first dataset, results showed that individuals scores could be reasonably predicted (r = 0.6, p = 4 × 10-18) using intrinsic functional connectivity in the EEG alpha band (8-13 Hz). In particular, the brain regions which contributed the most to the predictive network belong to the default mode network. We further tested the predictive potential of the established model by conducting two external validations on (N1 = 53, N2 = 154). Results showed statistically significant correlations between the predicted and the measured depression scale scores (r1 = 0.52, r2 = 0.44, p < 0.001). These findings lay the foundation for developing a generalizable and scientifically interpretable EEG network-based markers that can ultimately support clinicians in a biologically-based characterization of MDD.

Web-Based Single Session Intervention for Perceived Control Over Anxiety During COVID-19: Randomized Controlled Trial

BACKGROUND

Anxiety is rising across the United States during the COVID-19 pandemic, and social distancing mandates preclude in-person mental health care. Greater perceived control over anxiety has predicted decreased anxiety pathology, including adaptive responses to uncontrollable stressors. Evidence suggests that no-therapist, single-session interventions can strengthen perceived control over emotions like anxiety; similar programs, if designed for the COVID-19 context, could hold substantial public health value.

OBJECTIVE

Our registered report evaluated a no-therapist, single-session, online intervention targeting perceived control over anxiety in the COVID-19 context against a placebo intervention encouraging handwashing. We tested whether the intervention could (1) decrease generalized anxiety and increase perceived control over anxiety and (2) achieve this without decreasing social-distancing intentions.

METHODS

We tested these questions using a between-subjects design in a weighted-probability sample of US adults recruited via a closed online platform (ie, Prolific). All outcomes were indexed via online self-report questionnaires.

RESULTS

Of 522 randomized individuals, 500 (95.8%) completed the baseline survey and intervention. Intent-to-treat analyses using all randomized participants (N=522) found no support for therapeutic or iatrogenic effects; effects on generalized anxiety were d=-0.06 (95% CI -0.27 to 0.15; P=.48), effects on perceived control were d=0.04 (95% CI -0.08 to 0.16; P=.48), and effects on social-distancing intentions were d=-0.02 (95% CI -0.23 to 0.19; P=.83).

CONCLUSIONS

Strengths of this study included a large, nationally representative sample and adherence to open science practices. Implications for scalable interventions, including the challenge of targeting perceived control over anxiety, are discussed.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04459455; https://clinicaltrials.gov/show/NCT04459455.

Research Domain Criteria (RDoC): Progress and Potential

The National Institute of Mental Health (NIMH) addressed in its 2008 Strategic Plan an emerging concern that the current diagnostic system was hampering translational research, as accumulating data suggested that disorder categories constituted heterogeneous syndromes rather than specific diseases. However, established practices in peer review placed high priority on extant disorders in evaluating grant applications for mental illness. To provide guidelines for alternative study designs, NIMH included a goal to develop new ways of studying psychopathology based on dimensions of measurable behavior and related neurobiological measures. The Research Domain Criteria (RDoC) project is the result, intended to build a literature that informs new conceptions of mental illness and future revisions to diagnostic manuals. The framework calls for the study of empirically-derived fundamental dimensions as characterized by related behavioral/psychological and neurobiological data (e.g., reward valuation, working memory). RDoC also emphasizes full-range dimensional approaches (from typical to increasingly abnormal), neurodevelopment and environmental effects, and research designs that integrate data across behavioral, biological, and self-report measures. This commentary provides an overview of the project's first decade and its potential future directions. RDoC remains grounded in experimental psychopathology perspectives, and its progress is strongly linked to psychological measurement and integrative approaches to brain-behavior relationships.

Translational Informatics for Natural Products as Antidepressant Agents

Depression, a neurological disorder, is a universally common and debilitating illness where social and economic issues could also become one of its etiologic factors. From a global perspective, it is the fourth leading cause of long-term disability in human beings. For centuries, natural products have proven their true potential to combat various diseases and disorders, including depression and its associated ailments. Translational informatics applies informatics models at molecular, imaging, individual, and population levels to promote the translation of basic research to clinical applications. The present review summarizes natural-antidepressant-based translational informatics studies and addresses challenges and opportunities for future research in the field.

Exploring the role of immune pathways in the risk and development of depression in adolescence: Research protocol of the IDEA-FLAME study

Extensive research suggests a role for the innate immune system in the pathogenesis of depression, but most of the studies are conducted in adult populations, in high-income countries and mainly focus on the study of inflammatory proteins alone, which provides only a limited understanding of the immune pathways involved in the development of depression. The IDEA-FLAME study aims to identify immune phenotypes underlying increased risk of developing depression in adolescence in a middle-income country. To this end, we will perform deep-immunophenotyping of peripheral blood mononuclear cells and RNA genome-wide gene expression analyses in a longitudinal cohort of Brazilian adolescents stratified for depression risk. The project will involve the 3-year follow-up of an already recruited cohort of 150 Brazilian adolescents selected for risk/presence of depression on the basis of a composite risk score we developed using sociodemographic characteristics (50 adolescents with low-risk and 50 with high-risk of developing depression, and 50 adolescents with a current major depressive disorder). We will 1) test whether the risk group classification at baseline is associated with differences in immune cell frequency, phenotype and functional status, 2) test whether baseline immune markers (cytokines and immune cell markers) are associated with severity of depression at 3-year follow-up, and 3) identify changes in gene expression of immune pathways over the 3-year follow-up in adolescents with increased risk and presence of depression. Because of the exploratory nature of the study, the findings would need to be replicated in a separate and larger sample. Ultimately, this research will contribute to elucidating key immune therapeutic targets and inform the development of interventions to prevent onset of depression among adolescents.

The Normative Modeling Framework for Computational Psychiatry.. [PMID: 35650452 PMCID: PMC7613648 DOI: 10.1101/2021.08.08.455583]

Normative modeling is an emerging and innovative framework for mapping individual differences at the level of a single subject or observation in relation to a reference model. It involves charting centiles of variation across a population in terms of mappings between biology and behavior which can then be used to make statistical inferences at the level of the individual. The fields of computational psychiatry and clinical neuroscience have been slow to transition away from patient versus “healthy” control analytic approaches, likely due to a lack of tools designed to properly model biological heterogeneity of mental disorders. Normative modeling provides a solution to address this issue and moves analysis away from case-control comparisons that rely on potentially noisy clinical labels. In this article, we define a standardized protocol to guide users through, from start to finish, normative modeling analysis using the Predictive Clinical Neuroscience toolkit (PCNtoolkit). We describe the input data selection process, provide intuition behind the various modeling choices, and conclude by demonstrating several examples of down-stream analyses the normative model results may facilitate, such as stratification of high-risk individuals, subtyping, and behavioral predictive modeling. The protocol takes approximately 1-3 hours to complete.

Markov chain Monte Carlo methods for hierarchical clustering of dynamic causal models

In this article, we address technical difficulties that arise when applying Markov chain Monte Carlo (MCMC) to hierarchical models designed to perform clustering in the space of latent parameters of subject‐wise generative models. Specifically, we focus on the case where the subject‐wise generative model is a dynamic causal model (DCM) for functional magnetic resonance imaging (fMRI) and clusters are defined in terms of effective brain connectivity. While an attractive approach for detecting mechanistically interpretable subgroups in heterogeneous populations, inverting such a hierarchical model represents a particularly challenging case, since DCM is often characterized by high posterior correlations between its parameters. In this context, standard MCMC schemes exhibit poor performance and extremely slow convergence. In this article, we investigate the properties of hierarchical clustering which lead to the observed failure of standard MCMC schemes and propose a solution designed to improve convergence but preserve computational complexity. Specifically, we introduce a class of proposal distributions which aims to capture the interdependencies between the parameters of the clustering and subject‐wise generative models and helps to reduce random walk behaviour of the MCMC scheme. Critically, these proposal distributions only introduce a single hyperparameter that needs to be tuned to achieve good performance. For validation, we apply our proposed solution to synthetic and real‐world datasets and also compare it, in terms of computational complexity and performance, to Hamiltonian Monte Carlo (HMC), a state‐of‐the‐art Monte Carlo technique. Our results indicate that, for the specific application domain considered here, our proposed solution shows good convergence performance and superior runtime compared to HMC.

Pharmacotherapy of schizophrenia: Mechanisms of antipsychotic accumulation, therapeutic action and failure

Schizophrenia is a multi-dimensional disorder with a complex and mostly unknown etiology, leading to a severe decline in life quality. Antipsychotic drugs (APDs) remain beneficial interventions in the treatment of the disorder, but vary significantly in binding profile, clinical effects and adverse reactions. The present review summarizes the main principles of APD mechanisms of action with a particular focus on recent findings in APD accumulation and its role in the therapeutic efficacy and treatment failure. High and low doses of APDs were shown to be effective in different dimensions of antipsychotic-like behaviour in rodent models. Efficacy of the APDs correlates with high dopamine D2 receptor occupancy, which occurs quickly after drug administration. However, onset and peak of action are delayed up to several days or weeks. APD accumulation via acidic trapping in synaptic vesicles is considered to underlie the time course of APD action. Use-dependent exocytosis, co-release with dopamine and serotonin and inhibition of ion channels impact on the neuronal transmission and determine effects of APDs. Disruption in accumulating properties leads to diminished APD effects. In addition, long-term APD administration at therapeutic doses leads to treatment failure both in animal models and in humans. APD failure was associated with treatment induced neuroadaptations, including a decline in extracellular dopamine levels, dopamine transporter upregulation, and altered neuronal firing. However, enhanced synaptic vesicle release has also been reported. APD loss of efficacy may be reversed through inhibition of the dopamine transporter or switching the administration regimen from continuous to intermittent. Thus, manipulating the accumulation properties of APDs, changes in the administration regimen and doses, or co-administration with dopamine transporter inhibitors may be considered to yield benefits in the development of new effective strategies in the treatment of schizophrenia.

The Need of Personalized Medicine in Coping with Stress during Infertility Treatment

The term personalized medicine was created for oncological patients, but due to its positive clinical results it is now used in many other fields of medicine, including reproductive medicine. The aim of the study was to determine the level of stress and strategies of coping with stress in patients treated for infertility. The study—using a questionnaire developed by the authors, the Perceived Stress Scale-10 (PSS-10), and the Coping Orientation to Problems Experienced Inventory (Mini-COPE)—was conducted among 456 people from infertile couples. Conclusions: More than half of the studied patients demonstrated a high level of stress. The choice of coping strategies was related to the respondents’ gender and level of stress as well as their experience with assisted reproductive technology.

Neutral sphingomyelinase mediates the co-morbidity trias of alcohol abuse, major depression and bone defects

Mental disorders are highly comorbid and occur together with physical diseases, which are often considered to arise from separate pathogenic pathways. We observed in alcohol-dependent patients increased serum activity of neutral sphingomyelinase. A genetic association analysis in 456,693 volunteers found associations of haplotypes of SMPD3 coding for NSM-2 (NSM) with alcohol consumption, but also with affective state, and bone mineralisation. Functional analysis in mice showed that NSM controls alcohol consumption, affective behaviour, and their interaction by regulating hippocampal volume, cortical connectivity, and monoaminergic responses. Furthermore, NSM controlled bone-brain communication by enhancing osteocalcin signalling, which can independently supress alcohol consumption and reduce depressive behaviour. Altogether, we identified a single gene source for multiple pathways originating in the brain and bone, which interlink disorders of a mental-physical co-morbidity trias of alcohol abuse-depression/anxiety-bone disorder. Targeting NSM and osteocalcin signalling may, thus, provide a new systems approach in the treatment of a mental-physical co-morbidity trias.

Prescribed medication use among elementary teachers: Prevalence and associated factors

The aims of the study were to assess the prevalence and analyze the associated factors of medication use among teachers. A cross-sectional study was carried out, involving 530 teachers from Londrina city, Paraná, Brazil. The dependent variable was prescribed medication use and the independent variables were sociodemographic, work-related, lifestyle, health disorders, and chronic diseases, all assessed through questionnaires. Prevalence of medication use was 59.1%. Chronic disease was associated with all medications analyzed. Variables positively associated with medication use according to health disorder type were: Cardiometabolic (Length of employment, overweight, not current tobacco use, and TV viewing); Psychological (Length of employment, common mental disorders, current tobacco use, and disability); Orthopedic (Length of employment, health insurance, overweight, musculoskeletal pain, low job support, and disability); Respiratory (TV viewing and problems related to dust or chalk powder); and Gastrointestinal (common mental disorders and physical activity [negative association]). Support for access, the appropriate use of medicines, and a reduction in medication use should consider work-related, lifestyle, and health disorders, as well as chronic diseases.

An evidence map of actigraphy studies exploring longitudinal associations between rest-activity rhythms and course and outcome of bipolar disorders

BACKGROUND

Evidence mapping is a structured approach used to synthesize the state-of-the-art in an emerging field of research when systematic reviews or meta-analyses are deemed inappropriate. We employed this strategy to summarise knowledge regarding longitudinal ecological monitoring of rest-activity rhythms (RAR) and disease modifiers, course of illness, treatment response or outcome in bipolar disorders (BD).

STRUCTURE

We had two key aims: (1) to determine the number and type of actigraphy studies of in BD that explored data regarding: outcome over time (e.g. relapse/recurrence according to polarity, or recovery/remission), treatment response or illness trajectories and (2) to examine the range of actigraphy metrics that can be used to estimate disruptions of RAR and describe which individual circadian rhythm or sleep-wake cycle parameters are most consistently associated with outcome over time in BD. The mapping process incorporated four steps: clarifying the project focus, describing boundaries and 'coordinates' for mapping, searching the literature and producing a brief synopsis with summary charts of the key outputs. Twenty-seven independent studies (reported in 29 publications) were eligible for inclusion in the map. Most were small-scale, with the median sample size being 15 per study and median duration of actigraphy being about 7 days (range 1-210). Interestingly, 17 studies comprised wholly or partly of inpatients (63%). The available evidence indicated that a discrete number of RAR metrics are more consistently associated with transition between different phases of BD and/or may be predictive of longitudinal course of illness or treatment response. The metrics that show the most frequent associations represent markers of the amount, timing, or variability of RAR rather than the sleep quality metrics that are frequently targeted in contemporary studies of BD.

CONCLUSIONS

Despite 50 years of research, use of actigraphy to assess RAR in longitudinal studies and examination of these metrics and treatment response, course and outcome of BD is under-investigated. This is in marked contrast to the extensive literature on case-control or cross-sectional studies of actigraphy, especially typical sleep analysis metrics in BD. However, given the encouraging findings on putative RAR markers, we recommend increased study of putative circadian phenotypes of BD.

Pupil Dilation during Reward Anticipation Is Correlated to Depressive Symptom Load in Patients with Major Depressive Disorder

Depression is a debilitating disorder with high prevalence and socioeconomic cost, but the brain-physiological processes that are altered during depressive states are not well understood. Here, we build on recent findings in macaques that indicate a direct causal relationship between pupil dilation and anterior cingulate cortex mediated arousal during anticipation of reward. We translated these findings to human subjects with concomitant pupillometry/fMRI in a sample of unmedicated participants diagnosed with major depression and healthy controls. We could show that the upregulation and maintenance of arousal in anticipation of reward was disrupted in patients in a symptom-load dependent manner. We could further show that the failure to maintain reward anticipatory arousal showed state-marker properties, as it tracked the load and impact of depressive symptoms independent of prior diagnosis status. Further, group differences of anticipatory arousal and continuous correlations with symptom load were not traceable only at the level of pupillometric responses, but were mirrored also at the neural level within salience network hubs. The upregulation and maintenance of arousal during reward anticipation is a novel translational and well-traceable process that could prove a promising gateway to a physiologically informed patient stratification and targeted interventions.

Schema therapy versus cognitive behavioral therapy versus individual supportive therapy for depression in an inpatient and day clinic setting: study protocol of the OPTIMA-RCT

BACKGROUND

Major depressive disorder represents (MDD) a major cause of disability and disease burden. Beside antidepressant medication, psychotherapy is a key approach of treatment. Schema therapy has been shown to be effective in the treatment of psychiatric disorders, especially personality disorders, in a variety of settings and patient groups. Nevertheless, there is no evidence on its effectiveness for MDD in an inpatient nor day clinic setting and little is known about the factors that drive treatment response in such a target group.

METHODS

In the current protocol, we outline OPTIMA (OPtimized Treatment Identification at the MAx Planck Institute): a single-center randomized controlled trial of schema therapy as a treatment approach for MDD in an inpatient and day clinic setting. Over the course of 7 weeks, we compare schema therapy with cognitive behavioral therapy and individual supportive therapy, conducted in individual and group sessions and with no restrictions regarding concurrent antidepressant medication, thus approximating real-life treatment conditions. N = 300 depressed patients are included. All study therapists undergo a specific training and supervision and therapy adherence is assessed. Primary outcome is depressive symptom severity as self-assessment (Beck Depression Inventory-II) and secondary outcomes are clinical ratings of MDD (Montgomery-Asberg Depression Rating Scale), recovery rates after 7 weeks according to the Munich-Composite International Diagnostic Interview, general psychopathology (Brief Symptom Inventory), global functioning (World Health Organization Disability Assessment Schedule), and clinical parameters such as dropout rates. Further parameters on a behavioral, cognitive, psychophysiological, and biological level are measured before, during and after treatment and in 2 follow-up assessments after 6 and 24 months after end of treatment.

DISCUSSION

To our knowledge, the OPTIMA-Trial is the first to investigate the effectiveness of schema therapy as a treatment approach of MDD, to investigate mechanisms of change, and explore predictors of treatment response in an inpatient and day clinic setting by using such a wide range of parameters. Insights from OPTIMA will allow more integrative approaches of psychotherapy of MDD. Especially, the identification of intervention-specific markers of treatment response can improve evidence-based clinical decision for individualizing treatment.

TRIAL REGISTRATION

Identifier on clinicaltrials.gov : NCT03287362 ; September, 12, 2017.

Loss of the psychiatric risk factor SLC6A15 is associated with increased metabolic functions in primary hippocampal neurons

Major depressive disorder (MDD) is one of the most severe global health problems with millions of people affected, however, the mechanisms underlying this disorder is still poorly understood. Genome-wide association studies have highlighted a link between the neutral amino acid transporter SLC6A15 and MDD. Additionally, a number of preclinical studies support the function of this transporter in modulating levels of brain neurotransmitters, stress system regulation and behavioural phenotypes related to MDD. However, the molecular and functional mechanisms involved in this interaction are still unresolved. Therefore, to investigate the effects of the SLC6A15 transporter, we used hippocampal tissue from Slc6a15-KO and wild-type mice, together with several in-vitro assays in primary hippocampal neurons. Utilizing a proteomics approach we identified differentially regulated proteins that formed a regulatory network and pathway analysis indicated significantly affected cellular domains, including metabolic, mitochondrial and structural functions. Furthermore, we observed reduced release probability at glutamatergic synapses, increased mitochondrial function, higher GSH/GSSG redox ratio and an improved neurite outgrowth in primary neurons lacking SLC6A15. In summary, we hypothesize that by controlling the intracellular concentrations of neutral amino acids, SLC6A15 affects mitochondrial activity, which could lead to alterations in neuronal structure and activity. These data provide further indication that a pharmacological or genetic reduction of SLC6A15 activity may indeed be a promising approach for antidepressant therapy.

Severity of self-reported depressive symptoms in a healthy sample is modulated by trait Harm Avoidance, not by 5-HTTLPR polymorphism

BACKGROUND

The length of the serotonin transporter polymorphic region (5-HTTLPR) has been suggested to be associated with risk for developing depression, though with inconsistent evidence. Likewise, the personality trait Harm Avoidance (HA) has been linked to vulnerability for developing depression. However, no study has investigated whether there is an interaction effect between 5-HTTLPR and trait HA on depressive symptoms in healthy individuals.

METHODS

A total of 319 healthy individuals were included in this cross-sectional study. All participants were genotyped for the 5-HTTLPR polymorphism and completed self-reported measures of personality trait HA with the Temperament and Character Inventory (TCI), and of depression with the Major Depression Inventory (MDI). Linear regression analyses were used to test interaction effects between 5-HTTLPR and HA on MDI. Post hoc analyses were further performed to investigate main effects of HA and possible interaction effects between 5-HTTLPR and HA sub-scales on MDI.

RESULTS

No significant interaction effect between 5-HTTLPR and HA on MDI was found. A significant main effect of trait HA on MDI was found, indicating that personality trait HA is a viable vulnerability factor for even sub-clinical depressive symptoms.

CONCLUSION

This study finds a strong significant relationship between HA and MDI. Moreover, the present study supports the line of research indicating that candidate gene-by-interactions does not increase vulnerability for developing depression even at a sub-clinical level.

Towards a brain-based predictome of mental illness

Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.

Brain-Predicted Age Associates With Psychopathology Dimensions in Youths

BACKGROUND

This study aimed to investigate whether dimensional constructs of psychopathology relate to variation in patterns of brain development and to determine whether these constructs share common neurodevelopmental profiles.

METHODS

Psychiatric symptom ratings from 9312 youths (8-21 years old) from the Philadelphia Neurodevelopmental Cohort were parsed into 7 independent dimensions of clinical psychopathology representing conduct, anxiety, obsessive-compulsive, attention, depression, bipolar, and psychosis symptoms. Using a subset of this cohort with structural magnetic resonance imaging (n = 1313), a normative model of brain morphology was established and the model was then applied to predict the age of youths with clinical symptoms. We investigated whether the deviation of brain-predicted age from true chronological age, called the brain age gap, explained individual variation in each psychopathology dimension.

RESULTS

Individual variation in the brain age gap significantly associated with clinical dimensions representing psychosis (t = 3.16, p = .0016), obsessive-compulsive symptoms (t = 2.5, p = .01), and general psychopathology (t = 4.08, p < .0001). Greater symptom severity along these dimensions was associated with brain morphology that appeared older than expected for typically developing youths of the same age. Psychopathology dimensions clustered into 2 modules based on shared brain loci where putative accelerated neurodevelopment was most prominent. Patterns of morphological development were accelerated in frontal cortices for depression, psychosis, and conduct symptoms (module 1), whereas acceleration was most evident in subcortex and insula for the remaining dimensions (module 2).

CONCLUSIONS

Our findings suggest that increased brain age, particularly in frontal cortex and subcortical nuclei, underpins clinical psychosis and obsessive-compulsive symptoms in youths. Psychopathology dimensions share common neural substrates, despite representing clinically independent symptom profiles.

Mental Health and Well-Being of University Students: A Bibliometric Mapping of the Literature

The purpose of this study is to map the literature on mental health and well-being of university students using metadata extracted from 5,561 journal articles indexed in the Web of Science database for the period 1975-2020. More specifically, this study uses bibliometric procedures to describe and visually represent the available literature on mental health and well-being in university students in terms of the growth trajectory, productivity, social structure, intellectual structure, and conceptual structure of the field over 45 years. Key findings of the study are that research on mental health and well-being in university students: (a) has experienced a steady growth over the last decades, especially since 2010; (b) is disseminated in a wide range of journals, mainly in the fields of psychology, psychiatry, and education research; (c) is published by scholars with diverse geographical background, although more than half of the publications are produced in the United States; (d) lies on a fragmented research community composed by multiple research groups with little interactions between them; (e) is relatively interdisciplinary and emerges from the convergence of research conducted in the behavioral and biomedical sciences; (f) tends to emphasize pathogenic approaches to mental health (i.e., mental illness); and (g) has mainly addressed seven research topics over the last 45 years: positive mental health, mental disorders, substance abuse, counseling, stigma, stress, and mental health measurement. The findings are discussed, and the implications for the future development of the field are highlighted.

Association of a CAMK2A genetic variant with logical memory performance and hippocampal volume in the elderly

Calcium/Calmodulin-dependent kinase alpha (αCaMKII) has been shown to play an essential role in synaptic plasticity and in learning and memory in animal models. However, there is little evidence for an involvement in specific memories in humans. Here we tested the potential involvement of the αCaMKII coding gene CAMK2A in verbal logical memory in two Caucasian populations from Germany, in a sample of 209 elderly people with cognitive impairments and a sample of 142 healthy adults. The association of single nucleotide polymorphisms (SNPs) located within the genomic region of CAMK2A with verbal logical memory learning and retrieval from the Wechsler Memory Scale was measured and hippocampal volume was assessed by structural MRI. In the elderly people, we found the minor allele of CAMK2A intronic SNP rs919741 to predict a higher hippocampal volume and better logical memory retrieval. This association was not found in healthy adults. The present study may provide evidence for an association of a genetic variant of the CAMK2A gene specifically with retrieval of logical memory in elderly humans. This effect is possibly mediated by a higher hippocampal volume.

The relevance of transdiagnostic shared networks to the severity of symptoms and cognitive deficits in schizophrenia: a multimodal brain imaging fusion study

Schizophrenia (SZ) is frequently concurrent with substance use, depressive symptoms, social communication and attention deficits. However, the relationship between common brain networks (e.g., SZ vs. substance use, SZ vs. depression, SZ vs. developmental disorders) with SZ on specific symptoms and cognition is unclear. Symptom scores were used as a reference to guide fMRI-sMRI fusion for SZ (n = 94), substance use with drinking (n = 313), smoking (n = 104), major depressive disorder (MDD, n = 260), developmental disorders with autism spectrum disorder (ASD, n = 421) and attention-deficit/hyperactivity disorder (ADHD, n = 244) respectively. Common brain regions were determined by overlapping the symptom-related components between SZ and these other groups. Correlation between the identified common brain regions and cognition/symptoms in an independent SZ dataset (n = 144) was also performed. Results show that (1): substance use was related with cognitive deficits in schizophrenia through gray matter volume (GMV) in anterior cingulate cortex and thalamus; (2) depression was linked to PANSS negative dimensions and reasoning in SZ through a network involving caudate-thalamus-middle/inferior temporal gyrus in GMV; (3) developmental disorders pattern was correlated with poor attention, speed of processing and reasoning in SZ through inferior temporal gyrus in GMV. This study reveals symptom driven transdiagnostic shared networks between SZ and other mental disorders via multi-group data mining, indicating that some potential common underlying brain networks associated with schizophrenia differently with respect to symptoms and cognition. These results have heuristic value and advocate specific approaches to refine available treatment strategies for comorbid conditions in schizophrenia.

Pharmacological sex hormone manipulation as a risk model for depression

Sex hormone transition may trigger severe depressive episodes in some women. In order to map mechanisms related to such phenomena we developed a pharmacological preclinical human model using sex hormone manipulation with gonadotropin releasing hormone agonist (GnRHa) in a placebo‐controlled design. Here the findings from this model is synthesized and discussed in the context of related literature on hormonal contributions to reproductive mental health disorders. The GnRha model work points to an estradiol‐dependent depressive response in healthy women undergoing short‐term sex hormone manipulation with GnRHa, which is linked to serotonin transporter changes (a key regulator of synaptic serotonin), a disengagement of hippocampus, and overengagement of brain networks recruited when processing emotional salient information. Further, the GnRHa model suggest that key brain regions in the reward circuit are less engaged in positive stimuli when undergoing sex hormone manipulation, which may underlie anhedonia. Also, the work supports that enhanced sensitivity to estrogen signaling at the level of gene expression may drive increased risk for depressive symptoms when exposed to sex steroid hormone fluctuations. In conclusion, the GnRHa model work highlights the brain signatures of rapid and profound changes in sex steroid hormone milieu, which reflect plausible mechanisms by which risk for mood disorders works. This model points to the role of estrogen dynamics and sensitivity, and offers a rationale for personalized prevention in hormonal transition phases, for example pregnancy to postpartum transition, perimenopause, and hormone treatments, which now can move into clinical translation and ideally pave the way for protecting mental and cognitive health.

The biological classification of mental disorders (BeCOME) study: a protocol for an observational deep-phenotyping study for the identification of biological subtypes

BACKGROUND

A major research finding in the field of Biological Psychiatry is that symptom-based categories of mental disorders map poorly onto dysfunctions in brain circuits or neurobiological pathways. Many of the identified (neuro) biological dysfunctions are "transdiagnostic", meaning that they do not reflect diagnostic boundaries but are shared by different ICD/DSM diagnoses. The compromised biological validity of the current classification system for mental disorders impedes rather than supports the development of treatments that not only target symptoms but also the underlying pathophysiological mechanisms. The Biological Classification of Mental Disorders (BeCOME) study aims to identify biology-based classes of mental disorders that improve the translation of novel biomedical findings into tailored clinical applications.

METHODS

BeCOME intends to include at least 1000 individuals with a broad spectrum of affective, anxiety and stress-related mental disorders as well as 500 individuals unaffected by mental disorders. After a screening visit, all participants undergo in-depth phenotyping procedures and omics assessments on two consecutive days. Several validated paradigms (e.g., fear conditioning, reward anticipation, imaging stress test, social reward learning task) are applied to stimulate a response in a basic system of human functioning (e.g., acute threat response, reward processing, stress response or social reward learning) that plays a key role in the development of affective, anxiety and stress-related mental disorders. The response to this stimulation is then read out across multiple levels. Assessments comprise genetic, molecular, cellular, physiological, neuroimaging, neurocognitive, psychophysiological and psychometric measurements. The multilevel information collected in BeCOME will be used to identify data-driven biologically-informed categories of mental disorders using cluster analytical techniques.

DISCUSSION

The novelty of BeCOME lies in the dynamic in-depth phenotyping and omics characterization of individuals with mental disorders from the depression and anxiety spectrum of varying severity. We believe that such biology-based subclasses of mental disorders will serve as better treatment targets than purely symptom-based disease entities, and help in tailoring the right treatment to the individual patient suffering from a mental disorder. BeCOME has the potential to contribute to a novel taxonomy of mental disorders that integrates the underlying pathomechanisms into diagnoses.

TRIAL REGISTRATION

Retrospectively registered on June 12, 2019 on ClinicalTrials.gov (TRN: NCT03984084).

Modafinil enhances cognitive, but not emotional conflict processing via enhanced inferior frontal gyrus activation and its communication with the dorsomedial prefrontal cortex

Cognitive control regulates cognitive and emotional systems to facilitate goal-directed behavior in the context of task-irrelevant distractors. Cognitive control deficits contribute to residual functional impairments across psychiatric disorders and represent a promising novel treatment target. Translational evidence suggests that modafinil may enhance performance in executive functions; however, differential effects on regulatory control in cognitive and emotional domains have not been examined. The present pre-registered randomized-controlled pharmacological fMRI trial examined differential effects of modafinil (single-dose, 200 mg) on cognitive and emotional conflict processing. To further separate objective cognitive enhancing effects from subjective performance perception, a metacognitive paradigm was employed. Results indicated that modafinil specifically enhanced cognitive conflict performance and concomitantly increased activation in the inferior frontal gyrus and its functional communication with the dorsomedial prefrontal cortex. Exploratory analysis further revealed modafinil-enhanced basolateral amygdala reactivity to cognitive conflict, with stronger reactivity being associated with higher cognitive conflict performance. Whereas modafinil enhanced cognitive performance in the metacognitive paradigm, confidence indices remained unaffected. Overall, the present results suggest that modafinil has the potential to enhance cognitive conflict processing while leaving emotional conflict processing unaffected. On the neural level modafinil enhanced the recruitment of a network engaged in general conflict and regulatory control processes, whereas effects on the amygdala may reflect improved arousal-mediated attention processes for conflicting information. The pattern of cognitive enhancing effects in the absence of effects on affective processing suggests a promising potential to enhance cognitive control in clinical populations.

Diet and Neurocognition in Mood Disorders - An Overview of the Overlooked

Bipolar disorder and major depression are associated with significant disability, morbidity, and reduced life expectancy. People with mood disorders have shown higher ratios of unhealthy lifestyle choices, including poor diet quality and suboptimal nutrition. Diet and nutrition impact on brain /mental health, but cognitive outcomes have been less researched in psychiatric disorders. Neurocognitive dysfunction is a major driver of social dysfunction and a therapeutic target in mood disorders, although effective cognitive-enhancers are currently lacking. This narrative review aimed to assess the potential cognitive benefits of dietary and nutritional interventions in subjects diagnosed with mood disorders. Eight clinical trials with nutrients were identified, whereas none involved dietary interventions. Efficacy to improve select cognitive deficits has been reported, but results are either preliminary or inconsistent. Methodological recommendations for future cognition trials in the field are advanced. Current evidence and future views are discussed from the perspectives of precision medicine, clinical staging, nutritional psychiatry, and the brain-gut-microbiota axis.

Identifying biological markers for improved precision medicine in psychiatry

Mental disorders represent an increasing personal and financial burden and yet treatment development has stagnated in recent decades. Current disease classifications do not reflect psychobiological mechanisms of psychopathology, nor the complex interplay of genetic and environmental factors, likely contributing to this stagnation. Ten years ago, the longitudinal IMAGEN study was designed to comprehensively incorporate neuroimaging, genetics, and environmental factors to investigate the neural basis of reinforcement-related behavior in normal adolescent development and psychopathology. In this article, we describe how insights into the psychobiological mechanisms of clinically relevant symptoms obtained by innovative integrative methodologies applied in IMAGEN have informed our current and future research aims. These aims include the identification of symptom groups that are based on shared psychobiological mechanisms and the development of markers that predict disease course and treatment response in clinical groups. These improvements in precision medicine will be achieved, in part, by employing novel methodological tools that refine the biological systems we target. We will also implement our approach in low- and medium-income countries to understand how distinct environmental, socioeconomic, and cultural conditions influence the development of psychopathology. Together, IMAGEN and related initiatives strive to reduce the burden of mental disorders by developing precision medicine approaches globally.

Animal models of liability to post-traumatic stress disorder: going beyond fear memory

In this review, we advocate a dimensional approach on the basis of candidate endophenotypes to the development of animal models of post-traumatic stress disorder (PTSD) capable of including genetic liability factors, variations in symptoms profile and underlying neurobiological mechanisms, and specific comorbidities. Results from the clinical literature pointed to two candidate endophenotypes of PTSD: low sensory gating and high waiting impulsivity. Findings of comparative studies in mice of two inbred strains characterized by different expressions of the two candidate endophenotypes showed different strain-specific neural and behavioral effects of stress experiences. Thus, mice of the standard C57BL/6J strain show stress-induced helplessness, stress-learned helplessness, and stress-extinction-resistant conditioned freezing. Instead, mice of the genetically unrelated DBA/2J strain, expressing both candidate endophenotypes, show stress-induced extinction-resistant avoidance and neural and behavioral phenotypes promoted by prolonged exposure to addictive drugs. These strain differences are in line with evidence of associations between genetic variants and specific stress-promoted pathological profiles in PTSD, support a role of genotype in determining different PTSD comorbidities, and offer the means to investigate specific pathogenic processes.

Individual differences v. the average patient: mapping the heterogeneity in ADHD using normative models

BACKGROUND

The present paper presents a fundamentally novel approach to model individual differences of persons with the same biologically heterogeneous mental disorder. Unlike prevalent case-control analyses, that assume a clear distinction between patient and control groups and thereby introducing the concept of an 'average patient', we describe each patient's biology individually, gaining insights into the different facets that characterize persistent attention-deficit/hyperactivity disorder (ADHD).

METHODS

Using a normative modeling approach, we mapped inter-individual differences in reference to normative structural brain changes across the lifespan to examine the degree to which case-control analyses disguise differences between individuals.

RESULTS

At the level of the individual, deviations from the normative model were frequent in persistent ADHD. However, the overlap of more than 2% between participants with ADHD was only observed in few brain loci. On average, participants with ADHD showed significantly reduced gray matter in the cerebellum and hippocampus compared to healthy individuals. While the case-control differences were in line with the literature on ADHD, individuals with ADHD only marginally reflected these group differences.

CONCLUSIONS

Case-control comparisons, disguise inter-individual differences in brain biology in individuals with persistent ADHD. The present results show that the 'average ADHD patient' has limited informative value, providing the first evidence for the necessity to explore different biological facets of ADHD at the level of the individual and practical means to achieve this end.

Deep neural networks in psychiatry

Machine and deep learning methods, today's core of artificial intelligence, have been applied with increasing success and impact in many commercial and research settings. They are powerful tools for large scale data analysis, prediction and classification, especially in very data-rich environments ("big data"), and have started to find their way into medical applications. Here we will first give an overview of machine learning methods, with a focus on deep and recurrent neural networks, their relation to statistics, and the core principles behind them. We will then discuss and review directions along which (deep) neural networks can be, or already have been, applied in the context of psychiatry, and will try to delineate their future potential in this area. We will also comment on an emerging area that so far has been much less well explored: by embedding semantically interpretable computational models of brain dynamics or behavior into a statistical machine learning context, insights into dysfunction beyond mere prediction and classification may be gained. Especially this marriage of computational models with statistical inference may offer insights into neural and behavioral mechanisms that could open completely novel avenues for psychiatric treatment.

Prospective cohort study of early biosignatures of response to lithium in bipolar-I-disorders: overview of the H2020-funded R-LiNK initiative

Background

Lithium is recommended as a first line treatment for bipolar disorders. However, only 30% of patients show an optimal outcome and variability in lithium response and tolerability is poorly understood. It remains difficult for clinicians to reliably predict which patients will benefit without recourse to a lengthy treatment trial. Greater precision in the early identification of individuals who are likely to respond to lithium is a significant unmet clinical need.

Structure

The H2020-funded Response to Lithium Network (R-LiNK; http://www.r-link.eu.com/) will undertake a prospective cohort study of over 300 individuals with bipolar-I-disorder who have agreed to commence a trial of lithium treatment following a recommendation by their treating clinician. The study aims to examine the early prediction of lithium response, non-response and tolerability by combining systematic clinical syndrome subtyping with examination of multi-modal biomarkers (or biosignatures), including omics, neuroimaging, and actigraphy, etc. Individuals will be followed up for 24 months and an independent panel will assess and classify each participants’ response to lithium according to predefined criteria that consider evidence of relapse, recurrence, remission, changes in illness activity or treatment failure (e.g. stopping lithium; new prescriptions of other mood stabilizers) and exposure to lithium. Novel elements of this study include the recruitment of a large, multinational, clinically representative sample specifically for the purpose of studying candidate biomarkers and biosignatures; the application of lithium-7 magnetic resonance imaging to explore the distribution of lithium in the brain; development of a digital phenotype (using actigraphy and ecological momentary assessment) to monitor daily variability in symptoms; and economic modelling of the cost-effectiveness of introducing biomarker tests for the customisation of lithium treatment into clinical practice. Also, study participants with sub-optimal medication adherence will be offered brief interventions (which can be delivered via a clinician or smartphone app) to enhance treatment engagement and to minimize confounding of lithium non-response with non-adherence.

Conclusions

The paper outlines the rationale, design and methodology of the first study being undertaken by the newly established R-LiNK collaboration and describes how the project may help to refine the clinical response phenotype and could translate into the personalization of lithium treatment.

Electronic supplementary material

The online version of this article (10.1186/s40345-019-0156-x) contains supplementary material, which is available to authorized users.