Why the clinical utility of diagnostic categories in psychiatry is intrinsically limited and how we can use new approaches to complement them

Effectiveness and predictors of psychotherapy in eating disorders: state-of-the-art and future directions

PURPOSE OF REVIEW

Psychotherapy is the cornerstone of the multidisciplinary treatment approach for eating disorders. This review examines recent evidence regarding effectiveness, predictors, and mechanisms of change of psychotherapy in eating disorders, providing a road map for clinicians and researchers.

RECENT FINDINGS

Family-based treatments (FBT) are effective in adolescents with anorexia nervosa and bulimia nervosa. Evidence-based psychotherapies for anorexia nervosa have no evidence of superiority compared with treatment as usual (TAU) in adults with anorexia nervosa. Cognitive-behavioural therapy (CBT) is the first-choice psychotherapy recommended for adults with bulimia nervosa and binge-eating disorder (BED). Self-help interventions have some evidence of effectiveness in nonunderweight individuals with eating disorders. Early symptom improvement and adolescent age predict more favourable outcomes.

SUMMARY

Evidence-based psychotherapies can be suggested for eating disorders, although follow-up data are needed. Beyond anorexia nervosa, bulimia nervosa, and BED, there is no evidence of psychotherapy effectiveness in other eating disorders. The effectiveness of novel (e.g. 'third-wave') psychotherapies, treatment delivery modality (e.g. internet-delivered), and adjunctive interventions (e.g. virtual reality) needs to be further explored. A broader definition of recovery is recommended, including behavioural, physical, and psychological criteria. Predictors and mechanisms of changes have not been studied enough: quantitative and qualitative studies are needed to promote more tailored and individualized psychotherapy interventions.

Rethinking the role of trazodone in the different depressive dimensions

INTRODUCTION

The efficacy of trazodone for several psychopathologic dimensions of depression has been shown in the literature. Trazodone has been widely used in some clinical contexts (e.g. for insomnia and depression in the elderly). However, the role of trazodone in several aspects of depression is not well known.

AREA COVERED

Eight experts from academic and medical centers across Italy met to identify the difficulties and barriers faced in daily clinical practice in the assessment and management of major depressive disorder and how the use of trazodone could address some unmet needs. The objective of the expert meetings and the present document was to increase knowledge of particular areas of treatment with trazodone.

EXPERT OPINION

Evidence of the role of trazodone in patients affected by major depressive disorder with anxiety symptoms, insomnia, agitation, cognitive deficits, alcohol use disorders, physical comorbidities, and suicide risk has been identified, showing the effectiveness of trazodone in different presentations of major depressive disorder. The main characteristics of patients with depression for whom trazodone seems to be most effective have been identified, providing clinicians with information on possible uses of this drug in such population of patients.

A Descriptive Diagnosis or a Causal Explanation? Accuracy of Depictions of Depression on Authoritative Health Organization Websites

INTRODUCTION

Psychiatric diagnoses are descriptive in nature, but the lay public commonly misconceives them as causal explanations. It is not known whether this logical error, a form of circular reasoning, can sometimes be mistakenly reinforced by health authorities themselves. In this study, we investigated the prevalence of misleading causal descriptions of depression in the information provided by authoritative mental health organizations on widely accessed internet sites.

METHODS

We searched for popular websites managed by leading mental health organizations and conducted a content analysis to evaluate whether they presented depression accurately as a description of symptoms, or inaccurately as a causal explanation.

RESULTS

Most websites used language that inaccurately described depression as a causal explanation to depressive symptoms.

CONCLUSION

Leading professional medical and psychiatric organizations commonly confound depression, a descriptive diagnostic label, with a causal explanation on their most prominently accessed informational websites. We argue that the scientifically inaccurate causal language in depictions of psychiatric diagnoses is potentially harmful because it leads the public to misunderstand the nature of mental health problems. Mental health authorities providing psychoeducation should clearly state that psychiatric diagnoses are purely descriptive to avoid misleading the public.

Pathways between Child Maltreatment, Psychological Symptoms, and Life Satisfaction: A Network Analysis in Adolescent Inpatients

Child maltreatment is a risk factor for mental disorders and decreased life satisfaction during adolescence. We investigated whether child maltreatment would link to life satisfaction both directly and through psychological symptoms, whether these relations would change from admission to discharge after treatment, and which types of maltreatment, symptoms and facets of life satisfaction would be most influential in adolescent inpatients with internalizing mental disorders. N = 896 adolescent receiving inpatient psychotherapeutic treatment completed questionnaires on child maltreatment experiences, current psychopathology and subjective life satisfaction at admission and discharge (n = 765). Main diagnoses were affective (n = 322), eating (n = 447), obsessive-compulsive (n = 70) and anxiety disorders (n = 57). Network models of child maltreatment, psychopathology and life satisfaction nodes were estimated at admission and discharge and compared using network comparison tests. Potential causal shortest pathways were investigated using directed acyclic graphs.Network models were stable with no significant differences between admission and discharge. Strongest nodes of each cluster were "emotional abuse" (child maltreatment), "worthlessness", "thinking about dying" and "feeling lonely" (psychopathology) and "satisfied with life" (life satisfaction) at both admission and discharge. Emotional neglect showed direct connections to life satisfaction, indicating its relevance for therapeutic interventions. At both admission and discharge, "sexual abuse" indirectly predicted lower life satisfaction through psychological symptoms. In conclusion, child maltreatment is directly and indirectly connected to life satisfaction in adolescents with mental disorders. Emotional abuse and neglect were especially important in linking child maltreatment to life satisfaction and psychopathology.

Towards precision in the diagnostic profiling of patients: leveraging symptom dynamics as a clinical characterisation dimension in the assessment of major depressive disorder

BACKGROUND

International guidelines present overall symptom severity as the key dimension for clinical characterisation of major depressive disorder (MDD). However, differences may reside within severity levels related to how symptoms interact in an individual patient, called symptom dynamics.

AIMS

To investigate these individual differences by estimating the proportion of patients that display differences in their symptom dynamics while sharing the same overall symptom severity.

METHOD

Participants with MDD (n = 73; mean age 34.6 years, s.d. = 13.1; 56.2% female) rated their baseline symptom severity using the Inventory for Depressive Symptomatology Self-Report (IDS-SR). Momentary indicators for depressive symptoms were then collected through ecological momentary assessments five times per day for 28 days; 8395 observations were conducted (average per person: 115; s.d. = 16.8). Each participant's symptom dynamics were estimated using person-specific dynamic network models. Individual differences in these symptom relationship patterns in groups of participants sharing the same symptom severity levels were estimated using individual network invariance tests. Subsequently, the overall proportion of participants that displayed differential symptom dynamics while sharing the same symptom severity was calculated. A supplementary simulation study was conducted to investigate the accuracy of our methodology against false-positive results.

RESULTS

Differential symptom dynamics were identified across 63.0% (95% bootstrapped CI 41.0-82.1) of participants within the same severity group. The average false detection of individual differences was 2.2%.

CONCLUSIONS

The majority of participants within the same depressive symptom severity group displayed differential symptom dynamics. Examining symptom dynamics provides information about person-specific psychopathological expression beyond severity levels by revealing how symptoms aggravate each other over time. These results suggest that symptom dynamics may be a promising new dimension for clinical characterisation, warranting replication in independent samples. To inform personalised treatment planning, a next step concerns linking different symptom relationship patterns to treatment response and clinical course, including patterns related to spontaneous recovery and forms of disorder progression.

Psychological Treatment of Exhaustion Due to Persistent Non-Traumatic Stress: A Scoping Review

BACKGROUND

Exhaustion due to persistent non-traumatic stress (ENTS) is a significant health problem with substantial personal, social, and economic impact. While there are increasing studies of ENTS, there is no international agreement on how it should be diagnosed and treated. This scoping review aimed to map definitions, diagnoses, treatments, outcome measures, and outcomes in psychological treatment studies of ENTS. A further aim was to assess the quality of the treatments and map what change processes are described within ENTS interventions.

METHODS

A PRISMA-guided scoping review of psychological treatment studies delivered in a clinical setting for ENTS was conducted using the databases of PubMed, PsycINFO, and CINAHL.

RESULTS

Of the 60 studies included, the majority (87%) stemmed from Europe. The most recurrent term for ENTS was burnout, and the diagnosis most often utilized was exhaustion disorder. Several treatments were reported, the most frequent being cognitive behavioral therapy (CBT) (68%). Statistically significant outcomes relevant to ENTS were reported in 65% (n = 39) of the studies, with effect sizes between 0.13 and 1.80. In addition, 28% of the treatments were rated as high quality. The most frequent change processes described were dysfunctional sleep, avoidance, behavioral activation, irrational thoughts and beliefs, worry, perceived competence/positive management, psychological flexibility, and recuperation.

CONCLUSIONS

While several treatments based on CBT show promising results for ENTS, there do not seem to be any uniformly established methods, theoretical models, or change processes. Instead of adopting a monocausal, syndromal, and potentially bio-reductionist perspective on ENTS, a process-based approach to treatment is encouraged.

Differentiation between atypical anorexia nervosa and anorexia nervosa using machine learning

OBJECTIVE

Body mass index (BMI) is the primary criterion differentiating anorexia nervosa (AN) and atypical anorexia nervosa despite prior literature indicating few differences between disorders. Machine learning (ML) classification provides us an efficient means of accurately distinguishing between two meaningful classes given any number of features. The aim of the present study was to determine if ML algorithms can accurately distinguish AN and atypical AN given an ensemble of features excluding BMI, and if not, if the inclusion of BMI enables ML to accurately classify between the two.

METHODS

Using an aggregate sample from seven studies consisting of individuals with AN and atypical AN who completed baseline questionnaires (N = 448), we used logistic regression, decision tree, and random forest ML classification models each trained on two datasets, one containing demographic, eating disorder, and comorbid features without BMI, and one retaining all features and BMI.

RESULTS

Model performance for all algorithms trained with BMI as a feature was deemed acceptable (mean accuracy = 74.98%, mean area under the receiving operating characteristics curve [AUC] = 74.75%), whereas model performance diminished without BMI (mean accuracy = 59.37%, mean AUC = 59.98%).

DISCUSSION

Model performance was acceptable, but not strong, if BMI was included as a feature; no other features meaningfully improved classification. When BMI was excluded, ML algorithms performed poorly at classifying cases of AN and atypical AN when considering other demographic and clinical characteristics. Results suggest a reconceptualization of atypical AN should be considered.

PUBLIC SIGNIFICANCE

There is a growing debate about the differences between anorexia nervosa and atypical anorexia nervosa as their diagnostic differentiation relies on BMI despite being similar otherwise. We aimed to see if machine learning could distinguish between the two disorders and found accurate classification only if BMI was used as a feature. This finding calls into question the need to differentiate between the two disorders.

Biomarker-Guided Tailored Therapy in Major Depression

This chapter provides a comprehensive examination of a broad range of biomarkers used for the diagnosis and prediction of treatment outcomes in major depressive disorder (MDD). Genetic, epigenetic, serum, cerebrospinal fluid (CSF), and neuroimaging biomarkers are analyzed in depth, as well as the integration of new technologies such as digital phenotyping and machine learning. The intricate interplay between biological and psychological elements is emphasized as essential for tailoring MDD management strategies. In addition, the evolving link between psychotherapy and biomarkers is explored to uncover potential associations that shed light on treatment response. This analysis underscores the importance of individualized approaches in the treatment of MDD that integrate advanced biological insights into clinical practice to improve patient outcomes.

History of learning disorders is associated with worse cognitive and functional outcomes in schizophrenia: results from the multicentric FACE-SZ cross-sectional dataset

Schizophrenia is associated with early neurodevelopmental disorders, including most frequently learning disorders (LD), among them dyslexia and dyspraxia. Despite the demonstrated links between schizophrenia and LD, specific clinical patterns of the schizophrenia with a history of LD subgroup remain unknown. The aim of the present study was to investigate cognitive impairment, symptoms and functional outcome associated with a history of LD in a large cross-sectional, multicentric, sample of schizophrenia subjects. 492 community-dwelling subjects with schizophrenia (75.6% male, mean age 30.8 years) were consecutively included in the network of the FondaMental Expert Centers for Schizophrenia in France and received a thorough clinical assessment. The 51 (10.4%) subjects identified with a history of LD had significantly impaired general cognitive ability (Wechsler Adult Intelligence Scale Full Scale Total IQ: Cohen's d = 0.50, p = 0.001), processing speed (d = 0.19), verbal comprehension (d = 0.29), working memory (d = 0.31), cognitive inhibition and flexibility (d = 0.26), central executive functioning (d = 0.26), phonemic verbal fluency (d = 0.22) and premorbid intellectual ability (d = 0.48), as well as with a worse functional outcome (Global Assessment of Functioning, d = 0.21), independently of age, sex, education level, symptoms, treatments, and addiction comorbidities. These results indicate that a history of LD is associated with later cognitive impairment and functional outcome in schizophrenia. This suggests that history of LD is a relevant clinical marker to discriminate subgroups of patients with schizophrenia with different profiles in a precision psychiatry framework.

Conceptualizing a less paranoid schizophrenia

Schizophrenia stands as one of the most studied and storied disorders in the history of clinical psychology; however, it remains a nexus of conflicting and competing conceptualizations. Patients endure great stigma, poor treatment outcomes, and condemnatory prognosis. Current conceptualizations suffer from unstable categorical borders, heterogeneity in presentation, outcome and etiology, and holes in etiological models. Taken in aggregate, research and clinical experience indicate that the class of psychopathologies oriented toward schizophrenia are best understood as spectra of phenomenological, cognitive, and behavioral modalities. These apparently taxonomic expressions are rooted in normal human personality traits as described in both psychodynamic and Five Factor personality models, and more accurately represent explicable distress reactions to biopsychosocial stress and trauma. Current categorical approaches are internally hampered by axiomatic bias and systemic inertia rooted in the foundational history of psychological inquiry; however, when such axioms are schematically decentralized, convergent cross-disciplinary evidence outlines a more robust explanatory construct. By reconceptualizing these disorders under a dimensional and cybernetic model, the aforementioned issues of instability and inaccuracy may be resolved, while simultaneously opening avenues for both early detection and intervention, as well as for more targeted and effective treatment approaches.

Enhancing the effectiveness of CBT for patients with unipolar depression by integrating digital interventions into treatment: A pilot randomized controlled trial

OBJECTIVE

Blended cognitive behavioral therapy (bCBT) combines face-to-face therapy with digital elements, such as digital health apps. This pilot study aimed to explore the effectiveness and safety of a novel bCBT application for treating unipolar depression in adults combined with cognitive behavioral therapy (CBT) compared to CBT alone in routine care.

METHODS

Patients (N = 82) were randomly assigned to bCBT (n = 42) or CBT (n = 40) over 12 weeks. bCBT consisted of weekly CBT sessions accompanied by the elona therapy depression module (a bCBT application for unipolar depression) for use between sessions. Standard CBT consisted of weekly CBT sessions. Outcomes (6,12 weeks) were analyzed with linear mixed models.

RESULTS

Improvements in depressive symptoms (BDI-II, PHQ-9) were descriptively larger for the bCBT group. Yet, this difference did not reach statistical significance. bCBT was superior to standard CBT in secondary outcome measures of psychological health (d = .50) and generalized anxiety symptoms (d = -.45). In other secondary outcomes (BAI, PSWQ, GSE, WHOQOL-BREF), improvements were descriptively larger for bCBT compared to CBT.

CONCLUSION

This pilot study provided preliminary evidence that bCBT might be advantageous in comparison to CBT alone in the treatment of depression, but larger RCTs of the bCBT application are needed.

Stratification of adolescents across mental phenomena emphasizes the importance of transdiagnostic distress: a replication in two general population cohorts

Characterizing patterns of mental phenomena in epidemiological studies of adolescents can provide insight into the latent organization of psychiatric disorders. This avoids the biases of chronicity and selection inherent in clinical samples, guides models of shared aetiology within psychiatric disorders and informs the development and implementation of interventions. We applied Gaussian mixture modelling to measures of mental phenomena from two general population cohorts: the Avon Longitudinal Study of Parents and Children (ALSPAC, n = 3018) and the Neuroscience in Psychiatry Network (NSPN, n = 2023). We defined classes according to their patterns of both positive (e.g. wellbeing and self-esteem) and negative (e.g. depression, anxiety, and psychotic experiences) phenomena. Subsequently, we characterized classes by considering the distribution of diagnoses and sex split across classes. Four well-separated classes were identified within each cohort. Classes primarily differed by overall severity of transdiagnostic distress rather than particular patterns of phenomena akin to diagnoses. Further, as overall severity of distress increased, so did within-class variability, the proportion of individuals with operational psychiatric diagnoses. These results suggest that classes of mental phenomena in the general population of adolescents may not be the same as those found in clinical samples. Classes differentiated only by overall severity support the existence of a general, transdiagnostic mental distress factor and have important implications for intervention.

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

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

Learning the "Science of the Art of Prescribing": From Evidence-based Algorithms to Individualized Medicine in Psychiatric Care

The purpose of this review is to highlight the limitations of the traditional diagnosis/evidence-based symptom reduction paradigm and advocate for an individualized medicine approach that incorporates psychological and relational aspects of prescribing in addition to the objective patient presentation. Potential barriers, challenges, and proposed future directions for improving education in psychological and relational aspects of prescribing are discussed. Psychological aspects of prescribing, as recently spelled out in the field of psychodynamic psychopharmacology, are generally acknowledged as important, but they do not have a well-defined position in contemporary residency training throughout North America. While residents receive in-depth exposure to diverse aspects of what to prescribe in their psychopharmacological training, and they work with patients' subjective and relational meaning and the quality of the therapeutic alliance in their psychotherapy rotations, an integrated approach to how to prescribe is generally lacking. Despite many legitimate challenges, the authors suggest that teaching an integrated approach that incorporates objective, subjective, and relational factors in the provision of psychopharmacology and utilizing evidence-based principles of individualized care should be prioritized in both residency training and the provision of psychiatric treatment as a whole.

What kind of science for dual diagnosis? A pragmatic examination of the enactive approach to psychiatry

The recommended treatment for dual diagnosis - the co-occurrence of substance use and another mental disorder - requires seamless integration of the involved disciplines and services. However, no integrative framework exists for communicating about dual diagnosis cases across disciplinary or sectoral boundaries. We examine if Enactive Psychiatry may bridge this theoretical gap. We evaluate the enactive approach through a two-step pragmatic lens: Firstly, by taking a historical perspective to describe more accurately how the theoretical gap within the field of dual diagnosis initially developed. Secondly, by applying the Enactive Psychiatry approach to data from a longitudinal study on the trajectory of cannabis use in psychosis disorders. By applying the theory rather than simply presenting it, we position ourselves better to evaluate whether it may assist the purpose of achieving a more expedient pragmatic “grip” on the field of dual diagnosis. In our discussion, we suggest that this may very well be the case. Finally, we consider the enactive approach as one of a small handful of new theories of mental disorders that draw on systems thinking and ecological psychology, and discuss whether they have the potential for a wider progressive problemshift within psychiatry. The case in favor of such potential, we argue, is less strong unless the role of complexity, similar to that seen within the dual diagnosis field, may be demonstrated for other fields of clinical practice.

What can we learn about the psychiatric diagnostic categories by analysing patients' lived experiences with Machine-Learning?

BACKGROUND

To deliver appropriate mental healthcare interventions and support, it is imperative to be able to distinguish one person from the other. The current classification of mental illness (e.g., DSM) is unable to do that well, indicating the problem of diagnostic heterogeneity between disorders (i.e., the disorder categories have many common symptoms). As a result, the same person might be diagnosed with two different disorders by two independent clinicians. We argue that this problem might have resulted because these disorders were created by a group of humans (APA taskforce members) who relied on more intuition and consensus than data. Literature suggests that human-led decisions are prone to biases, group-thinking, and other factors (such as financial conflict of interest) that can enormously influence creating diagnostic and treatment guidelines. Therefore, in this study, we inquire that if we prevent such human intervention (and thereby their associated biases) and use Artificial Intelligence (A.I.) to form those disorder structures from the data (patient-reported symptoms) directly, then can we come up with homogenous clusters or categories (representing disorders/syndromes: a group of co-occurring symptoms) that are adequately distinguishable from each other for them to be clinically useful. Additionally, we inquired how these A.I.-created categories differ (or are similar) from human-created categories. Finally, to the best of our knowledge, this is the first study, that demonstrated how to use narrative qualitative data from patients with psychopathology and group their experiences using an A.I. Therefore, the current study also attempts to serve as a proof-of-concept.

METHOD

We used secondary data scraped from online communities and consisting of 10,933 patients' narratives about their lived experiences. These patients were diagnosed with one or more DSM diagnoses for mental illness. Using Natural Language Processing techniques, we converted the text data into a numeric form. We then used an Unsupervised Machine Learning algorithm called K-Means Clustering to group/cluster the symptoms.  RESULTS: Using the data mining approach, the A.I. found four categories/clusters formed from the data. We presented ten symptoms or experiences under each cluster to demonstrate the practicality of application and understanding. We also identified the transdiagnostic factors and symptoms that were unique to each of these four clusters. We explored the extent of similarities between these clusters and studied the difference in data density in them. Finally, we reported the silhouette score of + 0.046, indicating that the clusters are poorly distinguishable from each other (i.e., they have high overlapping symptoms).

DISCUSSION

We infer that whether humans attempt to categorise mental illnesses or an A.I., the result is that the categories of mental disorders will not be unique enough to be able to distinguish one service seeker from another. Therefore, the categorical approach of diagnosing mental disorders can be argued to fall short of its purpose. We need to search for a classification system beyond the categorical approaches even if there are secondary merits (such as ease of communication and black-and-white (binary) decision making). However, using our A.I. based data mining approach had several meritorious findings. For example, we found that some symptoms are more exclusive or unique to one cluster. In contrast, others are shared by most other clusters (i.e., identification of transdiagnostic experiences). Such differences are interesting objects of inquiry for future studies. For example, in clear contrast to the traditional diagnostic systems, while some experiences, such as auditory hallucinations, are present in all four clusters, others, such as trouble with eating, are exclusive to one cluster (representing a syndrome: a group of co-occurring symptoms). We argue that trans-diagnostic conditions (e.g., auditory hallucinations) might be prime targets for symptom-level interventions. For syndrome-level grouping and intervention, however, we argue that exclusive symptoms are the main targets.

CONCLUSION

Categorical approach to mental disorders is not a way forward because the categories are not unique enough and have several shared symptoms. We argue that the same symptoms can be present in more than one syndrome, although dimensionally different. However, we need additional studies to test this hypothesis. Future directions and implications were discussed.

We are not ready to abandon the current schizophrenia construct, but should be prepared to do so

The current schizophrenia construct as delineated in the latest editions of the DSM and the ICD has some strengths, but also many weaknesses. It improved the reliability of the diagnosis, made communication among clinicians, users and families less ambiguous, is useful for education and training, and for reimbursement and insurance purposes. However, many serious weaknesses should be considered. The term "Schizophrenia" does not recognize the heterogeneity of the disorder and might nourish the belief that schizophrenia represents a unitary disease. In addition, there is no agreement on the existence and nature of a "core aspect" of the disorder. Stable dimensions, in particular negative symptoms and cognitive impairment, which are key determinants of functioning, are not de facto regarded as core aspects. Finally, the construct is associated to the notion of a poor outcome, to a high level of stigma and has acquired a derogatory connotation. We are not ready but should be prepared to abandon the current schizophrenia construct. Clinicians and researchers should be encouraged to complement the ICD/DSM diagnosis with an in-depth characterization of the individual clinical picture, along with other variables, such as family history, comorbidities, vulnerability factors and personal trajectory. The "Primary Psychoses" construct, together with improved cross-sectional and longitudinal phenotypes from representative population and patient cohorts, and the availability of artificial intelligence methods, could lead to a new and more precise taxonomy of psychotic disorders, and increase the probability of identifying meaningful biomarkers to improve prevention, diagnosis, prognosis, and treatment for people suffering from psychotic disorders.

Transdiagnostic symptom subtypes across autism spectrum disorders and attention deficit hyperactivity disorder: validated by measures of neurocognition and structural connectivity

BACKGROUNDS

Autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD) are neurodevelopmental disorders that exhibit within-disorder heterogeneity and cross-disorder phenotypic overlap, thus suggesting that the current disease categories may not fully represent the etiologic essence of the disorders, especially for highly comorbid neurodevelopmental disorders. In this study, we explored the subtypes of a combined sample of ASD and ADHD by integrating measurements of behavior, cognition and brain imaging.

METHODS

A total of 164 participants, including 65 with ASD, 47 with ADHD, and 52 controls, were recruited. Unsupervised machine learning with an agglomerative hierarchical clustering algorithm was used to identify transdiagnostic symptom clusters. Neurocognition and brain structural connectivity measurements were used to assess symptom clusters. Mediation analysis was used to explore the relationship between transdiagnostic symptoms, neurocognition and brain structural connectivity.

RESULTS

We identified three symptom clusters that did not fall within the diagnostic boundaries of DSM. External measurements from neurocognition and neuroimaging domains supported distinct profiles, including fine motor function, verbal fluency, and structural connectivity in the corpus callosum between these symptom clusters, highlighting possible biomarkers for ASD and ADHD. Additionally, fine motor function was shown to mediate the relationship between the corpus callosum and perseveration symptoms.

CONCLUSIONS

In this transdiagnostic study on ASD and ADHD, we identified three subtypes showing meaningful associations between symptoms, neurocognition and brain white matter structural connectivity. The fine motor function and structural connectivity of corpus callosum might be used as biomarkers for neurodevelopmental disorders with social skill symptoms. The results of this study highlighted the importance of precise phenotyping and further supported the effects of fine motor intervention on ASD and ADHD.

Adolescent depression beyond DSM definition: a network analysis

Calls for refining the understanding of depression beyond diagnostic criteria have been growing in recent years. We examined the prevalence and relevance of DSM and non-DSM depressive symptoms in two Brazilian school-based adolescent samples with two commonly used scales, the Patient Health Questionnaire (PHQ-A) and the Mood and Feelings Questionnaire (MFQ). We analyzed cross-sectional data from two similarly recruited samples of adolescents aged 14-16 years, as part of the Identifying Depression Early in Adolescence (IDEA) study in Brazil. We assessed dimensional depressive symptomatology using the PHQ-A in the first sample (n = 7720) and the MFQ in the second sample (n = 1070). We conducted network analyses to study symptom structure and centrality estimates of the two scales. Additionally, we compared centrality of items included (e.g., low mood, anhedonia) and not included in the DSM (e.g., low self-esteem, loneliness) in the MFQ. Sad mood and worthlessness items were the most central items in the network structure of the PHQ-A. In the MFQ sample, self-hatred and loneliness, two non-DSM features, were the most central items and DSM and non-DSM items in this scale formed a highly interconnected network of symptoms. Furthermore, analysis of the MFQ sample revealed DSM items not to be more frequent, severe or interconnected than non-DSM items, but rather part of a larger network of symptoms. A focus on symptoms might advance research on adolescent depression by enhancing our understanding of the disorder.

Depressive subfactors and cognitive function in midlife

BACKGROUND

This study aimed to evaluate the heterogeneous association of depressive subtypes with cognitive function, according to age and sex.

METHODS

This cross-sectional study utilized the baseline data from the Cardiovascular and Metabolic Disease Etiology Research Center cohort and included 5271 midlife participants. For identifying depressive subtypes of the Beck Depression Inventory Ⅱ items, factor analyses were utilized and yielded two factors -melancholic- and somatic-depressive subtypes. The information of Mini-Mental State Examination was used for screening cognitive function. The association between depressive subtypes and cognitive function was analysed using multiple regression after adjusting for all covariates.

RESULTS

We observed heterogeneous association between depressive subtypes and cognitive dysfunction in midlife participants. The results of sex- and age- stratified analyses indicated that the somatic subtype was associated with dysfunction in cognitive ability. Among women, especially those aged over 60 years, MMSE scores decreased as the somatic depression scores increased. These results might suggest that the somatic subtype, rather than the melancholic subtype, has a greater association with cognitive assessment in a general midlife population, particularly older women.

LIMITATIONS

Although a confirmatory factor analysis was performed, depressive subtypes need validation and reliability tests.

CONCLUSIONS

Given this heterogeneity, characterisation of depressive subtypes according to sex and age may improve our understanding of how each depressive symptom is associated differently with cognitive dysfunction in midlife.

Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning

Psychiatric disorders show heterogeneous symptoms and trajectories, with current nosology not accurately reflecting their molecular etiology and the variability and symptomatic overlap within and between diagnostic classes. This heterogeneity impedes timely and targeted treatment. Our study aimed to identify psychiatric patient clusters that share clinical and genetic features and may profit from similar therapies. We used high-dimensional data clustering on deep clinical data to identify transdiagnostic groups in a discovery sample (N = 1250) of healthy controls and patients diagnosed with depression, bipolar disorder, schizophrenia, schizoaffective disorder, and other psychiatric disorders. We observed five diagnostically mixed clusters and ordered them based on severity. The least impaired cluster 0, containing most healthy controls, showed general well-being. Clusters 1-3 differed predominantly regarding levels of maltreatment, depression, daily functioning, and parental bonding. Cluster 4 contained most patients diagnosed with psychotic disorders and exhibited the highest severity in many dimensions, including medication load. Depressed patients were present in all clusters, indicating that we captured different disease stages or subtypes. We replicated all but the smallest cluster 1 in an independent sample (N = 622). Next, we analyzed genetic differences between clusters using polygenic scores (PGS) and the psychiatric family history. These genetic variables differed mainly between clusters 0 and 4 (prediction area under the receiver operating characteristic curve (AUC) = 81%; significant PGS: cross-disorder psychiatric risk, schizophrenia, and educational attainment). Our results confirm that psychiatric disorders consist of heterogeneous subtypes sharing molecular factors and symptoms. The identification of transdiagnostic clusters advances our understanding of the heterogeneity of psychiatric disorders and may support the development of personalized treatments.

Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing

Causal interactions between specific psychiatric symptoms could contribute to the heterogenous clinical trajectories observed in early psychopathology. Current diagnostic approaches merge clinical manifestations that co-occur across subjects and could significantly hinder our understanding of clinical pathways connecting individual symptoms. Network analysis techniques have emerged as alternative approaches that could help shed light on the complex dynamics of early psychopathology. The present study attempts to address the two main limitations that have in our opinion hindered the application of network approaches in the clinical setting. Firstly, we show that a multi-layer network analysis approach, can move beyond a static view of psychopathology, by providing an intuitive characterization of the role of specific symptoms in contributing to clinical trajectories over time. Secondly, we show that a Graph-Signal-Processing approach, can exploit knowledge of longitudinal interactions between symptoms, to predict clinical trajectories at the level of the individual. We test our approaches in two independent samples of individuals with genetic and clinical vulnerability for developing psychosis. Novel network approaches can allow to embrace the dynamic complexity of early psychopathology and help pave the way towards a more a personalized approach to clinical care.

The role of RDoC in future classification of mental disorders

The Research Domain Criteria (RDoC) project constitutes a translational framework for psychopathology research, initiated by the National Institute of Mental Health in an attempt to provide new avenues for research to circumvent problems emerging from the use of symptom-based diagnostic categories in diagnosing disorders. The RDoC alternative is a focus on psychopathology based on dimensions simultaneously defined by observable behavior (including quantitative measures of cognitive or affective behavior) and neurobiological measures. Key features of the RDoC framework include an emphasis on functional dimensions that range from normal to abnormal, integration of multiple measures in study designs (which can foster computational approaches), and high priority on studies of neurodevelopment and environmental influences (and their interaction) that can contribute to advances in understanding the etiology of disorders throughout the lifespan. The paper highlights key implications for ways in which RDoC can contribute to future ideas about classification, as well as some of the considerations involved in translating basic behavioral and neuroscience data to psychopathology.


Preventive psychiatry: a blueprint for improving the mental health of young people

Preventive approaches have latterly gained traction for improving mental health in young people. In this paper, we first appraise the conceptual foundations of preventive psychiatry, encompassing the public health, Gordon's, US Institute of Medicine, World Health Organization, and good mental health frameworks, and neurodevelopmentally-sensitive clinical staging models. We then review the evidence supporting primary prevention of psychotic, bipolar and common mental disorders and promotion of good mental health as potential transformative strategies to reduce the incidence of these disorders in young people. Within indicated approaches, the clinical high-risk for psychosis paradigm has received the most empirical validation, while clinical high-risk states for bipolar and common mental disorders are increasingly becoming a focus of attention. Selective approaches have mostly targeted familial vulnerability and non-genetic risk exposures. Selective screening and psychological/psychoeducational interventions in vulnerable subgroups may improve anxiety/depressive symptoms, but their efficacy in reducing the incidence of psychotic/bipolar/common mental disorders is unproven. Selective physical exercise may reduce the incidence of anxiety disorders. Universal psychological/psychoeducational interventions may improve anxiety symptoms but not prevent depressive/anxiety disorders, while universal physical exercise may reduce the incidence of anxiety disorders. Universal public health approaches targeting school climate or social determinants (demographic, economic, neighbourhood, environmental, social/cultural) of mental disorders hold the greatest potential for reducing the risk profile of the population as a whole. The approach to promotion of good mental health is currently fragmented. We leverage the knowledge gained from the review to develop a blueprint for future research and practice of preventive psychiatry in young people: integrating universal and targeted frameworks; advancing multivariable, transdiagnostic, multi-endpoint epidemiological knowledge; synergically preventing common and infrequent mental disorders; preventing physical and mental health burden together; implementing stratified/personalized prognosis; establishing evidence-based preventive interventions; developing an ethical framework, improving prevention through education/training; consolidating the cost-effectiveness of preventive psychiatry; and decreasing inequalities. These goals can only be achieved through an urgent individual, societal, and global level response, which promotes a vigorous collaboration across scientific, health care, societal and governmental sectors for implementing preventive psychiatry, as much is at stake for young people with or at risk for emerging mental disorders.

Is social support pre-treatment associated with prognosis for adults with depression in primary care?

OBJECTIVE

Depressed patients rate social support as important for prognosis, but evidence for a prognostic effect is lacking. We aimed to test the association between social support and prognosis independent of treatment type, and the severity of depression, and other clinical features indicating a more severe illness.

METHODS

Individual patient data were collated from all six eligible RCTs (n = 2858) of adults seeking treatment for depression in primary care. Participants were randomized to any treatment and completed the same baseline assessment of social support and clinical severity factors. Two-stage random effects meta-analyses were conducted.

RESULTS

Social support was associated with prognosis independent of randomized treatment but effects were smaller when adjusting for depressive symptoms and durations of depression and anxiety, history of antidepressant treatment, and comorbid panic disorder: percentage decrease in depressive symptoms at 3-4 months per z-score increase in social support = -4.14(95%CI: -6.91 to -1.29). Those with a severe lack of social support had considerably worse prognoses than those with no lack of social support: increase in depressive symptoms at 3-4 months = 14.64%(4.25% to 26.06%).

CONCLUSIONS

Overall, large differences in social support pre-treatment were associated with differences in prognostic outcomes. Adding the Social Support scale to clinical assessments may be informative, but after adjusting for routinely assessed clinical prognostic factors the differences in prognosis are unlikely to be of a clinically important magnitude. Future studies might investigate more intensive treatments and more regular clinical reviews to mitigate risks of poor prognosis for those reporting a severe lack of social support.

Psychiatric symptoms and syndromes transcending diagnostic boundaries in Indian multiplex families: The cohort of ADBS study

Syndromes of schizophrenia, bipolar disorder, obsessive-compulsive disorder, substance use disorders and Alzheimer's dementia are highly heritable. About 10-20% of subjects have another affected first degree relative (FDR), and thus represent a 'greater' genetic susceptibility. We screened 3583 families to identify 481 families with multiple affected members, assessed 1406 individuals in person, and collected information systematically about other relatives. Within the selected families, a third of all FDRs were affected with serious mental illness. Although similar diagnoses aggregated within families, 62% of the families also had members with other syndromes. Moreover, 15% of affected individuals met criteria for co-occurrence of two or more syndromes, across their lifetime. Using dimensional assessments, we detected a range of symptom clusters in both affected and unaffected individuals, and across diagnostic categories. Our findings suggest that in multiplex families, there is considerable heterogeneity of clinical syndromes, as well as sub-threshold symptoms. These families would help provide an opportunity for further research using both genetic analyses and biomarkers.

Clustering of Multiple Psychiatric Disorders Using Functional Connectivity in the Data-Driven Brain Subnetwork

Recently, the dimensional approach has attracted much attention, bringing a paradigm shift to a continuum of understanding of different psychiatric disorders. In line with this new paradigm, we examined whether there was common functional connectivity related to various psychiatric disorders in an unsupervised manner without explicitly using diagnostic label information. To this end, we uniquely applied a newly developed network-based multiple clustering method to resting-state functional connectivity data, which allowed us to identify pairs of relevant brain subnetworks and subject cluster solutions accordingly. Thus, we identified four subject clusters, which were characterized as major depressive disorder (MDD), young healthy control (young HC), schizophrenia (SCZ)/bipolar disorder (BD), and autism spectrum disorder (ASD), respectively, with the relevant brain subnetwork represented by the cerebellum-thalamus-pallidum-temporal circuit. The clustering results were validated using independent datasets. This study is the first cross-disorder analysis in the framework of unsupervised learning of functional connectivity based on a data-driven brain subnetwork.

Potential Transdiagnostic Lipid Mediators of Inflammatory Activity in Individuals With Serious Mental Illness

Mental disorders are heterogeneous and psychiatric comorbidities are common. Previous studies have suggested a link between inflammation and mental disorders. This link can manifest as increased levels of proinflammatory mediators in circulation and as signs of neuroinflammation. Furthermore, there is strong evidence that individuals suffering from psychiatric disorders have increased risk of developing metabolic comorbidities. Our group has previously shown that, in a cohort of low-functioning individuals with serious mental disorders, there is increased expression of genes associated with the NLRP3 inflammasome, a known sensor of metabolic perturbations, as well as increased levels of IL-1-family cytokines. In the current study, we set out to explore the interplay between disease-specific changes in lipid metabolism and known markers of inflammation. To this end, we performed mass spectrometry-based lipidomic analysis of plasma samples from low-functioning individuals with serious mental disorders (n = 39) and matched healthy controls (n = 39). By identifying non-spurious immune-lipid associations, we derived a partial correlation network of inflammatory markers and molecular lipids. We identified levels of lipids as being altered between individuals with serious mental disorders and controls, showing associations between lipids and inflammatory mediators, e.g., osteopontin and IL-1 receptor antagonist. These results indicate that, in low-functioning individuals with serious mental disorders, changes in specific lipids associate with immune mediators that are known to affect neuroinflammatory diseases.

A Network Analysis of Major Depressive Disorder Symptoms and Age- and Gender-Related Differences in People over 65 in a Madrid Community Sample (Spain)

Major depressive disorder (MDD) is one of the most prevalent conditions among mental disorders in individuals over 65 years. People over 65 who suffer from MDD are often functionally impaired, chronically physically ill, and express cognitive problems. The concordance between a clinician-assessed MDD diagnosis in a primary care setting and MDD assessed with a structured clinical interview in older adults is only approximately 18%. Network analysis may provide an alternative statistical technique to better understand MDD in this population by a dimensional approach to symptomatology. The aim of this study was to carry out a network analysis of major depressive disorder (MDD) in people over 65 years old. A symptom network analysis was conducted according to age and gender in 555 people over 65, using a sample from the MentDis_ICF65+ Study. The results revealed different networks for men and women, and for the age groups 65–74 and 75–84. While depressive mood stood out in women, in men the network was more dispersed with fatigue or loss of energy and sleep disturbances as the main symptoms. In the 65–74 age group, the network was complex; however, in the 75–84 age group, the network was simpler with sleep disturbances as the central symptom. The gaps between the networks indicate the different characteristics of MDD in the elderly, with variations by gender and age, supporting the idea that MDD is a complex dynamic system that has unique characteristics in each person, rather than a prototypical classification with an underlying mental disorder. These unique characteristics can be taken into account in the clinical practice for detection and intervention of MDD.

A process-based approach to psychological diagnosis and treatment:The conceptual and treatment utility of an extended evolutionary meta model

For half a century, the dominant paradigm in psychotherapy research has been to develop syndrome-specific treatment protocols for hypothesized but unproved latent disease entities, as defined by psychiatric nosological systems. While this approach provided a common language for mental health problems, it failed to achieve its ultimate goal of conceptual and treatment utility. Process-based therapy (PBT) offers an alternative approach to understanding and treating psychological problems, and promoting human prosperity. PBT targets empirically established biopsychosocial processes of change that researchers have shown are functionally important to long terms goals and outcomes. By building on concepts of known clinical utility, and organizing them into coherent theoretical models, an idiographic, functional-analytic approach to diagnosis is within our grasp. We argue that a multi-dimensional, multi-level extended evolutionary meta-model (EEMM) provides consilience and a common language for process-based diagnosis. The EEMM applies the evolutionary concepts of context-appropriate variation, selection, and retention to key biopsychosocial dimensions and levels related to human suffering, problems, and positive functioning. The EEMM is a meta-model of diagnostic and intervention approaches that can accommodate any set of evidence-based change processes, regardless of the specific therapy orientation. In a preliminary way, it offers an idiographic, functional analytic, and clinically useful alternative to contemporary psychiatric nosological systems.

Mapping the Research Domain Criteria Social Processes Constructs to the Social Responsiveness Scale

OBJECTIVE

Research Domain Criteria (RDoC) operationalizes a set of basic social dimensions that can be used to deconstruct sources of variation in social impairments across affected individuals, regardless of their diagnostic status. This is a necessary step toward the development of etiologically based and individualized treatments. The main objective of this investigation was to derive estimations of the RDoC social constructs from the Social Responsiveness Scale (SRS-2).

METHOD

Exploratory structural equation modeling and confirmatory factor analysis were conducted using individual SRS-2 items from six distinct databases ( N = 27,953; mean age = 9.55 years, SD = 3.79; 71.7% male participants) spanning normative (33.8%) and atypical (66.2%) development. The following models were estimated: a one-factor model; a three-factor model with separate attachment and affiliation, social communication, and understanding of mental states factors; and a four-factor model where social communication was further split into production of facial and non-facial communication.

RESULTS

The one-factor solution showed poor fit. The three-factor solution had adequate fit (comparative fit index = 0.952, Tucker Lewis Index = 0.937, root mean square error of approximation = 0.054). However, the four-factor solution had superior fit (comparative fit index = 0.973, Tucker Lewis Index = 0.961, root mean square error of approximation = 0.042) and was robust across age, sex, and clinical status.

CONCLUSION

To our knowledge, this is the first study examining estimations of the RDoC social constructs from an existing measure. Reported findings show promise for capturing important RDoC social constructs using the SRS-2 and highlight crucial areas for the development of novel dimensional social processing measures.

The clinical characterization of the adult patient with depression aimed at personalization of management

Depression is widely acknowledged to be a heterogeneous entity, and the need to further characterize the individual patient who has received this diagnosis in order to personalize the management plan has been repeatedly emphasized. However, the research evidence that should guide this personalization is at present fragmentary, and the selection of treatment is usually based on the clinician's and/or the patient's preference and on safety issues, in a trial-and-error fashion, paying little attention to the particular features of the specific case. This may be one of the reasons why the majority of patients with a diagnosis of depression do not achieve remission with the first treatment they receive. The predominant pessimism about the actual feasibility of the personalization of treatment of depression in routine clinical practice has recently been tempered by some secondary analyses of databases from clinical trials, using approaches such as individual patient data meta-analysis and machine learning, which indicate that some variables may indeed contribute to the identification of patients who are likely to respond differently to various antidepressant drugs or to antidepressant medication vs. specific psychotherapies. The need to develop decision support tools guiding the personalization of treatment of depression has been recently reaffirmed, and the point made that these tools should be developed through large observational studies using a comprehensive battery of self-report and clinical measures. The present paper aims to describe systematically the salient domains that should be considered in this effort to personalize depression treatment. For each domain, the available research evidence is summarized, and the relevant assessment instruments are reviewed, with special attention to their suitability for use in routine clinical practice, also in view of their possible inclusion in the above-mentioned comprehensive battery of measures. The main unmet needs that research should address in this area are emphasized. Where the available evidence allows providing the clinician with specific advice that can already be used today to make the management of depression more personalized, this advice is highlighted. Indeed, some sections of the paper, such as those on neurocognition and on physical comorbidities, indicate that the modern management of depression is becoming increasingly complex, with several components other than simply the choice of an antidepressant and/or a psychotherapy, some of which can already be reliably personalized.

Predicting Patients' Readmission: Do Clinicians Outperform a Statistical Model? An Exploratory Study on Clinical Risk Judgment in Mental Health

This study explores whether clinicians or a statistical model can better identify patients at risk of early readmission and investigates variables potentially associated with clinicians' risk judgment. We focus on a total of 142 patients discharged from acute psychiatric wards in the Verona Mental Health Department (Italy). Psychiatrists assessed patients' risk of readmission at 30 and 90 days postdischarge, predicted their postdischarge compliance, and assessed their Global Assessment of Functioning (GAF) score at admission and discharge. Clinicians' judgment outperformed the statistical model, with the difference reaching statistical significance for 30-day readmission. Clinicians' readmission risk judgment, both for 30 and 90 days, was found to be statistically associated with predicted compliance with community treatment and GAF score at discharge. Clinicians' superior performance might be explained by their risk judgment depending on nonmeasurable factors, such as experience and intuition. Patients with a poorer GAF score at discharge and poor assumed compliance were predicted to have a higher risk of readmission.

Diagnosis, Clinical Features, and Therapeutic Implications of Agitated Depression

Agitated "unipolar" depression is a clinical entity characterized by excitement together with depressed mood during the same episode. The clinical picture of agitated "unipolar" depression is characterized by a depressed and anxious mood with inner, psychic agitation, whereas motor agitation may or may not be present. Some investigators have conceptualized this disorder as a mixed affective state, laying on the bipolar disorder spectrum, but controversies still persist. The diagnosis of agitated "unipolar" depression has important prognostic and therapeutic implications, with many clinicians reporting difficulties to adequately diagnose and treat it.

Developmental and symptom profiles in early-onset psychosis

Psychotic disorders in children are more heterogeneous than is captured by categorical diagnoses. In a new cohort of children and adolescents, we evaluated the relationships among age at onset (AAO), clinical symptoms and developmental impairments. Patients with schizophrenia and other "spectrum" psychotic diagnoses (N = 88; AAO 6-17, mean 12.6) were evaluated with diagnostic interviews, a new clinical scale (Lifetime Dimensions of Psychosis Scale-Child and Adolescent), and neuropsychological and medical evaluations. Key findings were replicated in an adult cohort of 2420 cases, including 127 with retrospective AAO<13. Factor and cluster analyses were carried out to identify clinical profiles. Five clinical factors were identified in each cohort: Positive, Bizarre Positive, Negative/Formal Thought Disorder, Depression and Mania. Earlier AAO predicted severity of bizarre positive symptoms in children and of bizarre and other symptoms in adults. Four clinical clusters in the child cohort were characterized by: more severe bizarre positive symptoms (N = 31); negative symptoms (N = 15); premorbid autism spectrum features and developmental delay (N = 12); and depressive symptoms with heterogeneous diagnoses and mild positive/negative symptoms (N = 25). Previous factor-analytic studies of childhood psychosis did not specifically consider bizarre positive symptoms. Here, bizarre positive symptoms emerged as clinical markers of severe, childhood-onset psychosis similar to adult schizophrenia. The four clusters are clinically meaningful and useful for treatment planning and potentially for biological research. Childhood-onset cases are rare and thus difficult to study, but additional, larger cohorts may be useful in dissecting the biological and developmental heterogeneity of psychotic disorders.

The Italian ICD-11 field trial: clinical utility of diagnostic guidelines for schizophrenia and related disorders

Background

The 11th revision of the International Classification of Diseases and Related Disorders (ICD-11) has been released. In order to test the clinical consistency and the clinical utility of the proposed guidelines the World Health Organization (WHO) has carried out the Ecological Implementation Field Studies in various countries. In this paper the results of the Italian field trials on the clinical utility of the ICD-11 diagnostic guideline concerning schizophrenia and related disorders will be presented.

Methods

In Italy, field trials have been carried out at the Department of Psychiatry of the University of Campania “L. Vanvitelli”. All patients showing any psychotic symptom and referring to the outpatient and inpatient units have been recruited. Patients were interviewed by two clinicians with whom they had not had any prior clinical contact. At the end of each interview, clinicians were asked to complete 12 questions about the clinical utility of the diagnostic guidelines as applied to each patient.

Results

Fourteen clinicians and 100 patients have been involved. The ICD-11 clinical guidelines were perceived as easy to use, with an adequate goodness of fit, clear and understandable and with an adequate level of details and specificity to describe the essential features of the diagnoses. Clinicians rated very positively their usefulness in describing the threshold between patient’s disorder and normality. Despite still very positive, the guidelines have been perceived as less useful to select a treatment, to assess patients’ prognosis and to communicate with other mental health professionals.

Conclusions

The 11th revision of the chapter on Mental, Behavioural and Neurodevelopmental Disorders has made substantive changes to the conceptualization of mental disorders which could have impacted on their reliability and clinical utility. Results of the Italian field studies, in line with those reported by the international sample, highlight that ICD-11 has been rated as highly clinically useful by participating clinician, more than the ICD-10. This could be considered a good reason to be optimistic about the implementation of the ICD-11 among global clinicians.

Trial registration The study has been approved by the Ethical Review Board of the University of Campania “L. Vanvitelli” (N. 416, 2016)

Transdiagnostic or Disorder Specific? Indicators of Substance and Behavioral Addictions Nominated by People with Lived Experience

Using a transdiagnostic perspective, the present research examined the prominent indicators of substance (alcohol, cocaine, marijuana, tobacco) and behavioral (gambling, video games, sex, shopping, work, eating) addictions nominated by people with lived experiences. Specifically, we aimed to explore whether the perceived most important indicators nominated were consistent across the 10 addictions or differed based on the specific addiction. Additionally, we explored gender differences in the perceived most important indicators across addictive behaviors. A large online sample of adults recruited from a Canadian province (n = 3503) were asked to describe the most important signs or symptoms of problems with these substances and behaviors. Open-ended responses were analyzed among a subsample of 2603 respondents (n = 1562 in the past year) who disclosed that they had personally experienced a problem with at least one addiction listed above. Content analyses revealed that dependence (e.g., craving, impairments in control) and patterns of use (e.g., frequency) were the most commonly perceived indicators for both substance and behavioral addictions, accounting for over half of all the qualitative responses. Differences were also found between substance and behavioral addictions regarding the proportion of the most important signs nominated. Consistent with the syndrome model of addiction, unique indicators were also found for specific addictive behaviors, with the greatest proportion of unique indicators found for eating. Supplemental analyses found that perceived indicators across addictions were generally gender invariant. Results provide some support for a transdiagnostic conceptualization of substance and behavioral addictions. Implications for the study, prevention, and treatment of addictions are discussed.

Beyond diagnosis in psychiatric practice

In psychiatry, the information conveyed by diagnosis (i.e., the "type" to which the individual patient is reconducted) is in itself insufficient for therapeutic and prognostic purposes. Hence the need for a more detailed characterization of the individual case, with a special focus on the assessment of low-order and high-order psychopathological dimensions, the evaluation of the severity of the clinical picture, the assessment of the stage of development of the disorder, and the exploration of a series of antecedent and concomitant variables. We should start to promote the construction and validation of tools guiding the clinician systematically in this characterization, trying to incorporate in this effort elements of the approaches that are currently presented as "alternative" to the ICD and DSM.

Bimodal distribution of tone-matching deficits indicates discrete pathophysiological entities within the syndrome of schizophrenia

To date, no measures are available that permit differentiation of discrete, clinically distinct subtypes of schizophrenia (SZ) with potential differential underlying pathophysiologies. Over recent years, there has been increasing recognition that SZ is heterogeneously associated with deficits in early auditory processing (EAP), as demonstrated using clinically applicable tasks such as tone-matching task (TMT). Here, we pooled TMT performances across 310 SZ individuals and 219 healthy controls (HC), along with clinical, cognitive, and resting-state functional-connectivity MRI (rsFC-MRI) measures. In addition, TMT was measured in a group of 24 patients at symptomatic clinical high risk (CHR) for SZ and 24 age-matched HC (age range 7-27 years). We provide the first demonstration that the EAP deficits are bimodally distributed across SZ subjects (P < 0.0001 vs. unimodal distribution), with one group showing entirely unimpaired TMT performance (SZ-EAP+), and a second showing an extremely large TMT impairment (SZ-EAP-), relative to both controls (d = 2.1) and SZ-EAP+ patients (d = 3.4). The SZ-EAP- group predominated among samples drawn from inpatient sites, showed higher levels of cognitive symptoms (PANSS), worse social cognition and a differential deficit in neurocognition (MATRICS battery), and reduced functional capacity. rsFC-MRI analyses showed significant reduction in SZ-EAP- relative to controls between subcortical and cortical auditory regions. As opposed to SZ, CHR patients showed intact EAP function. In HC age-matched to CHR, EAP ability was shown to increase across the age range of vulnerability preceding SZ onset. These results indicate that EAP measure segregates between discrete SZ subgroups. As TMT can be readily implemented within routine clinical settings, its use may be critical to account for the heterogeneity of clinical outcomes currently observed across SZ patients, as well as for pre-clinical detection and efficacious treatment selection.

Transdiagnostic psychiatry: a systematic review

The usefulness of current psychiatric classification, which is based on ICD/DSM categorical diagnoses, remains questionable. A promising alternative has been put forward as the "transdiagnostic" approach. This is expected to cut across existing categorical diagnoses and go beyond them, to improve the way we classify and treat mental disorders. This systematic review explores whether self-defining transdiagnostic research meets such high expectations. A multi-step Web of Science literature search was performed according to an a priori protocol, to identify all studies that used the word "transdiagnostic" in their title, up to May 5, 2018. Empirical variables which indexed core characteristics were extracted, complemented by a bibliometric and conceptual analysis. A total of 111 studies were included. Most studies were investigating interventions, followed by cognition and psychological processes, and neuroscientific topics. Their samples ranged from 15 to 91,199 (median 148) participants, with a mean age from 10 to more than 60 (median 33) years. There were several methodological inconsistencies relating to the definition of the gold standard (DSM/ICD diagnoses), of the outcome measures and of the transdiagnostic approach. The quality of the studies was generally low and only a few findings were externally replicated. The majority of studies tested transdiagnostic features cutting across different diagnoses, and only a few tested new classification systems beyond the existing diagnoses. About one fifth of the studies were not transdiagnostic at all, because they investigated symptoms and not disorders, a single disorder, or because there was no diagnostic information. The bibliometric analysis revealed that transdiagnostic research largely restricted its focus to anxiety and depressive disorders. The conceptual analysis showed that transdiagnostic research is grounded more on rediscoveries than on true innovations, and that it is affected by some conceptual biases. To date, transdiagnostic approaches have not delivered a credible paradigm shift that can impact classification and clinical care. Practical "TRANSD"iagnostic recommendations are proposed here to guide future research in this field.

The evidence-based group-level symptom-reduction model as the organizing principle for mental health care: time for change?

The content and organization of mental health care have been heavily influenced by the view that mental difficulties come as diagnosable disorders that can be treated by specialist practitioners who apply evidence-based practice (EBP) guidelines of symptom reduction at the group level. However, the EBP symptom-reduction model is under pressure, as it may be disconnected from what patients need, ignores evidence of the trans-syndromal nature of mental difficulties, overestimates the contribution of the technical aspects of treatment compared to the relational and ritual components of care, and underestimates the lack of EBP group-to-individual generalizability. A growing body of knowledge indicates that mental illnesses are seldom "cured" and are better framed as vulnerabilities. Important gains in well-being can be achieved when individuals learn to live with mental vulnerabilities through a slow process of strengthening resilience in the social and existential domains. In this paper, we examine what a mental health service would look like if the above factors were taken into account. The mental health service of the 21st century may be best conceived of as a small-scale healing community fostering connectedness and strengthening resilience in learning to live with mental vulnerability, complemented by a limited number of regional facilities. Peer support, organized at the level of a recovery college, may form the backbone of the community. Treatments should be aimed at trans-syndromal symptom reduction, tailored to serve the higher-order process of existential recovery and social participation, and applied by professionals who have been trained to collaborate, embrace idiography and maximize effects mediated by therapeutic relationship and the healing effects of ritualized care interactions. Finally, integration with a public mental health system of e-communities providing information, peer and citizen support and a range of user-rated self-management tools may help bridge the gap between the high prevalence of common mental disorder and the relatively low capacity of any mental health service.

Transdiagnostic dimensions of psychosis in the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP)

The validity of the classification of non-affective and affective psychoses as distinct entities has been disputed, but, despite calls for alternative approaches to defining psychosis syndromes, there is a dearth of empirical efforts to identify transdiagnostic phenotypes of psychosis. We aimed to investigate the validity and utility of general and specific symptom dimensions of psychosis cutting across schizophrenia, schizoaffective disorder and bipolar I disorder with psychosis. Multidimensional item-response modeling was conducted on symptom ratings of the Positive and Negative Syndrome Scale, Young Mania Rating Scale, and Montgomery-Åsberg Depression Rating Scale in the multicentre Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) consortium, which included 933 patients with a diagnosis of schizophrenia (N=397), schizoaffective disorder (N=224), or bipolar I disorder with psychosis (N=312). A bifactor model with one general symptom dimension, two distinct dimensions of non-affective and affective psychosis, and five specific symptom dimensions of positive, negative, disorganized, manic and depressive symptoms provided the best model fit. There was further evidence on the utility of symptom dimensions for predicting B-SNIP psychosis biotypes with greater accuracy than categorical DSM diagnoses. General, positive, negative and disorganized symptom dimension scores were higher in African American vs. Caucasian patients. Symptom dimensions accurately classified patients into categorical DSM diagnoses. This study provides evidence on the validity and utility of transdiagnostic symptom dimensions of psychosis that transcend traditional diagnostic boundaries of psychotic disorders. Findings further show promising avenues for research at the interface of dimensional psychopathological phenotypes and basic neurobiological dimensions of psychopathology.

The necessity, validity, and clinical utility of a new diagnostic entity: Acute suicidal affective disturbance

OBJECTIVE

Here we argue for the necessity, validity, and clinical utility of a new diagnostic entity, acute suicidal affective disturbance (ASAD).

METHOD

We expand on the conceptual, clinical, and practical rationale for ASAD, propose its defining features, describe research results to date, and suggest avenues for future research.

RESULTS

There is accruing evidence for the existence of a previously unclassified, rapid-onset mood disturbance that geometrically escalates and regularly results in life-threatening behavior.

CONCLUSIONS

ASAD research may not only improve the field's understanding of suicidal behavior but also enhance clinical effectiveness and save lives.

Biological and Clinical Markers in Panic Disorder

OBJECTIVE

Classifying mental disorders on the basis of objective makers might clarify their aetiology, help in making the diagnosis, identify "at risk" individuals, determine the severity of mental illness, and predict the course of the disorder. This study aims to review biological and clinical markers of panic disorder (PD).

METHODS

A computerized search was carried out in PubMed and Science Direct using the key words: "marker/biomarker/clinical marker/neurobiology/staging" combined using Boolean AND operator with "panic." In addition, the reference lists from existing reviews and from the articles retrieved were inspected. Only English language papers published in peer-reviewed journals were included.

RESULTS

Structural changes in the amygdala, hippocampus, cerebral blood level in the left occipital cortex, serotonin 5-TH and noradrenergic systems activation, aberrant respiratory regulation, hearth rate variability, blood cells and peripheral blood stem cells, hypothalamic-pituitary-adrenal axis dysregulation were identified as potential candidate biomarkers of PD. Staging was identified as clinical marker of PD. According to the staging model, PD is described as follows: prodromal phase (stage 1); acute phase (stage 2); panic attacks (stage 3); chronic phase (stage 4).

CONCLUSION

The clinical utility, sensitivity, specificity, and the predictive value of biomarkers for PD is still questionable. The staging model of PD might be a valid susceptibility, diagnostic, prognostic, and predictive marker of PD. A possible longitudinal model of biological and clinical markers of PD is proposed.

Clinical utility of ICD-11 diagnostic guidelines for high-burden mental disorders: results from mental health settings in 13 countries

In this paper we report the clinical utility of the diagnostic guidelines for ICD-11 mental, behavioural and neurodevelopmental disorders as assessed by 339 clinicians in 1,806 patients in 28 mental health settings in 13 countries. Clinician raters applied the guidelines for schizophrenia and other primary psychotic disorders, mood disorders (depressive and bipolar disorders), anxiety and fear-related disorders, and disorders specifically associated with stress. Clinician ratings of the clinical utility of the proposed ICD-11 diagnostic guidelines were very positive overall. The guidelines were perceived as easy to use, corresponding accurately to patients' presentations (i.e., goodness of fit), clear and understandable, providing an appropriate level of detail, taking about the same or less time than clinicians' usual practice, and providing useful guidance about distinguishing disorder from normality and from other disorders. Clinicians evaluated the guidelines as less useful for treatment selection and assessing prognosis than for communicating with other health professionals, though the former ratings were still positive overall. Field studies that assess perceived clinical utility of the proposed ICD-11 diagnostic guidelines among their intended users have very important implications. Classification is the interface between health encounters and health information; if clinicians do not find that a new diagnostic system provides clinically useful information, they are unlikely to apply it consistently and faithfully. This would have a major impact on the validity of aggregated health encounter data used for health policy and decision making. Overall, the results of this study provide considerable reason to be optimistic about the perceived clinical utility of the ICD-11 among global clinicians.

Modern medicine and the one-size-fits-all approach: A clinician's comment to Alexandra Pârvan's "Mind Electric" article

As a clinician, I can easily agree with the author that a person's own reality of being healthy is independent of physical evidence or clinical categories and that this perspective should be considered to improve clinical care. However, I cannot follow the assumptions about the nature and working of modern medicine and psychiatry as typically using "black box" and one-size-fits-all treatments in daily practice. I outline several working contexts of doctors where this criticism does only marginally apply or not at all and wonder whether the author might wish, if possible at all from a philosophical viewpoint, to differentiate her concepts with regard to these different contexts. In addition, I think that ill health in the field of psychiatry might have to be dealt with differently than physical ill health.