Challenges and opportunities for drug discovery in psychiatric disorders: the drug hunters' perspective

Deviation from Nash mixed equilibrium in repeated rock-scissors-paper reflect individual traits

Current psychiatric nosology is based on observed and self-reported symptoms. Heterogenous pathophysiological mechanisms may underlie similar symptoms leading to diagnosis not matching up to the neurobiology. Recent research has sought to move away from diagnoses by symptoms, to viewing aberrant mental health in terms of abnormal human neurobehavioral functioning and concurrent deviations in the pathophysiology. Human behavior in a social context is a core neurobehavioral function with large individual variation that may reflect genomic, metabolic or neurobiological variation, whose identification potentially yields more accurate targeting for the development of interventions and biomedical treatments. In this research, we describe an experimental framework that utilizes a zero-sum game of repeated Rock-Paper-Scissors played against an artificial intelligence agent as an assay of social interaction. Human deviation from the Nash Mixed Equilibrium strategy of play, the only guaranteed way to avoid exploitation, can be seen in the sequential dependence of hands. We hypothesize that this deviation represents humans mimicing randomness to avoid exploitation through constant adjustments of behavior, which we analyze in terms of a set of switching heuristic lag-1 conditional response rules. We quantify and interpret the set of rules subjects are able to utilize as mirroring individual traits. Subjects in the study also completed the Autism Quotient Abridged survey, and subscores of the social, imagination and routine factors were found to be predicted by a combination of behavioral features derived from game play.

Rethinking psychiatric symptoms: the role of measurement heterogeneity in diagnostic validity

The current research environment in psychiatry is marked by the discredit of the main psychiatric classifications. The common narrative about the DSM holds that the current diagnostic categories lack diagnostic validity. This claim is supported by the high degrees of diagnostic heterogeneity and comorbidity among diagnosed patients. Current attempts to overcome these problems emphasize the need to develop alternative ways of investigating psychopathology that no longer rely on the DSM categories. In this line, transdiagnostic research initiatives such as RDoC promote the abandonment of the DSM categories while still relying on traditional psychiatric symptoms. This reliance assumes that symptoms do not pose similar problems to the ones commonly ascribed to the DSM categories. In this article, I challenge what I call the "received view of symptoms" and argue that a closer look at symptom measurement reveals that different measurements of purportedly the same symptom differ from each other in ways that have an impact on both psychiatric research and clinical practice. Furthermore, I show that psychiatric symptoms are not "neutral" vis-à-vis the DSM categories. To illustrate my points, I use a case study from the history of the measurement of anhedonia. Based on this case study, I argue that in addition to diagnostic heterogeneity, there is also symptom measurement heterogeneity. Finally, I suggest a reassessment of the common narrative about the DSM's lack of diagnostic validity in light of my case study and argue that closer attention should be given to symptoms.

Identifying neurobiological heterogeneity in clinical high-risk psychosis: a data-driven biotyping approach using resting-state functional connectivity

To explore the neurobiological heterogeneity within the Clinical High-Risk (CHR) for psychosis population, this study aimed to identify and characterize distinct neurobiological biotypes within CHR using features from resting-state functional networks. A total of 239 participants from the Shanghai At Risk for Psychosis (SHARP) program were enrolled, consisting of 151 CHR individuals and 88 matched healthy controls (HCs). Functional connectivity (FC) features that were correlated with symptom severity were subjected to the single-cell interpretation through multikernel learning (SIMLR) algorithm in order to identify latent homogeneous subgroups. The cognitive function, clinical symptoms, FC patterns, and correlation with neurotransmitter systems of biotype profiles were compared. Three distinct CHR biotypes were identified based on 646 significant ROI-ROI connectivity features, comprising 29.8%, 19.2%, and 51.0% of the CHR sample, respectively. Despite the absence of overall FC differences between CHR and HC groups, each CHR biotype demonstrated unique FC abnormalities. Biotype 1 displayed augmented somatomotor connection, Biotype 2 shown compromised working memory with heightened subcortical and network-specific connectivity, and Biotype 3, characterized by significant negative symptoms, revealed extensive connectivity reductions along with increased limbic-subcortical connectivity. The neurotransmitter correlates differed across biotypes. Biotype 2 revealed an inverse trend to Biotype 3, as increased neurotransmitter concentrations improved functional connectivity in Biotype 2 but reduced it in Biotype 3. The identification of CHR biotypes provides compelling evidence for the early manifestation of heterogeneity within the psychosis spectrum, suggesting that distinct pathophysiological mechanisms may underlie these subgroups.

Neuropsychiatric drug development: Perspectives on the current landscape, opportunities and potential future directions

Mental health represents a major challenge to our societies. One key difficulty associated with neuropsychiatric drug development is the lack of connection between the underlying biology and the disease. Nevertheless, there is growing optimism in this field with recent drug approvals (the first in decades) and renewed interest from pharmaceutical companies and investors. Here we review some of the most promising drug discovery and development endeavors currently deployed by industry. We also present elements illustrating the renewed interest from key stakeholders in neuropsychiatric drug development and provide potential future directions in this field.

NMDAR dysfunction and the regulation of dopaminergic transmission in schizophrenia

A substantial body of evidence implicates dysfunction in N-methyl-d-aspartate receptors (NMDARs) in the pathophysiology of schizophrenia. This article illustrates how NMDAR dysfunction may give rise to many of the neurobiological phenomena frequently associated with schizophrenia with a particular focus on how NMDAR dysfunction affects the thalamic reticular nucleus (nRT) and pedunculopontine tegmental nucleus (PPTg). Furthermore, this article presents a model for schizophrenia illustrating how dysfunction in the nRT may interrupt prefrontal regulation of midbrain dopaminergic neurons, and how dysfunction in the PPTg may drive increased, irregular burst firing.

What Remains to Be Discovered in Schizophrenia Therapeutics: Contributions by Advancing the Molecular Mechanisms of Drugs for Psychosis and Schizophrenia

Schizophrenia is a frequently debilitating and complex mental disorder affecting approximately 1% of the global population, characterized by symptoms such as hallucinations, delusions, disorganized thoughts and behaviors, cognitive dysfunction, and negative symptoms. Traditional treatment has centered on postsynaptic dopamine antagonists, commonly known as antipsychotic drugs, which aim to alleviate symptoms and improve functioning and the quality of life. Despite the availability of these medications, significant challenges remain in schizophrenia therapeutics, including incomplete symptom relief, treatment resistance, and medication side effects. This opinion article explores advancements in schizophrenia treatment, emphasizing molecular mechanisms, novel drug targets, and innovative delivery methods. One promising approach is novel strategies that target neural networks and circuits rather than single neurotransmitters, acknowledging the complexity of brain region interconnections involved in schizophrenia. Another promising approach is the development of biased agonists, which selectively activate specific signaling pathways downstream of receptors, offering potential for more precise pharmacological interventions with fewer side effects. The concept of molecular polypharmacy, where a single drug targets multiple molecular pathways, is exemplified by KarXT, a novel drug combining xanomeline and trospium to address both psychosis and cognitive dysfunction. This approach represents a comprehensive strategy for schizophrenia treatment, potentially improving outcomes for patients. In conclusion, advancing the molecular understanding of schizophrenia and exploring innovative therapeutic strategies hold promise for addressing the unmet needs in schizophrenia treatment, aiming for more effective and tailored interventions. Future research should focus on these novel approaches to achieve better clinical outcomes and improve the functional level and quality of life for individuals with schizophrenia.

Identifying plasma metabolic characteristics of major depressive disorder, bipolar disorder, and schizophrenia in adolescents

Major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ) are classified as major mental disorders and together account for the second-highest global disease burden, and half of these patients experience symptom onset in adolescence. Several studies have reported both similar and unique features regarding the risk factors and clinical symptoms of these three disorders. However, it is still unclear whether these disorders have similar or unique metabolic characteristics in adolescents. We conducted a metabolomics analysis of plasma samples from adolescent healthy controls (HCs) and patients with MDD, BD, and SCZ. We identified differentially expressed metabolites between patients and HCs. Based on the differentially expressed metabolites, correlation analysis, metabolic pathway analysis, and potential diagnostic biomarker identification were conducted for disorders and HCs. Our results showed significant changes in plasma metabolism between patients with these mental disorders and HCs; the most distinct changes were observed in SCZ patients. Moreover, the metabolic differences in BD patients shared features with those in both MDD and SCZ, although the BD metabolic profile was closer to that of MDD than to SCZ. Additionally, we identified the metabolites responsible for the similar and unique metabolic characteristics in multiple metabolic pathways. The similar significant differences among the three disorders were found in fatty acid, steroid-hormone, purine, nicotinate, glutamate, tryptophan, arginine, and proline metabolism. Interestingly, we found unique characteristics of significantly altered glycolysis, glycerophospholipid, and sphingolipid metabolism in SCZ; lysine, cysteine, and methionine metabolism in MDD and BD; and phenylalanine, tyrosine, and aspartate metabolism in SCZ and BD. Finally, we identified five panels of potential diagnostic biomarkers for MDD-HC, BD-HC, SCZ-HC, MDD-SCZ, and BD-SCZ comparisons. Our findings suggest that metabolic characteristics in plasma vary across psychiatric disorders and that critical metabolites provide new clues regarding molecular mechanisms in these three psychiatric disorders.

Role of Biosynthesis and Catabolism of Neurotransmitters in Drug Discovery for Anxiety and Depression

The purpose of this review is to correlate the probable causes of anxiety disorders with the imbalance of neurotransmitters in the brain and also highlight the drugs for these mental disorders that have been discovered based on the biosynthesis and catabolism of these brain chemicals. Peer-reviewed journal's articles, news and books published in English between 1997 and 2023 describing the role of neurotransmitters in anxiety disorders were searched in Google Scholar, Research Gate and PubMed databases. The contents were carefully analyzed by the authors and understood and compiled to build a concise perspective on the role of biosynthesis and catabolism of neurotransmitters in anxiety and depression. Anxiety disorders are reported to be common patterns of psychological symptoms that impact multiple areas of life. Anxiety and depression are prevalent worldwide and are significantly contributing towards the global health burden. Genetic determinants are believed to play an important role in these disorders. According to modern medicine, one of the most important aspects that is known to be crucial for these disorders is the imbalance of neurotransmitters in the brain. The biosynthesis and catabolism of neurotransmitters have been extensively targeted for innovative drug discovery approaches at various steps that have led to the discovery of many drugs for these psychological disorders. The biosynthetic and catabolic reaction cycles of neurotransmitters and the discovery of drugs based on these hypotheses are discussed. To the best of the authors' knowledge, this review compiles already known descriptive knowledge on "relation of neurotransmitter imbalance with anxiety disorders" in a precise way that will provide readers with an overview of the vast literature.

Synthesis and Biological Evaluation of O6-Aminoalkyl-Hispidol Analogs as Multifunctional Monoamine Oxidase-B Inhibitors towards Management of Neurodegenerative Diseases

Oxidative catabolism of monoamine neurotransmitters by monoamine oxidases (MAOs) produces reactive oxygen species (ROS), which contributes to neuronal cells' death and also lowers monoamine neurotransmitter levels. In addition, acetylcholinesterase activity and neuroinflammation are involved in neurodegenerative diseases. Herein, we aim to achieve a multifunctional agent that inhibits the oxidative catabolism of monoamine neurotransmitters and, hence, the detrimental production of ROS while enhancing neurotransmitter levels. Such a multifunctional agent might also inhibit acetylcholinesterase and neuroinflammation. To meet this end goal, a series of aminoalkyl derivatives of analogs of the natural product hispidol were designed, synthesized, and evaluated against both monoamine oxidase-A (MAO-A) and monoamine oxidase-B (MAO-B). Promising MAO inhibitors were further checked for the inhibition of acetylcholinesterase and neuroinflammation. Among them, compounds 3aa and 3bc were identified as potential multifunctional molecules eliciting submicromolar selective MAO-B inhibition, low-micromolar AChE inhibition, and the inhibition of microglial PGE₂ production. An evaluation of their effects on memory and cognitive impairments using a passive avoidance test confirmed the in vivo activity of compound 3bc, which showed comparable activity to donepezil. In silico molecular docking provided insights into the MAO and acetylcholinesterase inhibitory activities of compounds 3aa and 3bc. These findings suggest compound 3bc as a potential lead for the further development of agents against neurodegenerative diseases.

Phosphodiesterase inhibitors in psychiatric disorders

RATIONALE

Challenges in drug development for psychiatric disorders have left much room for the introduction of novel treatments with better therapeutic efficacies and indices. As a result, intense research has focused on identifying new targets for developing such pharmacotherapies. One of these targets may be the phosphodiesterase (PDE) class of enzymes, which play important roles in intracellular signaling. Due to their critical roles in cellular pathways, these enzymes affect diverse neurobiological functions from learning and memory formation to neuroinflammation.

OBJECTIVES

In this paper, we reviewed studies on the use of PDE inhibitors (PDEIs) in preclinical models and clinical trials of psychiatric disorders including depression, anxiety, schizophrenia, post-traumatic stress disorder (PTSD), bipolar disorder (BP), sexual dysfunction, and feeding disorders.

RESULTS

PDEIs are able to improve symptoms of psychiatric disorders in preclinical models through activating the cAMP-PKA-CREB and cGMP-PKG pathways, attenuating neuroinflammation and oxidative stress, and stimulating neural plasticity. The most promising therapeutic candidates to emerge from these preclinical studies are PDE2 and PDE4 inhibitors for depression and anxiety and PDE1 and PDE10 inhibitors for schizophrenia. Furthermore, PDE3 and 4 inhibitors have shown promising results in clinical trials in patients with depression and schizophrenia.

CONCLUSIONS

Larger and better designed clinical studies of PDEIs in schizophrenia, depression, and anxiety are warranted to facilitate their translation into the clinic. Regarding the other conditions discussed in this review (most notably PTSD and BP), better characterization of the effects of PDEIs in preclinical models is required before clinical studies.

Anti-inflammatory properties of commonly used psychiatric drugs

Mental health and neurodevelopmental disorders are extremely common across the lifespan and are characterized by a complicated range of symptoms that affect wellbeing. There are relatively few drugs available that target disease mechanisms for any of these disorders. Instead, therapeutics are focused on symptoms and syndromes, largely driven by neurotransmitter hypotheses, such as serotonin or dopamine hypotheses of depression. Emerging evidence suggests that maternal inflammation during pregnancy plays a key role in neurodevelopmental disorders, and inflammation can influence mental health expression across the lifespan. It is now recognized that commonly used psychiatric drugs (anti-depressants, anti-psychotics, and mood stabilizers) have anti-inflammatory properties. In this review, we bring together the human evidence regarding the anti-inflammatory mechanisms for these main classes of psychiatric drugs across a broad range of mental health disorders. All three classes of drugs showed evidence of decreasing levels of pro-inflammatory cytokines, particularly IL-6 and TNF-α, while increasing the levels of the anti-inflammatory cytokine, IL-10. Some studies also showed evidence of reduced inflammatory signaling via nuclear factor- (NF-)κB and signal transducer and activator of transcription (STAT) pathways. As researchers, clinicians, and patients become increasingly aware of the role of inflammation in brain health, it is reassuring that these psychiatric drugs may also abrogate this inflammation, in addition to their effects on neurotransmission. Further studies are required to determine whether inflammation is a driver of disease pathogenesis, and therefore should be a therapeutic target in future clinical trials.

The Role of fMRI in Drug Development: An Update

Functional magnetic resonance imaging (fMRI) of the brain is a technology that holds great potential for increasing the efficiency of drug development for the central nervous system (CNS). In preclinical studies and both early- and late-phase human trials, fMRI has the potential to improve cross-species translation of drug effects, help to de-risk compounds early in development, and contribute to the portfolio of evidence for a compound's efficacy and mechanism of action. However, to date, the utilization of fMRI in the CNS drug development process has been limited. The purpose of this chapter is to explore this mismatch between potential and utilization. This chapter provides introductory material related to fMRI and drug development, describes what is required of fMRI measurements for them to be useful in a drug development setting, lists current capabilities of fMRI in this setting and challenges faced in its utilization, and ends with directions for future development of capabilities in this arena. This chapter is the 5-year update of material from a previously published workshop summary (Carmichael et al., Drug DiscovToday 23(2):333-348, 2018).

Positional scanning of natural product hispidol's ring-B: discovery of highly selective human monoamine oxidase-B inhibitor analogues downregulating neuroinflammation for management of neurodegenerative diseases

Multifunctional molecules might offer better treatment of complex multifactorial neurological diseases. Monoaminergic pathways dysregulation and neuroinflammation are common convergence points in diverse neurodegenerative and neuropsychiatric disorders. Aiming to target these diseases, polypharmacological agents modulating both monoaminergic pathways and neuroinflammatory were addressed. A library of analogues of the natural product hispidol was prepared and evaluated for inhibition of monoamine oxidases (MAOs) isoforms. Several molecules emerged as selective potential MAO B inhibitors. The most promising compounds were further evaluated in vitro for their impact on microglia viability, induced production of proinflammatory mediators and MAO-B inhibition mechanism. Amongst tested compounds, 1p was a safe potent competitive reversible MAO-B inhibitor and inhibitor of microglial production of neuroinflammatory mediators; NO and PGE2. In-silico study provided insights into molecular basis of the observed selective MAO B inhibition. This study presents compound 1p as a promising lead compound for management of neurodegenerative disease.

Feasibility and acceptability of a novel telepsychiatry-delivered precision prescribing intervention for anxiety and depression

BACKGROUND

Major Depressive Disorder and Generalized Anxiety Disorder are pervasive and debilitating conditions, though treatment is often inaccessible and based on trial-and-error prescribing methods. The present observational study seeks to describe the use of a proprietary precision prescribing algorithm piloted during routine clinical practice as part of Brightside's telepsychiatry services. The primary aim is to determine the feasibility and acceptability of implementing this intervention. Secondary aims include exploring remission and symptom improvement rates.

METHODS

Participants were adult patients enrolled in Brightside who completed at least 12 weeks of treatment for depression and/or anxiety and received a prescription for at least one psychiatric medication. A prescription recommendation was made by Brightside's algorithm at treatment onset and was utilized for clinical decision support. Participants received baseline screening surveys of the PHQ-9 and GAD-7, and at weeks 2,4,6,8,10 and 12. Intent-to-treat (ITT) sensitivity analyses were conducted. Feasibility of the implementation was measured by the platform's ability to enroll and engage participants in timely psychiatric care, as well as offer high touch-point treatment options. Acceptability was measured by patient responses to a 5-star satisfaction rating.

RESULTS

Brightside accessed and treated 6248 patients from October 2018 to April 2021, treating a majority of patients within 4-days of enrollment. The average plan cost was $115/month. 89% of participants utilized Brightside's core medication plan at a cost of $95/month. 13.4% of patients in the study rated Brightside's services as highly satisfactory, averaging a 4.6-star rating. Furthermore, 90% of 6248 patients experienced a MCID in PHQ-9 or GAD-7 score. Remission rates were 75% (final PHQ-9 or GAD-7 score < 10) for the study sample and 59% for the ITT sample. 69.3% of Brightside patients were treated with the medication initially prescribed at intake.

CONCLUSIONS

Results suggest that the present intervention may be feasible and acceptable within the assessed population. Exploratory analyses suggest that Brightside's course of treatment, guided by precision recommendations, improved patients' symptoms of anxiety and depression.

Mapping Research Domain Criteria using a transdiagnostic mini-RDoC assessment in mental disorders: a confirmatory factor analysis

This study aimed to build on the relationship of well-established self-report and behavioral assessments to the latent constructs positive (PVS) and negative valence systems (NVS), cognitive systems (CS), and social processes (SP) of the Research Domain Criteria (RDoC) framework in a large transnosological population which cuts across DSM/ICD-10 disorder criteria categories. One thousand four hundred and thirty one participants (42.1% suffering from anxiety/fear-related, 18.2% from depressive, 7.9% from schizophrenia spectrum, 7.5% from bipolar, 3.4% from autism spectrum, 2.2% from other disorders, 18.4% healthy controls, and 0.2% with no diagnosis specified) recruited in studies within the German research network for mental disorders for the Phenotypic, Diagnostic and Clinical Domain Assessment Network Germany (PD-CAN) were examined with a Mini-RDoC-Assessment including behavioral and self-report measures. The respective data was analyzed with confirmatory factor analysis (CFA) to delineate the underlying latent RDoC-structure. A revised four-factor model reflecting the core domains positive and negative valence systems as well as cognitive systems and social processes showed a good fit across this sample and showed significantly better fit compared to a one factor solution. The connections between the domains PVS, NVS and SP could be substantiated, indicating a universal latent structure spanning across known nosological entities. This study is the first to give an impression on the latent structure and intercorrelations between four core Research Domain Criteria in a transnosological sample. We emphasize the possibility of using already existing and well validated self-report and behavioral measurements to capture aspects of the latent structure informed by the RDoC matrix.

Machine learning models to predict neuropsychiatric disorders in various brain tumors

OBJECTIVE

Neuropsychiatric disorders in brain tumor patients are commonly observed. It is difficult to anticipate these disorders in different types of brain tumors. The goal of the study was to see how well machine learning (ML)-based decision algorithms might predict neuropsychiatric problems in different types of brain tumors.

METHODS

145 histopathologically-confirmed primary brain tumors of both gender aged 25-65 years of age, were included for neuropsychiatric assessments. The datasets of brain tumor patients were employed for building the models. Four different decision ML classification trees/models (J48, Random Forest, Random Tree & Hoeffding Tree) with supervised learning were trained, tested, and validated on class labeled data of brain tumor patients. The models were compared in order to determine the best accurate classifier in predicting neuropsychiatric problems in various brain tumors. Following categorical attributes as independent variables (predictors) were included from the data of brain tumor patients: age, gender, depression, dementia, and brain tumor types. With the machine learning decision tree/model techniques, a multi-target classification was performed with classes of neuropsychiatric diseases that were predicted from the selected attributes.

RESULTS

86 percent of patients were depressed, and 55 percent were suffering from dementia. Anger was the most often reported neuropsychiatric condition in brain tumor patients (92.41%), followed by sleep disorders (83%), apathy (80%), and mood swings (76.55%). When compared to other tumor types, glioblastoma patients had a higher rate of depression (20%) and dementia (20.25%). The developed models Random Forest and Random Tree were found successful with an accuracy of up to 94% (10-folds) for the prediction of neuropsychiatric disorders in brain tumor patients. The multiclass target (neuropsychiatric ailments) accuracies were having good measures of precision (0.9-1.0), recall (0.9-1.0), F-measure (0.9-1.0), and ROC area (0.9-1.0) in decision models.

CONCLUSION

Random Forest Trees can be used to accurately predict neuropsychiatric illnesses. Based on the model output, the ML-decision trees will aid the physician in pre-diagnosing the mental issue and deciding on the best therapeutic approach to avoid subsequent neuropsychiatric issues in brain tumor patients.

Digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectives

Depression is a serious medical condition and is a leading cause of disability worldwide. Current depression diagnostics and assessment has significant limitations due to heterogeneity of clinical presentations, lack of objective assessments, and assessments that rely on patients' perceptions, memory, and recall. Digital phenotyping (DP), especially assessments conducted using mobile health technologies, has the potential to greatly improve accuracy of depression diagnostics by generating objectively measurable endophenotypes. DP includes two primary sources of digital data generated using ecological momentary assessments (EMA), assessments conducted in real-time, in subjects' natural environment. This includes active EMA, data that require active input by the subject, and passive EMA or passive sensing, data passively and automatically collected from subjects' personal digital devices. The raw data is then analyzed using machine learning algorithms to identify behavioral patterns that correlate with patients' clinical status. Preliminary investigations have also shown that linguistic and behavioral clues from social media data and data extracted from the electronic medical records can be used to predict depression status. These other sources of data and recent advances in telepsychiatry can further enhance DP of the depressed patients. Success of DP endeavors depends on critical contributions from both psychiatric and engineering disciplines. The current review integrates important perspectives from both disciplines and discusses parameters for successful interdisciplinary collaborations. A clinically-relevant model for incorporating DP in clinical setting is presented. This model, based on investigations conducted by our group, delineates development of a depression prediction system and its integration in clinical setting to enhance depression diagnostics and inform the clinical decision making process. Benefits, challenges, and opportunities pertaining to clinical integration of DP of depression diagnostics are discussed from interdisciplinary perspectives.

A Systematic Approach to Neuropsychiatric Intervention: Functional Neuroanatomy Underlying Symptom Domains as Targets for Treatment

An ever-growing population experiences a wide range of psychopathologies, and there is now more than ever a need for clear differential diagnoses between disorders. Furthering this need is the fact that many psychological, psychiatric, and neurological disorders have overlapping features. Functional neuroimaging has been shown to differentiate not only between the function of different brain structures but also between the roles of these structures in functional networks. The aim of this article is to aid in the goal of parsing out disorders on the basis of specific symptom domains by utilizing the most recent literature on functional networks. Current literature on the role of brain networks in relation to different psychopathological symptom domains is examined and corresponding circuit-based therapies that have been or may be used to treat them are discussed. Research on depression, obsession and compulsions, addiction, anxiety, and psychosis is reviewed. An understanding of networks and their specific dysfunctions opens the possibility of a new form of psychopathological treatment.

Estimation of the Development of Depression and PTSD in Children Exposed to Sexual Abuse and Development of Decision Support Systems by Using Artificial Intelligence

The most common diagnoses after childhood sexual abuse are Post-Traumatic Stress Disorder and depression. The aim of this study is to design a decision support system to help psychiatry physicians in the treatment of childhood sexual abuse. Computer aided decision support system (CADSS) based on ANN, which predicts the development of PTSD and Major Depressive Disorder, using different parameters of the act of abuse and patients was designed. The data of 149 girls and 21 boys who were victims of sexual abuse were included in the study. In the designed CADDS, the gender of the victim, the type of sexual abuse, the age of exposure, the duration until reporting, the time of abuse, the proximity of the abuser to the victim, number of sexual abuse, whether the child is exposed to threats and violence during the abuse, the person who reported the event, and the intelligence level of the victim are used as input parameters. The average accuracy values for all three designed systems were calculated as 99.2%. It has been shown that the system designed by using these data can be used safely in the psychiatric assessment process, in order to differentiate psychiatric diagnoses in the early post-abuse period.

'Omics' of suicidal behaviour: A path to personalised psychiatry

Psychiatric disorders, including suicide, are complex disorders that are affected by many different risk factors. It has been estimated that genetic factors contribute up to 50% to suicide risk. As the candidate gene approach has not identified a gene or set of genes that can be defined as biomarkers for suicidal behaviour, much is expected from cutting edge technological approaches that can interrogate several hundred, or even millions, of biomarkers at a time. These include the '-omic' approaches, such as genomics, transcriptomics, epigenomics, proteomics and metabolomics. Indeed, these have revealed new candidate biomarkers associated with suicidal behaviour. The most interesting of these have been implicated in inflammation and immune responses, which have been revealed through different study approaches, from genome-wide single nucleotide studies and the micro-RNA transcriptome, to the proteome and metabolome. However, the massive amounts of data that are generated by the '-omic' technologies demand the use of powerful computational analysis, and also specifically trained personnel. In this regard, machine learning approaches are beginning to pave the way towards personalized psychiatry.

Precision Psychiatry: Machine learning as a tool to find new pharmacological targets

There is an increasing amount of data arising from neurobehavioral sciences and medical records that cannot be adequately analyzed by traditional research methods. New drugs develop at a slow rate and seem unsatisfactory for the majority of neurobehavioral disorders. Machine learning (ML) techniques, instead, can incorporate psychopathological, computational, cognitive, and neurobiological underpinning knowledge leading to a refinement of detection, diagnosis, prognosis, treatment, research, and support. Machine and deep learning methods are currently used to accelerate the process of discovering new pharmacological targets and drugs.

OBJECTIVE

The present work reviews current evidence regarding the contribution of machine learning to the discovery of new drug targets.

METHODS

Scientific articles from PubMed, SCOPUS, EMBASE, and Web of Science Core Collection published until May 2021 were included in this review.

RESULTS

The most significant areas of research are schizophrenia, depression and anxiety, Alzheimer´s disease, and substance use disorders. ML techniques have pinpointed target gene candidates and pathways, new molecular substances, and several biomarkers regarding psychiatric disorders. Drug repositioning studies using ML have identified multiple drug candidates as promising therapeutic agents.

CONCLUSION

Next-generation ML techniques and subsequent deep learning may power new findings regarding the discovery of new pharmacological agents by bridging the gap between biological data and chemical drug information.

Pharmacogenomics: A road ahead for precision medicine in psychiatry

Psychiatric genomics is providing insights into the nature of psychiatric conditions that in time should identify new drug targets and improve patient care. Less attention has been paid to psychiatric pharmacogenomics research, despite its potential to deliver more rapid change in clinical practice and patient outcomes. The pharmacogenomics of treatment response encapsulates both pharmacokinetic ("what the body does to a drug") and pharmacodynamic ("what the drug does to the body") effects. Despite early optimism and substantial research in both these areas, they have to date made little impact on clinical management in psychiatry. A number of bottlenecks have hampered progress, including a lack of large-scale replication studies, inconsistencies in defining valid treatment outcomes across experiments, a failure to routinely incorporate adverse drug reactions and serum metabolite monitoring in study designs, and inadequate investment in the longitudinal data collections required to demonstrate clinical utility. Nonetheless, advances in genomics and health informatics present distinct opportunities for psychiatric pharmacogenomics to enter a new and productive phase of research discovery and translation.

Mismatch negativity as EEG biomarker supporting CNS drug development: a transnosographic and translational study

The lack of translation from basic research into new medicines is a major challenge in CNS drug development. The need to use novel approaches relying on (i) patient clustering based on neurobiology irrespective to symptomatology and (ii) quantitative biomarkers focusing on evolutionarily preserved neurobiological systems allowing back-translation from clinical to nonclinical research has been highlighted. Here we sought to evaluate the mismatch negativity (MMN) response in schizophrenic (SZ) patients, Alzheimer's disease (AD) patients, and age-matched healthy controls. To evaluate back-translation of the MMN response, we developed EEG-based procedures allowing the measurement of MMN-like responses in a rat model of schizophrenia and a mouse model of AD. Our results indicate a significant MMN attenuation in SZ but not in AD patients. Consistently with the clinical findings, we observed a significant attenuation of deviance detection (~104.7%) in rats subchronically exposed to phencyclidine, while no change was observed in APP/PS1 transgenic mice when compared to wild type. This study provides new insight into the cross-disease evaluation of the MMN response. Our findings suggest further investigations to support the identification of neurobehavioral subtypes that may help patients clustering for precision medicine intervention. Furthermore, we provide evidence that MMN could be used as a quantitative/objective efficacy biomarker during both preclinical and clinical stages of SZ drug development.

Targeting Arousal and Sleep through Noninvasive Brain Stimulation to Improve Mental Health

Arousal and sleep represent fundamental physiological domains, and alterations in the form of insomnia (difficulty falling or staying asleep) or hypersomnia (increased propensity for falling asleep or increased sleep duration) are prevalent clinical problems. Current first-line treatments include psychotherapy and pharmacotherapy. Despite significant success, a number of patients do not benefit sufficiently. Progress is limited by an incomplete understanding of the -neurobiology of insomnia and hypersomnia. This work summarizes current concepts of the regulation of arousal and sleep and its modulation through noninvasive brain stimulation (NIBS), including transcranial magnetic, current, and auditory stimulation. Particularly, we suggest: (1) characterization of patients with sleep problems - across diagnostic entities of mental disorders - based on specific alterations of sleep, including alterations of sleep slow waves, sleep spindles, cross-frequency coupling of brain oscillations, local sleep-wake regulation, and REM sleep and (2) targeting these with specific NIBS techniques. While evidence is accumulating that the modulation of specific alterations of sleep through NIBS is feasible, it remains to be tested whether this translates to clinically relevant effects and new treatment developments.

The General Factor of Psychopathology in the Adolescent Brain Cognitive Development (ABCD) Study: A Comparison of Alternative Modeling Approaches

Many models of psychopathology include a single general factor of psychopathology (GFP) or "p factor" to account for covariation across symptoms. The Adolescent Brain Cognitive Development (ABCD) Study provides a rich opportunity to study the development of the GFP. However, a variety of approaches for modeling the GFP have emerged, raising questions about how modeling choices impact estimated GFP scores. We used the ABCD baseline assessment (ages 9-10 years-old; N=11,875) of the parent-rated Child Behavior Checklist (CBCL) to examine the implications of modeling the GFP using items versus scales; using a priori CBCL scales versus data-driven dimensions; and using bifactor, higher-order, or single-factor models. Children's rank-ordering on the GFP was stable across models, with GFP scores similarly related to criterion variables. Results suggest that while theoretical debates about modeling the GFP continue, the practical implications of these choices for rank-ordering children and assessing external associations will often be modest.

Heterogeneity and Classification of Recent Onset Psychosis and Depression: A Multimodal Machine Learning Approach

Diagnostic heterogeneity within and across psychotic and affective disorders challenges accurate treatment selection, particularly in the early stages. Delineation of shared and distinct illness features at the phenotypic and brain levels may inform the development of more precise differential diagnostic tools. We aimed to identify prototypes of depression and psychosis to investigate their heterogeneity, with common, comorbid transdiagnostic symptoms. Analyzing clinical/neurocognitive and grey matter volume (GMV) data from the PRONIA database, we generated prototypic models of recent-onset depression (ROD) vs. recent-onset psychosis (ROP) by training support-vector machines to separate patients with ROD from patients with ROP, who were selected for absent comorbid features (pure groups). Then, models were applied to patients with comorbidity, ie, ROP with depressive symptoms (ROP+D) and ROD participants with sub-threshold psychosis-like features (ROD+P), to measure their positions within the affective-psychotic continuum. All models were independently validated in a replication sample. Comorbid patients were positioned between pure groups, with ROP+D patients being more frequently classified as ROD compared to pure ROP patients (clinical/neurocognitive model: χ2 = 14.874; P < .001; GMV model: χ2 = 4.933; P = .026). ROD+P patient classification did not differ from ROD (clinical/neurocognitive model: χ2 = 1.956; P = 0.162; GMV model: χ2 = 0.005; P = .943). Clinical/neurocognitive and neuroanatomical models demonstrated separability of prototypic depression from psychosis. The shift of comorbid patients toward the depression prototype, observed at the clinical and biological levels, suggests that psychosis with affective comorbidity aligns more strongly to depressive rather than psychotic disease processes. Future studies should assess how these quantitative measures of comorbidity predict outcomes and individual responses to stratified therapeutic interventions.

Time to re-engage psychiatric drug discovery by strengthening confidence in preclinical psychopharmacology

BACKGROUND

There is urgent need for new medications for psychiatric disorders. Mental illness is expected to become the leading cause of disability worldwide by 2030. Yet, the last two decades have seen the pharmaceutical industry withdraw from psychiatric drug discovery after costly late-stage trial failures in which clinical efficacy predicted pre-clinically has not materialised, leading to a crisis in confidence in preclinical psychopharmacology.

METHODS

Based on a review of the relevant literature, we formulated some principles for improving investment in translational neuroscience aimed at psychiatric drug discovery.

RESULTS

We propose the following 8 principles that could be used, in various combinations, to enhance CNS drug discovery: (1) consider incorporating the NIMH Research Domain Criteria (RDoC) approach; (2) engage the power of translational and systems neuroscience approaches; (3) use disease-relevant experimental perturbations; (4) identify molecular targets via genomic analysis and patient-derived pluripotent stem cells; (5) embrace holistic neuroscience: a partnership with psychoneuroimmunology; (6) use translational measures of neuronal activation; (7) validate the reproducibility of findings by independent collaboration; and (8) learn and reflect. We provide recent examples of promising animal-to-human translation of drug discovery projects and highlight some that present re-purposing opportunities.

CONCLUSIONS

We hope that this review will re-awaken the pharma industry and mental health advocates to the opportunities for improving psychiatric pharmacotherapy and so restore confidence and justify re-investment in the field.

Deep learning for small and big data in psychiatry

Psychiatry today must gain a better understanding of the common and distinct pathophysiological mechanisms underlying psychiatric disorders in order to deliver more effective, person-tailored treatments. To this end, it appears that the analysis of 'small' experimental samples using conventional statistical approaches has largely failed to capture the heterogeneity underlying psychiatric phenotypes. Modern algorithms and approaches from machine learning, particularly deep learning, provide new hope to address these issues given their outstanding prediction performance in other disciplines. The strength of deep learning algorithms is that they can implement very complicated, and in principle arbitrary predictor-response mappings efficiently. This power comes at a cost, the need for large training (and test) samples to infer the (sometimes over millions of) model parameters. This appears to be at odds with the as yet rather 'small' samples available in psychiatric human research to date (n < 10,000), and the ambition of predicting treatment at the single subject level (n = 1). Here, we aim at giving a comprehensive overview on how we can yet use such models for prediction in psychiatry. We review how machine learning approaches compare to more traditional statistical hypothesis-driven approaches, how their complexity relates to the need of large sample sizes, and what we can do to optimally use these powerful techniques in psychiatric neuroscience.

Implications of de novo mutations in guiding drug discovery: A study of four neuropsychiatric disorders

Recent studies have suggested an important role of de novo mutations (DNMs) in neuropsychiatric disorders. As DNMs are not subject to elimination due to evolutionary pressure, they are likely to have greater disruptions on biological functions. While a number of sequencing studies have been performed on neuropsychiatric disorders, the implications of DNMs for drug discovery remain to be explored. In this study, we employed a gene-set analysis approach to address this issue. Four neuropsychiatric disorders were studied, including schizophrenia (SCZ), autistic spectrum disorders (ASD), intellectual disability (ID) and epilepsy. We first identified gene-sets associated with different drugs, and analyzed whether the gene-set pertaining to each drug overlaps with DNMs more than expected by chance. We also assessed which medication classes are enriched among the prioritized drugs. We discovered that neuropsychiatric drug classes were indeed significantly enriched for DNMs of all four disorders; in particular, antipsychotics and antiepileptics were the most strongly enriched drug classes for SCZ and epilepsy respectively. Interestingly, we revealed enrichment of several unexpected drug classes, such as lipid-lowering agents for SCZ and anti-neoplastic agents. By inspecting individual hits, we also uncovered other interesting drug candidates or mechanisms (e.g. histone deacetylase inhibition and retinoid signaling) that might warrant further investigations. Taken together, this study provided evidence for the usefulness of DNMs in guiding drug discovery or repositioning.

Enhancing the Utility of Preclinical Research in Neuropsychiatry Drug Development

Most large pharmaceutical companies have downscaled or closed their clinical neuroscience research programs in response to the low clinical success rate for drugs that showed tremendous promise in animal experiments intended to model psychiatric pathophysiology. These failures have raised serious concerns about the role of preclinical research in the identification and evaluation of new pharmacotherapies for psychiatry. In the absence of a comprehensive understanding of the neurobiology of psychiatric disorders, the task of developing "animal models" seems elusive. The purpose of this review is to highlight emerging strategies to enhance the utility of preclinical research in the drug development process. We address this issue by reviewing how advances in neuroscience, coupled with new conceptual approaches, have recently revolutionized the way we can diagnose and treat common psychiatric conditions. We discuss the implications of these new tools for modeling psychiatric conditions in animals and advocate for the use of systematic reviews of preclinical work as a prerequisite for conducting psychiatric clinical trials. We believe that work in animals is essential for elucidating human psychopathology and that improving the predictive validity of animal models is necessary for developing more effective interventions for mental illness.

Precision pharmacotherapy: psychiatry's future direction in preventing, diagnosing, and treating mental disorders

Mental disorders account for around one-third of disability worldwide and cause enormous personal and societal burden. Current pharmacotherapies and nonpharmacotherapies do help many patients, but there are still high rates of partial or no response, delayed effect, and unfavorable adverse effects. The current diagnostic taxonomy of mental disorders by the Diagnostic and Statistical Manual of Mental Disorders and the International Classification of Diseases relies on presenting signs and symptoms, but does not reflect evidence from neurobiological and behavioral systems. However, in the last decades, the understanding of biological mechanisms underlying mental disorders has grown and can be used for the development of precision medicine, that is, to deliver a patient-tailored individual treatment. Precision medicine may incorporate genetic variants contributing to the mental disorder and the response to pharmacotherapies, but also consider gene ¥ environment interactions, blood-based markers, neuropsychological tests, data from electronic health records, early life adversity, stressful life events, and very proximal factors such as lifestyle, nutrition, and sport. Methods such as artificial intelligence and the underlying machine learning and deep learning approaches provide the framework to stratify patients, initiate specific tailored treatments and thus increase response rates, reduce adverse effects and medical errors. In conclusion, precision medicine uses measurable health parameters to identify individuals at risk of a mental disorder, to improve the diagnostic process and to deliver a patient-tailored treatment.

Animal models in addiction research: A dimensional approach

Drug addiction affects approximately 10% of the population and these numbers are rising. Treatment and prevention of addiction are impeded by current diagnostic systems, such as DSM-5, which are based on outcomes rather than processes. Here, we review the importance of adopting a dimensional framework, specifically the Research Domain Criteria (RDoC), to identify protective and vulnerability mechanisms in addiction. We discuss how preclinical researchers should work within this framework to develop animal models based on domains of function. We highlight RDoC paradigms related to addiction and discuss how these can be used to investigate the biological underpinnings of an addiction cycle (i.e., binge/intoxication, negative affect, and craving). Using this information, we then outline the critical role of animal research in ongoing revisions to the RDoC matrix (specifically the functional significance of domains, constructs and subconstructs) and its contribution to the development and refinement of addiction theories. We conclude with an overview of the contribution that animal research has made to the development of pharmacological and behavioural treatments for addiction.

Targeting recovery in first episode psychosis: The importance of neurocognition and premorbid adjustment in a 3-year longitudinal study

INTRODUCTION

Recovery in psychotic disorders remains a major challenge across mental health. Identifying predictors of recovery in first psychotic episodes is a priority in order to increase knowledge on underlying mechanisms of the illness and to obtain objective severity markers at initial phases. In this study we gathered sociodemographic, clinical and cognitive data to explore predictive variables of recovery after three years follow-up in a sample of 399 patients with a first episode of psychosis (FEP).

MATERIAL AND METHOD

This is a longitudinal study including patients with a FEP. A dichotomic variable of recovery was created according to symptomatic and functional outcome after 3years follow-up. Significant variables in univariate analysis were entered into a binary logistic regression to obtain a multivariate prediction model of recovery.

RESULTS

The predictive model was statistically significant and classified an overall of 76% of patients correctly, specifically 86.7% of patients that would not recover and 55% of the patients that would recover. From all the variables that where significantly different between recovered and not recovered patients, only speed of processing, executive functions and premorbid adjustment were found to be significant predictors of recovery.

DISCUSSION

This study provides evidence that the degree of basal impairment in cognitive functions related to the Prefrontal Cortex and a worst premorbid adaptation predict in a significant way which patients are less likely to recover three years after a FEP.

The role of fMRI in drug development

Functional magnetic resonance imaging (fMRI) has been known for over a decade to have the potential to greatly enhance the process of developing novel therapeutic drugs for prevalent health conditions. However, the use of fMRI in drug development continues to be relatively limited because of a variety of technical, biological, and strategic barriers that continue to limit progress. Here, we briefly review the roles that fMRI can have in the drug development process and the requirements it must meet to be useful in this setting. We then provide an update on our current understanding of the strengths and limitations of fMRI as a tool for drug developers and recommend activities to enhance its utility.

Machine Learning Approaches for Clinical Psychology and Psychiatry

Machine learning approaches for clinical psychology and psychiatry explicitly focus on learning statistical functions from multidimensional data sets to make generalizable predictions about individuals. The goal of this review is to provide an accessible understanding of why this approach is important for future practice given its potential to augment decisions associated with the diagnosis, prognosis, and treatment of people suffering from mental illness using clinical and biological data. To this end, the limitations of current statistical paradigms in mental health research are critiqued, and an introduction is provided to critical machine learning methods used in clinical studies. A selective literature review is then presented aiming to reinforce the usefulness of machine learning methods and provide evidence of their potential. In the context of promising initial results, the current limitations of machine learning approaches are addressed, and considerations for future clinical translation are outlined.

Machine Learning for Precision Psychiatry: Opportunities and Challenges

The nature of mental illness remains a conundrum. Traditional disease categories are increasingly suspected to misrepresent the causes underlying mental disturbance. Yet psychiatrists and investigators now have an unprecedented opportunity to benefit from complex patterns in brain, behavior, and genes using methods from machine learning (e.g., support vector machines, modern neural-network algorithms, cross-validation procedures). Combining these analysis techniques with a wealth of data from consortia and repositories has the potential to advance a biologically grounded redefinition of major psychiatric disorders. Increasing evidence suggests that data-derived subgroups of psychiatric patients can better predict treatment outcomes than DSM/ICD diagnoses can. In a new era of evidence-based psychiatry tailored to single patients, objectively measurable endophenotypes could allow for early disease detection, individualized treatment selection, and dosage adjustment to reduce the burden of disease. This primer aims to introduce clinicians and researchers to the opportunities and challenges in bringing machine intelligence into psychiatric practice.

Anhedonia Is Associated with Poorer Outcomes in Contingency Management for Cocaine Use Disorder

This study explored anhedonia (lack of interest or pleasure in non-drug rewards) as a potentially modifiable individual difference associated with the effectiveness of Contingency Management (CM). It also tested the hypothesis that a dopaminergic drug, levodopa (L-DOPA), would improve the effectiveness of CM, particularly in individuals high in anhedonia. The study was a single-site, randomized, double-blind, parallel group, 12-week trial comparing L-DOPA with placebo, with both medication groups receiving voucher-based CM targeting cocaine-negative urines. Participants were N=85 treatment-seeking adults with CUD. Anhedonia was measured at baseline using a validated self-report measure and a progressive ratio behavioral measure. Treatment Effectiveness Score (TES) was defined as the total number of cocaine-negative urines submitted. Analyses based on Frequentist general linear models were not significant, but Bayesian analyses indicated a high probability (92.6%) that self-reported anhedonia was associated with poor treatment outcomes (lower TES). L-DOPA did not significantly improve outcomes, nor was the effect of L-DOPA moderated by anhedonia. While the study failed to replicate positive findings from previous studies of L-DOPA in combination with CM, it does provide preliminary evidence that anhedonia may be a modifiable individual difference associated with poorer CM outcomes.

Affective responses across psychiatric disorders-A dimensional approach

Studying psychiatric disorders across nosological boundaries aims at a better understanding of mental disorders by identifying comprehensive signatures of core symptoms. Here, we studied neurobiological correlates of emotion processing in several major psychiatric disorders. We assessed differences between diagnostic groups, and investigated whether there is a psychopathological correlate of emotion processing that transcends disorder categories. 135 patient with psychiatric disorders (alcohol dependence, n=29; schizophrenia, n=37; major depressive disorder (MDD), n=25; acute manic episode of bipolar disorder, n=12; panic disorder, n=12, attention deficit/hyperactivity disorder (ADHD), n=20) and healthy controls (n=40) underwent an functional magnetic resonance imaging (fMRI) experiment with affectively positive, aversive and neutral pictures from the International Affective Picture System (IAPS). Between-group differences were assessed with full-factorial ANOVAs, with age, gender and smoking habits as covariates. Self-ratings of depressed mood and anxiety were correlated with activation clusters showing significant stimulus-evoked fMRI activation. Furthermore, we examined functional connectivity with the amygdala as seed region during the processing of aversive pictures. During the presentation of pleasant stimuli, we observed across all subjects significant activation of the ventromedial prefrontal cortex (vmPFC), bilateral middle temporal gyrus and right precuneus, while a significant activation of the left amygdala and the bilateral middle temporal gyrus was found during the presentation of aversive stimuli. We did neither find any significant interaction with diagnostic group, nor any correlation with depression and anxiety scores at the activated clusters or with amygdala connectivity. Positive and aversive IAPS-stimuli were consistently processed in limbic and prefrontal brain areas, irrespective of diagnostic category. A dimensional correlate of these neural activation patterns was not found.

Opioid antagonists in broadly defined behavioral addictions: a narrative review

INTRODUCTION

Naltrexone (NTX), a mu-opioid receptor antagonist, has been approved for the treatment of alcoholism and opioid dependence. More recently, however, NTX and a related drug, nalmefene (NMF), have also shown positive results for the treatment of gambling disorders.

AREAS COVERED

In this study, we reviewed the trials testing the effect of opioid antagonists (OA) in gambling disorders and in other broadly defined behavioral addictions, including selected DSM-5 disruptive, impulse-control, and conduct disorders, obsessive-compulsive and related disorders, eating disorders, and other conditions not currently recognized by official classification schemes. We found six randomized controlled trials (RCTs) of OA in gambling disorder, two RCTs of OA in trichotillomania (hair pulling disorder), two RCTs of OA in binge eating disorder, and one RCT of OA for kleptomania. We also reviewed case reports on hypersexual disorder, compulsive buying and skin picking disorders.

EXPERT OPINION

The reviewed data supported the use of OA, namely NTX and NMF, in gambling disorder (both) and kleptomania (NTX). We did not find enough evidence to support the use of NTX or NMF in trichotillomania (hair pulling disorder), excoriation (skin-picking) disorder, compulsive buying disorder, hypersexual disorder, or binge eating disorder.

Animal Models of Maladaptive Traits: Disorders in Sensorimotor Gating and Attentional Quantifiable Responses as Possible Endophenotypes

Traditional diagnostic scales are based on a number of symptoms to evaluate and classify mental diseases. In many cases, this process becomes subjective, since the patient must calibrate the magnitude of his/her symptoms and therefore the severity of his/her disorder. A completely different approach is based on the study of the more vulnerable traits of cognitive disorders. In this regard, animal models of mental illness could be a useful tool to characterize indicators of possible cognitive dysfunctions in humans. Specifically, several cognitive disorders such as schizophrenia involve a dysfunction in the mesocorticolimbic dopaminergic system during development. These variations in dopamine levels or dopamine receptor sensibility correlate with many behavioral disturbances. These behaviors may be included in a specific phenotype and may be analyzed under controlled conditions in the laboratory. The present study provides an introductory overview of different quantitative traits that could be used as a possible risk indicator for different mental disorders, helping to define a specific endophenotype. Specifically, we examine different experimental procedures to measure impaired response in attention linked to sensorimotor gating as a possible personality trait involved in maladaptive behaviors.

Research Domain Criteria: toward future psychiatric nosologies

The Research Domain Criteria (RDoC) project was initiated by the National Institute of Mental Health (NIMH) in early 2009 as the implementation of Goal 1.4 of its just-issued strategic plan. In keeping with the NIMH mission, to “transform the understanding and treatment of mental illnesses through basic and clinical research,” RDoC was explicitly conceived as a research-related initiative. The statement of the relevant goal in the strategic plan reads: “Develop, for research purposes, new ways of classifying mental disorders based on dimensions of observable behavior and neurobiological measures.” Due to the novel approach that RDoC takes to conceptualizing and studying mental disorders, it has received widespread attention, well beyond the borders of the immediate research community. This review discusses the rationale for the experimental framework that RDoC has adopted, and its implications for the nosology of mental disorders in the future.

Integrating Children's Mental Health into Primary Care

Children's mental health problems are among global health advocates' highest priorities. Nearly three-quarters of adult disorders have their onset or origins during childhood, becoming progressively harder to treat over time. Integrating mental health with primary care and other more widely available health services has the potential to increase treatment access during childhood, but requires re-design of currently-available evidence-based practices to fit the context of primary care and place a greater emphasis on promoting positive mental health. While some of this re-design has yet to be accomplished, several components are currently well-defined and show promise of effectiveness and practicality.