Method matters: Understanding diagnostic reliability in DSM-IV and DSM-5

Improving dependability in science: A critique on the psychometric qualities of the HiTOP psychosis superspectrum

We reevaluated HiTOP's existing factor analytic evidence-base for a Psychosis (P) superspectrum as encompassing two psychosis-relevant subfactors ("spectra")-Thought Disorder (TD) and Detachment (D). We found that their data did not support P as a superspectrum with TD and D subfactors. Instead, TD contained both positive and negative symptoms of psychosis and emerged at the subfactor level. D did not target negative symptoms but, largely, disorders unrelated to psychosis and should not be placed under P. Determining if P is truly a superspectrum with psychosis TD and D subfactors will require factor analyses whose items are symptom-based and span the full range of psychopathology. Secondly, HiTOP authors state that TD and D provide a "nearly 2-fold" improvement in reliability over schizophrenia diagnoses but, after aligning the comparative study methodologies, this 2-fold improvement disappears. Finally, HiTOP's use of the term thought disorder is inconsistent with the ICD-11 and psychosis literature, in which it refers to formal thought disorder. We recommend that HiTOP (a) refer to P as a subfactor with positive and negative symptoms of psychosis until research indicates otherwise, (b) regularly rely on formal systematic reviews, (c) use appropriate reliability comparisons, (d) deconflate D with negative symptoms, and (e) rename TD.

Coding social interactions in naturalistic settings: The taxonomy of dyadic conversation

Social interaction and conversation is an essential aspect of human behavior, yet existing methods for coding conversations are outdated, and often can only be used in contrived research settings. The Taxonomy of Dyadic Conversation (TDC) is a coding system designed to code dyadic interactions in natural settings by labeling the utterances and turns taken within an interaction using speech categories. The TDC was used to code child-caregiver and adult-adult conversations in a children's museum and during a public forum, respectively. Results supported hypotheses that predicted adult-adult interactions would contain more Declarative Statement and Acknowledgment utterances than child-caregiver interactions, while child-caregiver interactions contained fewer Conversational Turns, as well as more Command and Encouragement utterances. Results also indicated high levels of inter-rater reliability. The potential for additions and modifications to be applied to the standard TDC is discussed.

Spatial-Temporal EEG Fusion Based on Neural Network for Major Depressive Disorder Detection

In view of the major depressive disorder characteristics such as high mortality as well as high recurrence, it is important to explore an objective and effective detection method for major depressive disorder. Considering the advantages complementary of different machine learning algorithms in information mining process, as well as the fusion complementary of different information, in this study, the spatial-temporal electroencephalography fusion framework using neural network is proposed for major depressive disorder detection. Since electroencephalography is a typical time series signal, we introduce recurrent neural network embedded in long short-term memory unit for extract temporal domain features to solve the problem of long-distance information dependence. To reduce the volume conductor effect, the temporal electroencephalography data are mapping into a spatial brain functional network using phase lag index, then the spatial domain features were extracted from brain functional network using 2D convolutional neural networks. Considering the complementarity between different types of features, the spatial-temporal electroencephalography features are fused to achieve data diversity. The experimental results show that spatial-temporal features fusion can improve the detection accuracy of major depressive disorder with a highest of 96.33%. In addition, our research also found that theta, alpha, and full frequency band in brain regions of left frontal, left central, right temporal are closely related to MDD detection, especially theta frequency band in left frontal region. Only using single-dimension EEG data as decision basis, it is difficult to fully explore the valuable information hidden in the data, which affects the overall detection performance of MDD. Meanwhile, different algorithms have their own advantages for different application scenarios. Ideally, different algorithms should use their respective advantages to jointly address complex problems in engineering fields. To this end, we propose a computer-aided MDD detection framework based on spatial-temporal EEG fusion using neural network, as shown in Fig. 1. The simplified process is as follows: (1) Raw EEG data acquisition and preprocessing. (2) The time series EEG data of each channel are input as recurrent neural network (RNN), and RNN is used to process and extract temporal domain (TD) features. (3) The BFN among different EEG channels is constructed, and CNN is used to process and extract the spatial domain (SD) features of the BFN. (4) Based on the theory of information complementarity, the spatial-temporal information is fused to realize efficient MDD detection. Fig. 1 MDD detection framework based on spatial-temporal EEG fusion.

Romanian Inventory of Depression and Anxiety Symptoms (IDAS-II)

Background

The Inventory of Depression and Anxiety Symptoms (IDAS-II) is a self-report measure comprising 99 items divided into 18 non-overlapping scales that allows for a dimensional assessment of depression, anxiety, and bipolar symptoms. The IDAS-II is currently available in English, Turkish, Spanish, German, and Swedish. This study's major goal was to adapt and validate the IDAS-II to the Romanian population.

Method

Participants from a community sample (N = 1,072) completed the IDAS-II (Romanian version) and additional measures assessing depression and anxiety disorders.

Results

Item-level factor analyses validated the unidimensionality of the scales, and internal consistency results indicated that most symptom scales had satisfactory alpha coefficient values. Based on previous structural analyses, a confirmatory factor analysis (CFA) on the IDAS-II scales confirmed a three-component model of "Distress," "Obsessions/Fear," and "Positive Mood." Convergent and discriminant validity were established by correlational analyses with other symptom measures.

Limitations

This study was conducted using a sample from the general population and several of the employed measures have limitations. Specifically, the current study was unable to employ Romanian versions of the gold-standard instruments that assess well-being, obsessive-compulsive disorder, and claustrophobia.

Conclusion

The IDAS-II (Romanian version) is the first clinical measure to assess internalizing dimensions of the Hierarchical Taxonomy of Psychopathology (HiTOP) model that is available for the Romanian population.

Operational definitions and measurement of externalizing behavior problems: An integrative review including research models and clinical diagnostic systems

Measurement of externalizing disorders such as antisocial disorders, attention-deficit/hyperactivity disorder or borderline disorder have relevant implications for the daily lives of people with these disorders. While the Diagnostic and Statistical Manual of Mental Disorders (DSM) and the International Classification of Diseases (ICD) have provided the diagnostic framework for decades, recent dimensional frameworks question the categorical approach of psychopathology, inherent in traditional nosotaxies. Tests and instruments develop under the DSM or ICD framework preferentially adopt this categorical approach, providing diagnostic labels. In contrast, dimensional measurement instruments provide an individualized profile for the domains that comprise the externalizing spectrum, but are less widely used in practice. Current paper aims to review the operational definitions of externalizing disorders defined under these different frameworks, revise the different measurement alternatives existing, and provide an integrative operational definition. First, an analysis of the operational definition of externalizing disorders among the DSM/ICD diagnostic systems and the recent Hierarchical Taxonomy of Psychopathology (HiTOP) model is carried out. Then, in order to analyze the coverage of operational definitions found, a description of measurement instruments among each conceptualization is provided. Three phases in the development of the ICD and DSM diagnosis systems can be observed with direct implications for measurement. ICD and DSM versions have progressively introduced systematicity, providing more detailed descriptions of diagnostic criteria and categories that ease the measurement instrument development. However, it is questioned whether the DSM/ICD systems adequately modelize externalizing disorders, and therefore their measurement. More recent theoretical approaches, such as the HiTOP model seek to overcome some of the criticism raised towards the classification systems. Nevertheless, several issues concerning this model raise mesasurement challenges. A revision of the instruments underneath each approach shows incomplete coverage of externalizing disorders among the existing instruments. Efforts to bring nosotaxies together with other theoretical models of psychopathology and personality are still needed. The integrative operational definition of externalizing disorders provided may help to gather clinical practice and research.

Canalization and plasticity in psychopathology

This theoretical article revives a classical bridging construct, canalization, to describe a new model of a general factor of psychopathology. To achieve this, we have distinguished between two types of plasticity, an early one that we call 'TEMP' for 'Temperature or Entropy Mediated Plasticity', and another, we call 'canalization', which is close to Hebbian plasticity. These two forms of plasticity can be most easily distinguished by their relationship to 'precision' or inverse variance; TEMP relates to increased model variance or decreased precision, whereas the opposite is true for canalization. TEMP also subsumes increased learning rate, (Ising) temperature and entropy. Dictionary definitions of 'plasticity' describe it as the property of being easily shaped or molded; TEMP is the better match for this. Importantly, we propose that 'pathological' phenotypes develop via mechanisms of canalization or increased model precision, as a defensive response to adversity and associated distress or dysphoria. Our model states that canalization entrenches in psychopathology, narrowing the phenotypic state-space as the agent develops expertise in their pathology. We suggest that TEMP - combined with gently guiding psychological support - can counter canalization. We address questions of whether and when canalization is adaptive versus maladaptive, furnish our model with references to basic and human neuroscience, and offer concrete experiments and measures to test its main hypotheses and implications. This article is part of the Special Issue on "National Institutes of Health Psilocybin Research Speaker Series".

Dimensional personality impairment is associated with disruptions in intrinsic intralimbic functional connectivity

BACKGROUND

Recently proposed alternative dimensional models of personality disorder (PD) place the severity of impairments in self and interpersonal functioning at the core of personality pathology. However, associations of these impairments with disturbances in social, cognitive, and affective brain networks remain uninvestigated.

METHODS

The present study examined patterns of resting-state functional connectivity (rsFC) in a sample of 74 age- and sex-matched participants (45 inpatients with PD and 29 healthy controls). At a minimum, PD patients carried a diagnosis of borderline PD, although the majority of the sample had one or more additional PDs. rsFC patterns in the following networks were compared between groups and in association with dimensional personality impairments: default mode network (DMN)/core mentalization, frontolimbic, salience, and central executive. Further, the extent to which variation in rsFC was explained by levels of personality impairment as compared to typology-specific borderline PD symptom severity was explored.

RESULTS

Relative to controls, the PD group showed disruptions in rsFC within the DMN/core mentalization and frontolimbic networks. Among PD patients, greater severity of dimensional self-interpersonal impairment was associated with stronger intralimbic rsFC. In contrast, severity of borderline PD-specific typology was not associated with any rsFC patterns.

CONCLUSIONS

Disruptions in core mentalization and affective networks are present in PD. Higher intralimbic functional connectivity may underlie self-interpersonal personality impairment in PD regardless of diagnostic typology-specific PD symptoms, providing initial neurobiological evidence supporting alternative dimensional conceptualizations of personality pathology.

Precision Medicine Approaches to Mental Health Care

Developing a more comprehensive understanding of the physiological underpinnings of mental illness, precision medicine has the potential to revolutionize psychiatric care. With recent breakthroughs in next-generation multi-omics technologies and data analytics, it is becoming more feasible to leverage multimodal biomarkers, from genetic variants to neuroimaging biomarkers, to objectify diagnostics and treatment decisions in psychiatry and improve patient outcomes. Ongoing work in precision psychiatry will parallel progress in precision oncology and cardiology to develop an expanded suite of blood- and neuroimaging-based diagnostic tests, empower monitoring of treatment efficacy over time, and reduce patient exposure to ineffective treatments. The emerging model of precision psychiatry has the potential to mitigate some of psychiatry's most pressing issues, including improving disease classification, lengthy treatment duration, and suboptimal treatment outcomes. This narrative-style review summarizes some of the emerging breakthroughs and recurring challenges in the application of precision medicine approaches to mental health care.

The joint hierarchical structure of psychopathology and dysfunctional personality domain indicators among community-dwelling adults

To examine the hierarchical structure of psychopathology and dysfunctional personality domains, 2416 Italian community-dwelling adult volunteers were administered a set of psychometrically sound psychopathology measures and the Personality Inventory for DSM-5 Brief Form+ (PID-5-BF+). Parallel analysis, minimum average partial, and very simple structure results suggested that 1-6 principal components (PCs) should be retained. Goldberg's bass-ackwards model of the joint psychopathology measure and PID-5-BF+ ipsatized domain scale correlation matrix evidenced a hierarchical structure that was consistent with the working model proposed by the Hierarchical Taxonomy of Psychopathology (HiTOP) consortium. Hierarchical agglomerative cluster analysis around latent variables of the psychopathology indicators and PID-5-BF+ domain scales recovered four latent dimensions, which were akin to the corresponding bass-ackwards components and nicely reproduced the HiTOP Internalizing, Externalizing, Thought Disorder, and Eating Pathology dimensions.

Aversive Reactivity: A Transdiagnostic Functional Bridge Between Neuroticism and Avoidant Behavioral Coping

Aversive reactivity to negative affect has been described as a transdiagnostic mechanism that links distal temperamental vulnerabilities to clinically relevant behaviors. However, the abundance of constructs reflecting aversive reactivity has resulted in a proliferation of models that may ultimately be redundant. We performed a circumscribed review of studies measuring associations between six constructs - anxiety sensitivity, experiential avoidance, distress intolerance, intolerance of uncertainty, thought-action fusion, and negative urgency - and ten relevant coping behaviors. Results suggested that most constructs were measured in relation to a limited number of coping behaviors. Additionally, constructs were most often measured in isolation, rather than with similar constructs. Implications and suggestions for future research and treatment are discussed.

Exploring the Relationship Between Attachment and Pathological Personality Trait Domains in an Outpatient Psychiatric Sample

ABSTRACT

The current study investigates the relationship between insecure attachment and pathological personality trait domains in a sample of psychiatric outpatients. Participants ( N = 150) completed measures for attachment and personality. Bivariate correlations and multiple regression analyses investigated the extent to which insecure attachment and personality pathology were associated. Insecure attachment positively correlated with overall personality pathology, with attachment anxiety having a stronger correlation than attachment avoidance. Distinct relationships emerged between attachment anxiety and negative affectivity and attachment avoidance and detachment. Insecure attachment and male sex predicted overall personality pathology, but only attachment anxiety predicted all five trait domains. Insecure attachment might be a risk factor for pathological personality traits. Assessing attachment in clinical contexts and offering attachment-based interventions could benefit interpersonal outcomes.

Toward Precision Characterization and Treatment of Psychopathology: A Path Forward and Integrative Framework of the Hierarchical Taxonomy of Psychopathology and the Research Domain Criteria

A critical mission of psychological science is to conduct research that ultimately improves the lives of individuals who experience psychopathology. One important aspect of accomplishing this mission is increasing the likelihood that treatments will work for each person. I contend that treatment prognosis can be improved by moving toward a precision-medicine model. I advance a principle-driven framework for working toward these objectives. First, I synthesize the Hierarchical Taxonomy of Psychopathology and the Research Domain Criteria and demonstrate how integrating these models facilitates precision characterization of psychopathology. Second, I outline and demonstrate a systematic process for approaching treatment selection by leveraging precisely characterized representations of psychopathology. Finally, I advocate the research and clinical applications of this framework. Although clinical and psychological scientists are conducting exciting, multidisciplinary, and methodologically rigorous research in their respective domains, the impact of these pursuits will be maximized in the context of a unifying theoretical framework that supports a clear guiding mission.

Bottom-up transdiagnostic personality subtypes are associated with state psychopathology: A latent profile analysis

Introduction

Personality-based profiling helps elucidate associations between psychopathology symptoms and address shortcomings of current nosologies. The objective of this study was to bracket the assumption of a priori diagnostic class borders and apply the profiling approach to a transdiagnostic sample. Profiles resembling high-functioning, undercontrolled, and overcontrolled phenotypes were expected to emerge.

Methods

We used latent profile analysis on data from a sample of women with mental disorders (n = 313) and healthy controls (n = 114). 3-5 profile solutions were compared based on impulsivity, perfectionism, anxiety, stress susceptibility, mistrust, detachment, irritability, and embitterment. The best-fitting solution was then related to measures of depression, state anxiety, disordered eating, and emotion regulation difficulties to establish clinical significance.

Results

A 5-profile solution proved best-fitting. Extracted profiles included a high-functioning, a well-adapted, an impulsive and interpersonally dysregulated, an anxious and perfectionistic, and an emotionally and behaviorally dysregulated class. Significant differences were found in all outcome state measures, with the emotionally and behaviorally dysregulated class exhibiting the most severe psychopathology.

Discussion

These results serve as preliminary evidence of the predictive nature and clinical utility of personality-based profiles. Selected personality traits should be considered in case formulation and treatment planning. Further research is warranted to replicate the profiles and assess classification stability and profiles' association with treatment outcome longitudinally.

Few-Electrode EEG from the Wearable Devices Using Domain Adaptation for Depression Detection

Nowadays, major depressive disorder (MDD) has become a crucial mental disease that endangers human health. Good results have been achieved by electroencephalogram (EEG) signals in the detection of depression. However, EEG signals are time-varying, and the distributions of the different subjects' data are non-uniform, which poses a bad influence on depression detection. In this paper, the deep learning method with domain adaptation is applied to detect depression based on EEG signals. Firstly, the EEG signals are preprocessed and then transformed into pictures by two methods: the first one is to present the three channels of EEG separately in the same image, and the second one is the RGB synthesis of the three channels of EEG. Finally, the training and prediction are performed in the domain adaptation model. The results indicate that the domain adaptation model can effectively extract EEG features and obtain an average accuracy of 77.0 ± 9.7%. This paper proves that the domain adaptation method can effectively weaken the inherent differences of EEG signals, making the diagnosis of different users more accurate.

An Examination of the RDoC Negative Valence Systems Domain Constructs and the Self-Reports Unit of Analysis

In response to shortcomings with the current diagnostic classification system for mental health disorders, such as poor validity and reliability of categorical diagnoses, the National Institute of Mental Health proposed the Research Domain Criteria (RDoC) initiative to move towards a dimensional approach using translational research. The current study examined associations between measures of behaviors, cognitions, and mental health symptoms and how they overlap in the Negative Valence Systems (NVS) domain. Specifically, we examined how the Self-Reports unit of analysis reflects the RDoC NVS constructs of acute threat, potential threat, sustained threat, frustrative nonreward, and loss. The overall goal was to identify additional self-report measures that reflect these constructs. Participants, two student samples and two community samples (total N = 1,509), completed online self-reported measures. Questionnaire total and subscale scores were submitted to a principal-axis factor analysis with Promax rotation separately for each sample. For both student samples and one community sample six-factor solutions emerged reflecting major aspects of the RDoC NVS and positive valence systems, particularly acute threat (i.e., fear/panic), potential threat (i.e., inhibition/worry), sustained threat (i.e., chronic stress), loss (i.e., low well-being), frustrative nonreward (i.e., reactive aggression), and reduced behavioral activation. The second community sample differed in that fear/panic and frustration/anger was combined in a general distress factor. Recommendations for additional NVS self-report markers are discussed.

Extreme emotional disturbance: Legal frameworks and considerations for forensic evaluation

A significant minority of jurisdictions in the United States offer extreme emotional disturbance (EED) as a partial defense to murder. The form of this defense, as established by statute and case law, varies widely among jurisdictions. Empirical research on EED is scant with little guidance to forensic mental health professionals on how to approach and conceptualize potential EED cases. This paper addresses these issues by being the first known published work to (1) set forth a contemporary map of the varying definitions and scope of EED across the United States, (2) translate legal terminology into constructs accessible to forensic evaluators, and (3) provide legal and clinical analyses of sample EED cases to highlight key differences in the form of the defense and the admissibility of evidence between jurisdictions.

Abnormal Brain Topological Structure of Mild Depression During Visual Search Processing Based on EEG Signals

Studies have shown that attention bias can affect behavioral indicators in patients with depression, but it is still unclear how this bias affects the brain network topology of patients with mild depression (MD). Therefore, a novel functional brain network analysis and hierarchical clustering methods were used to explore the abnormal brain topology of MD patients based on EEG signals during the visual search paradigm. The behavior results showed that the reaction time of MD group was significantly higher than that of normal group. The results of functional brain network indicated significant differences in functional connections between the two groups, the amount of inter-hemispheric long-distance connections are much larger than intra-hemispheric short-distance connections. Patients with MD showed significantly lower local efficiency and clustering coefficient, destroyed community structure of frontal lobe and parietal-occipital lobe, frontal asymmetry, especially in beta band. In addition, the average value of long-distance connections between left frontal and right parietal-occipital lobes presented significant correlation with depressive symptoms. Our results suggested that MD patients achieved long-distance connections between the frontal and parietal-occipital regions by sacrificing the connections within the regions, which might provide new insights into the abnormal cognitive processing mechanism of depression.

A Systematic Review of Sleep–Wake Disorder Diagnostic Criteria Reliability Studies

The aim of this article is to provide a systematic review of reliability studies of the sleep–wake disorder diagnostic criteria of the international classifications used in sleep medicine. Electronic databases (ubMed (1946–2021) and Web of Science (—2021)) were searched up to December 2021 for studies computing the Cohen’s kappa coefficient of diagnostic criteria for the main sleep–wake disorder categories described in the principal classifications. Cohen’s kappa coefficients were extracted for each main sleep–wake disorder category, for each classification subtype, and for the different types of methods used to test the degree of agreement about a diagnosis. The database search identified 383 studies. Fifteen studies were analyzed in this systematic review. Insomnia disorder (10/15) and parasomnia disorder (7/15) diagnostic criteria were the most studied. The reliability of all sleep–wake disorders presented a Cohen’s kappa with substantial agreement (Cohen’s kappa mean = 0.66). The two main reliability methods identified were “test–retest reliability” (11/15), principally used for International Classification of Sleep Disorders (ICSD), and “joint interrater reliability” (4/15), principally used for Diagnostic and Statistical Manual of Mental Disorders (DSM) subtype diagnostic criteria, in particularl, the DSM-5. The implications in terms of the design of the methods used to test the degree of agreement about a diagnosis in sleep medicine are discussed.

Graph Convolutional Networks Reveal Network-Level Functional Dysconnectivity in Schizophrenia

BACKGROUND AND HYPOTHESIS

Schizophrenia is increasingly understood as a disorder of brain dysconnectivity. Recently, graph-based approaches such as graph convolutional network (GCN) have been leveraged to explore complex pairwise similarities in imaging features among brain regions, which can reveal abstract and complex relationships within brain networks.

STUDY DESIGN

We used GCN to investigate topological abnormalities of functional brain networks in schizophrenia. Resting-state functional magnetic resonance imaging data were acquired from 505 individuals with schizophrenia and 907 controls across 6 sites. Whole-brain functional connectivity matrix was extracted for each individual. We examined the performance of GCN relative to support vector machine (SVM), extracted the most salient regions contributing to both classification models, investigated the topological profiles of identified salient regions, and explored correlation between nodal topological properties of each salient region and severity of symptom.

STUDY RESULTS

GCN enabled nominally higher classification accuracy (85.8%) compared with SVM (80.9%). Based on the saliency map, the most discriminative brain regions were located in a distributed network including striatal areas (ie, putamen, pallidum, and caudate) and the amygdala. Significant differences in the nodal efficiency of bilateral putamen and pallidum between patients and controls and its correlations with negative symptoms were detected in post hoc analysis.

CONCLUSIONS

The present study demonstrates that GCN allows classification of schizophrenia at the individual level with high accuracy, indicating a promising direction for detection of individual patients with schizophrenia. Functional topological deficits of striatal areas may represent a focal neural deficit of negative symptomatology in schizophrenia.

Validity and utility of Hierarchical Taxonomy of Psychopathology (HiTOP): III. Emotional dysfunction superspectrum

The Hierarchical Taxonomy of Psychopathology (HiTOP) is a quantitative nosological system that addresses shortcomings of traditional mental disorder diagnoses, including arbitrary boundaries between psychopathology and normality, frequent disorder co-occurrence, substantial heterogeneity within disorders, and diagnostic unreliability over time and across clinicians. This paper reviews evidence on the validity and utility of the internalizing and somatoform spectra of HiTOP, which together provide support for an emotional dysfunction superspectrum. These spectra are composed of homogeneous symptom and maladaptive trait dimensions currently subsumed within multiple diagnostic classes, including depressive, anxiety, trauma-related, eating, bipolar, and somatic symptom disorders, as well as sexual dysfunction and aspects of personality disorders. Dimensions falling within the emotional dysfunction superspectrum are broadly linked to individual differences in negative affect/neuroticism. Extensive evidence establishes that dimensions falling within the superspectrum share genetic diatheses, environmental risk factors, cognitive and affective difficulties, neural substrates and biomarkers, childhood temperamental antecedents, and treatment response. The structure of these validators mirrors the quantitative structure of the superspectrum, with some correlates more specific to internalizing or somatoform conditions, and others common to both, thereby underlining the hierarchical structure of the domain. Compared to traditional diagnoses, the internalizing and somatoform spectra demonstrated substantially improved utility: greater reliability, larger explanatory and predictive power, and greater clinical applicability. Validated measures are currently available to implement the HiTOP system in practice, which can make diagnostic classification more useful, both in research and in the clinic.

Longitudinal bifactor modeling of anxiety, depression and schizotypy - The role of rumination as a shared mechanism

A bifactor model with a general (p) factor reflecting shared variance and specific factors reflecting additional variance in individual symptoms has been introduced to explain common co-occurrence among anxiety, depression and schizotypy. However, longitudinal evidence is lacking and the validity of bifactor modeling is debatable. The current study aimed to examine the presence of the p factor together with specific factors in accounting for relationships between anxiety, depression and schizotypy both cross-sectionally and longitudinally, and to investigate the relationship between these factors and rumination. A validated sample of university students were surveyed on levels of anxiety, depression, schizotypy and rumination at baseline (N = 2291), one year (N = 1833) and two years (N = 1656). Models were estimated using exploratory structural equation modeling (ESEM) and compared at each time point. Longitudinal invariance of the best-fitting model was examined and all potential within- and between-factor stability pathways were tested in an SEM framework. A bifactor model with a p factor and four specific factors (representing residual information of composite anxiety and depression, cognitive-perceptual, interpersonal and disorganized schizotypy respectively) consistently outperformed a correlated-factors model. The bifactor structure appeared longitudinally stable. Within-factor stabilities were moderate, and between-factor pathways reflected a few significant interactions, mostly involving the p factor. Rumination was independently associated with p and four specific factors at each time point. Therefore, there is a p factor accounting for concurrent and sequential co-occurrence of anxiety, depression and schizotypy. Rumination explained partly the p and specific factors. Transdiagnostic interventions should target rumination.

Psychological and Physical Intimate Partner Aggression Are Associated with Broad and Specific Internalizing Symptoms during Pregnancy

Background: Intimate partner violence (IPV) has serious consequences, particularly during high-risk periods such as pregnancy, which poses a significant risk to maternal mental health. However, it is unclear whether IPV presents a broad risk for psychopathology or is specific to distinct diagnoses or symptom dimensions (e.g., panic, social anxiety). Further, the relative impact of physical versus psychological aggression remains unclear. Methods: One hundred and fifty-nine pregnant couples completed surveys assessing psychological and physical intimate partner aggression unfolding in the couple relationship, as well as a range of internalizing symptoms. Results: Psychological and physical aggression were each associated with broad negative affectivity, which underlies mood and anxiety disorders; however, only psychological aggression demonstrated a unique association. Further, for pregnant women, aggression was uniquely associated with several symptom dimensions characteristic of PTSD. In contrast, men demonstrated a relatively heterogeneous symptom presentation in relation to aggression. Conclusion: The present study identifies unique symptom manifestations associated with IPV for couples navigating pregnancy and suggests psychological aggression can be more detrimental to mental health than physical aggression. To promote maternal perinatal mental health, clinicians should screen for covert forms of psychological aggression during pregnancy (e.g., raised voices, insults), trauma-related distress, and symptom elevations in women and their partners.

End-to-end multimodal clinical depression recognition using deep neural networks: A comparative analysis

BACKGROUND AND OBJECTIVE

Major Depressive Disorder is a highly prevalent and disabling mental health condition. Numerous studies explored multimodal fusion systems combining visual, audio, and textual features via deep learning architectures for clinical depression recognition. Yet, no comparative analysis for multimodal depression analysis has been proposed in the literature.

METHODS

In this paper, an up-to-date literature overview of multimodal depression recognition is presented and an extensive comparative analysis of different deep learning architectures for depression recognition is performed. First, audio features based Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) are studied. Then, early-level and model-level fusion of deep audio features with visual and textual features through LSTM and CNN architectures are investigated.

RESULTS

The performance of the proposed architectures using an hold-out strategy on the DAIC-WOZ dataset (80% training, 10% validation, 10% test split) for binary and severity levels of depression recognition is tested. Using this strategy, a set of experiments have been performed and they have demonstrated: (1) LSTM-based audio features perform slightly better than CNN ones with an accuracy of 66.25% versus 65.60% for binary depression classes. (2) the model level fusion of deep audio and visual features using LSTM network performed the best with an accuracy of 77.16%, a precision of 53% for the depressed class, and a precision of 83% for the non-depressed class. The given network obtained a normalized Root Mean Square Error (RMSE) of 0.15 for depression severity level prediction. Using a Leave-One-Subject-Out strategy, this network achieved an accuracy of 95.38% for binary depression detection, and a normalized RMSE of 0.1476 for depression severity level prediction. Our best-performing architecture outperforms all state-of-the-art approaches on DAIC-WOZ dataset.

CONCLUSIONS

The obtained results show that the proposed LSTM-based surpass the proposed CNN-based architectures allowing to learn temporal dynamics representations of multimodal features. Furthermore, model-level fusion of audio and visual features using an LSTM network leads to the best performance. Our best-performing architecture successfully detects depression using a speech segment of less than 8 seconds, and an average prediction computation time of less than 6ms; making it suitable for real-world clinical applications.

Does the millennial generation of women experience more mental illness than their mothers?

BACKGROUND

There is concern that rates of mental disorders may be increasing although findings disagree. Using an innovative design with a daughter-mother data set we assess whether there has been a generational increase in lifetime ever rates of major depressive disorder, generalised anxiety disorder, panic disorder, and post-traumatic stress disorder (PTSD) experienced prior to 30 years of age.

METHODS

Pregnant women were recruited during 1981-1983 and administered the Composite International Diagnostic Interview (CIDI) at the 27-year follow-up (2008-11). Offspring were administered the CIDI at the 30-year follow-up (2010-2014). Comparisons for onset of diagnosis are restricted to daughter and mother dyads up to 30 years of age. To address recall bias, disorders were stratified into more (≥12 months duration) and less persistent episodes (< 12 months duration) for the purposes of comparison. Sensitivity analyses with inflation were used to account for possible maternal failure to differentially recall past episodes.

RESULTS

When comparing life time ever diagnoses before 30 years, daughters had higher rates of persistent generalised anxiety disorder, and less persistent major depressive disorder, generalised anxiety disorder and PTSD.

CONCLUSIONS

In the context of conflicting findings concerning generational changes in mental disorders we find an increase in generational rates of persistent generalised anxiety disorders and a range of less persistent disorders. It is not clear whether this finding reflects actual changes in symptom levels over a generation or whether there has been a generational change in recognition of and willingness to report symptoms of mental illness.

Neurobiology and the Hierarchical Taxonomy of Psychopathology: progress toward ontogenetically informed and clinically useful nosology

The Hierarchical Taxonomy of Psychopathology (HiTOP) is an empirical structural model of psychological symptoms formulated to improve the reliability and validity of clinical assessment. Neurobiology can inform assessments of early risk and intervention strategies, and the HiTOP model has greater potential to interface with neurobiological measures than traditional categorical diagnoses given its enhanced reliability. However, one complication is that observed biological correlates of clinical symptoms can reflect various factors, ranging from dispositional risk to consequences of psychopathology. In this paper, we argue that the HiTOP model provides an optimized framework for conducting research on the biological correlates of psychopathology from an ontogenetic perspective that distinguishes among indicators of liability, current symptoms, and consequences of illness. Through this approach, neurobiological research can contribute more effectively to identifying individuals at high dispositional risk, indexing treatment-related gains, and monitoring the consequences of mental illness, consistent with the aims of the HiTOP framework.


Validity and utility of Hierarchical Taxonomy of Psychopathology (HiTOP): II. Externalizing superspectrum

The Hierarchical Taxonomy of Psychopathology (HiTOP) is an empirical effort to address limitations of traditional mental disorder diagnoses. These include arbitrary boundaries between disorder and normality, disorder co-occurrence in the modal case, heterogeneity of presentation within dis-orders, and instability of diagnosis within patients. This paper reviews the evidence on the validity and utility of the disinhibited externalizing and antagonistic externalizing spectra of HiTOP, which together constitute a broad externalizing superspectrum. These spectra are composed of elements subsumed within a variety of mental disorders described in recent DSM nosologies, including most notably substance use disorders and "Cluster B" personality disorders. The externalizing superspectrum ranges from normative levels of impulse control and self-assertion, to maladaptive disinhibition and antagonism, to extensive polysubstance involvement and personality psychopathology. A rich literature supports the validity of the externalizing superspectrum, and the disinhibited and antagonistic spectra. This evidence encompasses common genetic influences, environmental risk factors, childhood antecedents, cognitive abnormalities, neural alterations, and treatment response. The structure of these validators mirrors the structure of the phenotypic externalizing superspectrum, with some correlates more specific to disinhibited or antagonistic spectra, and others relevant to the entire externalizing superspectrum, underlining the hierarchical structure of the domain. Compared with traditional diagnostic categories, the externalizing superspectrum conceptualization shows improved utility, reliability, explanatory capacity, and clinical applicability. The externalizing superspectrum is one aspect of the general approach to psychopathology offered by HiTOP and can make diagnostic classification more useful in both research and the clinic.

Schizophrenia: a classic battle ground of nature versus nurture debate

Much has been learned about the etiology and pathogenesis of schizophrenia since the term was first used by Eugene Bleuler over a century ago to describe one of the most important forms of major mental illness to affect mankind. Both nature and nurture feature prominently in our understanding of the genesis of the overall risk of developing schizophrenia. We now have a firm grasp of the broad structure of the genetic architecture and several key environmental risk factors have been identified and delineated. However, much of the heritability of schizophrenia remains unexplained and the reported environmental risk factors do not explain all the variances not attributable to genetic risk factors. The biggest problem at present is that our understanding of the causal mechanisms involved is still in its infancy. In this review, we describe the extent and limits of our knowledge of the specific genetic/constitutional and non-genetic/environmental factors that contribute to the overall risk of schizophrenia. We suggest novel methods may be required to understand the almost certainly immensely complex multi-level causal mechanisms that contribute to the generation of the schizophrenia phenotype.

The distribution of daily affect distinguishes internalizing and externalizing spectra and subfactors

There has been increasing recognition that classically defined psychiatric disorders cluster hierarchically. However, the degree to which this hierarchical taxonomy manifests in the distribution of one's daily affective experience is unknown. In 462 young adults, we assessed psychiatric symptoms across internalizing and externalizing disorders and then used cell-phone-based ecological momentary assessment (EMA) to assess the distribution (mean, standard deviation, skew, kurtosis) of one's positive and negative affect over 3-4 months. Psychiatric symptoms were modeled using a higher-order factor model that estimated internalizing and externalizing spectra as well as specific disorders. Individualized factor loadings were extracted, and path models assessed associations between spectra and syndromes, and daily affect. Internalizing and externalizing spectra displayed broad differences in the distribution of affective experiences, while within the internalizing spectrum, syndromes loading onto fear and distress subfactors were associated with distinct patterns of affective experiences. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

A systematic review into the effectiveness of occupational therapy for improving function and participation in activities of everyday life in adults with a diagnosis of depression

BACKGROUND

Depression is a common mental health disorder, the symptoms of which can disrupt functioning and lead to reduced participation in everyday activities.  Occupational therapy is routinely provided for people with such difficulties; however, the evidence underpinning this intervention for depression has yet to be systematically assessed.

METHOD

A systematic review of the effectiveness of occupational therapy for people with a diagnosis of depression, using the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) was undertaken. Seven databases were searched using terms for depression combined with terms associated with occupational therapy. Due to heterogeneity in study design and outcome measures, a best evidence synthesis was undertaken as an alternative to meta-analysis.

RESULTS

Of 1962 articles identified, 63 full texts were assessed and six met the inclusion criteria. Studies were carried out in Canada, Germany, the Netherlands, Taiwan and the United Kingdom. There was strong evidence for the effectiveness of occupational therapy return-to-work interventions for improving depression symptomology, limited evidence for occupational therapy lifestyle interventions for reducing anxiety and suicidal ideation, and limited evidence for improving work participation.  No studies evaluated individualised client-centred occupational therapy, highlighting a gap in research.

LIMITATIONS

Incomplete reporting within studies and heterogeneity prevented meta-analysis.  English language restrictions were applied.

CONCLUSIONS

Whilst overall the evidence base for occupational therapy for depression is limited, strong evidence was found for the effectiveness of occupational therapy return-to-work interventions, which is important given the costs associated with mental ill-health and work absence. Further research is needed to strengthen the evidence base.

Translational Application of a Neuro-Scientific Multi-Modal Approach Into Forensic Psychiatric Evaluation: Why and How?

A prominent body of literature indicates that insanity evaluations, which are intended to provide influential expert reports for judges to reach a decision "beyond any reasonable doubt," suffer from a low inter-rater reliability. This paper reviews the limitations of the classical approach to insanity evaluation and the criticisms to the introduction of neuro-scientific approach in court. Here, we explain why in our opinion these criticisms, that seriously hamper the translational implementation of neuroscience into the forensic setting, do not survive scientific scrutiny. Moreover, we discuss how the neuro-scientific multimodal approach may improve the inter-rater reliability in insanity evaluation. Critically, neuroscience does not aim to introduce a brain-based concept of insanity. Indeed, criteria for responsibility and insanity are and should remain clinical. Rather, following the falsificationist approach and the convergence of evidence principle, the neuro-scientific multimodal approach is being proposed as a way to improve reliability of insanity evaluation and to mitigate the influence of cognitive biases on the formulation of insanity opinions, with the final aim to reduce errors and controversies.

The Clinical Consistency and Utility of ICD-11 Diagnostic Guidelines for Gaming Disorder: A Field Study Among the Chinese Population

Purpose: As a new category proposed in the International Classification of Diseases (11th Revision) (ICD-11), the reliability and clinical utility of ICD diagnostic guidelines for gaming disorder (GD) in the Chinese population have not been studied. The purpose of this field study is to clarify the reliability, clinical utility, and cultural applicability of ICD diagnostic guidelines for GD in China and its comparability with Internet GD (IGD) in the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5). Methods: Participants included 21 paired clinical raters consisting of seven psychiatrists and 200 gaming players aged from 15 to 18 years with different risk levels of Internet addiction based on the scores of Young's Internet Addiction Test. Each participant received a semi-structured face-to-face interview by paired clinical raters at the same time. Then clinical raters made the diagnosis and filled the clinical utility questionnaire independently according to the diagnostic guidelines for GD in both ICD-11 and DSM-5. Results: The diagnostic consistency coefficient (kappa value) between the paired clinical raters was 0.545 (0.490-0.600, p < 0.001) and 0.622 (0.553-0.691, p < 0.001) for ICD-11 and DSM-5 diagnostic guidelines, respectively, for GD. The diagnostic consistency was 0.847 (0.814-0.880, p < 0.001) between GD in ICD-11 and IGD in DSM-5. Meanwhile, 86.7% of responses that agreed with the ICD-11 diagnostic guidelines for GD provided enough detailed implementation characteristics and showed good overall clinical applicability (86.0%), specificity (94.4%), usefulness (84.1%), and acceptable cultural adaptation (74.8%). GD in ICD-11 was slightly more accepted than IGD in DSM-5 (p < 0.001), while the clinical efficiency of ICD-11 was inferior to that of DSM-5 (p < 0.001). Conclusion: This study indicates that the ICD-11 diagnostic guidelines for GD have acceptable clinical reliability and high consistency with IGD in DSM-5. Their clinical applicability and cultural adaption are comparable with those of DSM-5. Although the guidelines still need to be adjusted for better implementation in China, this is already a great step committed to reducing the serious consequences caused by excessive gaming behaviors through effective identification and normative diagnosis, especially for adolescents.

Validating Biobehavioral Technologies for Use in Clinical Psychiatry

The last decade has witnessed the development of sophisticated biobehavioral and genetic, ambulatory, and other measures that promise unprecedented insight into psychiatric disorders. As yet, clinical sciences have struggled with implementing these objective measures and they have yet to move beyond "proof of concept." In part, this struggle reflects a traditional, and conceptually flawed, application of traditional psychometrics (i.e., reliability and validity) for evaluating them. This paper focuses on "resolution," concerning the degree to which changes in a signal can be detected and quantified, which is central to measurement evaluation in informatics, engineering, computational and biomedical sciences. We define and discuss resolution in terms of traditional reliability and validity evaluation for psychiatric measures, then highlight its importance in a study using acoustic features to predict self-injurious thoughts/behaviors (SITB). This study involved tracking natural language and self-reported symptoms in 124 psychiatric patients: (a) over 5-14 recording sessions, collected using a smart phone application, and (b) during a clinical interview. Importantly, the scope of these measures varied as a function of time (minutes, weeks) and spatial setting (i.e., smart phone vs. interview). Regarding reliability, acoustic features were temporally unstable until we specified the level of temporal/spatial resolution. Regarding validity, accuracy based on machine learning of acoustic features predicting SITB varied as a function of resolution. High accuracy was achieved (i.e., ~87%), but only when the acoustic and SITB measures were "temporally-matched" in resolution was the model generalizable to new data. Unlocking the potential of biobehavioral technologies for clinical psychiatry will require careful consideration of resolution.

A multidisciplinary approach to insanity assessment as a way to reduce cognitive biases

Insanity assessment requires the evaluation of the psychopathological condition that underlies the mens rea. Psychopathological evaluation may be quite challenging due to (i) absence of biomarkers; (ii) low inter-rater reliability; (iii) presence of cognitive bias. This intrinsic low reliability of forensic psychiatric diagnosis does impact on insanity assessment, leading to arbitrary and unjust legal outcomes for the examinee. Thus, strategies to improve the reliability of insanity evaluation are strongly needed. A multidisciplinary approach has been proposed as a way to enrich clinical diagnosis with reliable and biologically founded data, thus minimizing subjectivity, reducing controversies and increasing inter-subject concordance in insanity assessment. By discussing a real case, here we show how the convergence of multiple indices can produce evidence that cannot be denied without introducing logical fallacies. Applying this approach, the forensic discussion will move from the presence/absence of psychopathology to the impact of psychopathology on insanity. This article illustrates how a multidisciplinary evaluation, which integrates neuroscientific methods with the classical insanity assessment, may lead to a more accurate approach in insanity evaluation. Critically, this approach will minimize the impact of cognitive bias on insanity opinion and thus result in an improvement of the whole criminal justice process.

The Inter-Rater Reliability and Validity of the Italian Translation of the Structured Clinical Interview for DSM-5 Alternative Model for Personality Disorders Module I and Module II: A Preliminary Report on Consecutively Admitted Psychotherapy Outpatients

To evaluate the reliability and convergent validity of the Structured Clinical Interview for DSM-5 Alternative Model for Personality Disorders (SCID-5-AMPD) Module I and Module II, 88 adult psychotherapy participants were administered the Italian translations of the SCID-5-AMPD Module I and Module II, Level of Personality Functioning Scale-Brief Form (LPFS-BF), Level of Personality Functioning Scale-Self Report (LPFS-SF), Personality Inventory for DSM-5 (PID-5), Personality Diagnostic Questionnaire-4+ (PDQ-4+), and Structured Clinical Interview for DSM-5 Personality Disorders (SCID-5-PD) relying on a Williams crossover design. SCID-5-AMPD Module I and Module II showed excellent inter-rater reliability. In terms of convergent validity, meaningful associations were observed between SCID-5-AMPD Module I scores and self-report measures of Criterion A; similarly, SCID-5-AMPD Module II trait scores were meaningfully related to PID-5 trait scores. As a whole, our preliminary findings supported the clinical utility of DSM-5 AMPD.

Internalized psychopathology dimensions in middle childhood: Cross-sectional and temporal associations

BACKGROUND

Anxiety, depression, and obsessive-compulsive symptoms often onset during middle childhood and are major causes of disability in young individuals. A better understanding of how these symptoms are linked and unfold over time is important to develop valid etiological models and effective prevention and treatment.

METHODS

In the present study, 950 community children (8-14 years) reported on a broad range of internalised symptoms at three time points over the course of a year. First, factor analysis was used to examine the overarching dimensions of these symptoms. Second, network analysis was used to examine unique cross-sectional associations among these empirically supported symptom dimensions. Last, longitudinal structural equation models (SEMs) were used to examine temporal associations among the symptom dimensions.

RESULTS

Six broad symptom dimensions fitted the self-report data well at all time points. These dimensions were conceptualized as depression, general anxiety, situational fears, compulsivity, intrusive thoughts, and somatic anxiety. Network analysis showed that these dimensions formed a highly interconnected network with general anxiety and somatic anxiety being most central (i.e., most strongly associated with other dimensions) at all time points. Longitudinal SEMs supported the central role played by general anxiety in the temporal associations among these dimensions.

CONCLUSIONS

Overarching expressions of internalized psychopathology are highly interconnected in middle childhood with possible central roles played by general and somatic anxiety. Interventions aimed at a general proneness for anxiety may be warranted in preventing and treating internalizing symptoms in middle childhood.

DSM-5 Alternative Model of Personality Disorder Dysfunctional Personality Traits as Predictors of Self-Reported Aggression in an Italian Sample of Consecutively Admitted, Personality-Disordered Psychotherapy Patients

In order to assess the relationships between DSM-5 Alternative Model of Personality Disorder (AMPD) maladaptive personality traits and self-reports of aggression, 508 Italian adult participants who met at least one DSM-IV Axis II/DSM-5 Section II personality disorder (PD) diagnosis were administered the Personality Inventory for DSM-5 (PID-5) and the Aggression Questionnaire (AQ). Analysis results showed that multiple regression results, PID-5 Hostility, Callousness, and Risk Taking trait scale scores explained a large amount of variance in AQ Physical Aggression (PA) scores. Moreover, PID-5 Hostility, Callousness, and Risk Taking explained more than 20% of the variance in the AQ Physical Aggression scale scores that was left unexplained by selected continuously scored DSM-IV Axis II/DSM-5 Section II PDs, whereas SCID-II Paranoid, Narcissistic, Borderline, and Antisocial PDs added only 4% of variance to the amount of variance in AQ Physical Aggression scores that was already explained by the PID-5 trait scale scores.

The difference between trait disinhibition and impulsivity-and why it matters for clinical psychological science

In the psychological science field, there is substantial interest in quantifying individual differences in self-regulatory capacity because of its transdiagnostic relevance to various forms of psychopathology. Trait disinhibition and impulsiveness are popular conceptualizations of dispositions reflecting self-regulation of behavioral and emotional responding. In the literature, these constructs are often treated interchangeably because of their shared focus on general disconstraint and a lack of direct comparisons between measures of each. The current work used structural modeling to examine conceptual and empirical differences between 2 popular operationalizations of these traits in 2 samples (Ns = 400, 308), and employed regression and dominance analyses to compare their predictive relations with criterion measures of externalizing problems and negative affectivity (NA). Impulsigenic traits were related both to externalizing problems and NA, whereas trait disinhibition was selectively associated with externalizing. In a dominance analysis, trait disinhibition exhibited complete dominance over all impulsigenic traits in predicting externalizing problems. Conversely, multiple impulsigenic traits evidenced complete dominance over trait disinhibition in prediction of NA. The current work provides evidence that (a) disinhibition and impulsigenic traits are not interchangeable, (b) disinhibition specifically indexes propensity for externalizing problems, and (c) impulsigenic traits reflect a blend of externalizing and NA that appears relevant to diverse forms of psychopathology. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Personality and psychopathology: In defense of a practical path toward integrating psychometric and biological approaches to advance a comprehensive model

Personality and psychopathology each reflect patterns of internal experience and outward behavior that differ between people and affect functioning. Drawing strict distinctions between the two concepts is not only difficult, but it may prove unnecessary for advancing an integrated model of psychological experiences associated with mental illness. We argue that developing such a model will be critical for improving treatment outcomes, and we discuss a practical path forward. Proponents of psychometric approaches to developing models of psychological experience focus on observable phenotypes and utilize statistical methods to describe patterns of covariation among a broad range of symptoms and dispositions. Advocates of biologically based approaches emphasize neuroscientific tools for identifying abnormalities in brain function that give rise to an individual's experience. There is substantial evidence that measures of personality and measures of symptoms capture nonoverlapping, clinically important information for understanding how and for whom treatments for mental illness work. In this article, we highlight the importance of combining psychometric and neurobiological approaches in order to understand which features of an individual those measures reflect, which aspects of neurobiology generate and maintain those features, how they relate to each other, and critically, how best to alter them to reduce distress and dysfunction.

An evolutionary life history approach to understanding mental health

In recent years, evolutionary life history theory has been used as a heuristic framework to understand mental health. This article reviews the life history theory and its integration with mental disorders and then introduces representative research methods and related empirical studies in the field of evolutionary psychopathology. In the end, this article concludes with future directions for further research examining and developing the evolutionary psychopathological framework.

Fractures in the framework: limitations of classification systems in psychiatry

This article examines the limitations of existing classification systems from the historical, cultural, political, and legal perspectives. It covers the evolution of classification systems with particular emphasis on the DSM and ICD systems. While pointing out the inherent Western bias in these systems, it highlights the potential of misuse of these systems to subserve other agendas. It raises concerns about the reliability, validity, comorbidity, and heterogeneity within diagnostic categories of contemporary classification systems. Finally, it postulates future directions in alternative methods of diagnosis and classification factoring in advances in artificial intelligence, machine learning, genetic testing, and brain imaging. In conclusion, it emphasizes the need to go beyond the limitations inherent in classifications systems to provide more relevant diagnoses and effective treatments.


Heterogeneity and Subtyping in Attention-Deficit/Hyperactivity Disorder-Considerations for Emerging Research Using Person-Centered Computational Approaches

Few if any experts believe that existing psychiatric diagnostic categories included in DSM and ICD are actually discrete disease entities. Attention-deficit/hyperactivity disorder (ADHD) is emblematic of the problems in the existing psychiatric classification system. ADHD symptoms reliably cluster into two correlated dimensions in factor analysis. However, children with ADHD vary considerably in their symptom profiles, symptom trajectories, clinical outcomes, and biological and psychological correlates. Thus, the field has sought alternative approaches that harness the dimensions of emotional, cognitive, and behavioral functioning that underlie ADHD and other existing psychiatric categories to create informative phenotypes that improve clinical prediction and clarify etiology. Within ADHD, cognitive (neuropsychological) and temperament/personality features have received considerable attention. In some cases, subphenotypes based on these features appear to improve on existing classifications and could eventually be translated into clinical practice. This review summarizes findings from subphenotyping efforts in ADHD that use cognitive, emotion-related, and other features to highlight major considerations for research applying person-oriented approaches to inform an improved psychiatric nosology. Considerations related to feature selection, validation of newly proposed divisions, defining populations of interest, and incorporating a developmental perspective are discussed.

Psychometric Properties of Persian Version of Structured Clinical Interview for DSM-5-Research Version (SCID-5-RV): A Diagnostic Accuracy Study

Background: The structured clinical interview for DSM-5 has recently been revised to reflect the new findings in the diagnostic criteria of psychological disorders. Objectives: The present study aimed to evaluate the reliability and validity of the Persian translation of structured clinical interview for diagnostic and statistical manual of mental disorders-fifth edition (DSM-5)-research version (SCID-5-RV) on Iranian adult population. Methods: In the current diagnostic accuracy study a total of 305 clinical samples were admitted to fifteen adult clinical settings and a subsample of these participants (n = 50, with a mean age of 34.31 and a standard deviation of 11.96) was recruited to evaluate test-retest reliability, and 40 non-clinical participants were recruited to examine construct validity. All participants completed the Millon Clinical Multiaxial inventory-III (MCMI-III) and Brief Symptom inventory (BSI). Results: SCID psychometric properties indicated an acceptable range for internal consistency (0.95 - 0.99), test-retest reliability (0.60 - 0.79), and Kappa reliability (0.57 - 0.72). Further, the agreement between interviewer and psychiatrist diagnoses was assessed using the Kappa index, and the result was satisfactory. The current diagnostic accuracy study used sensitivity and specificity indexes to assess the diagnostic validity of SCID by positive predictive value and also negative predictive value under the “likelihood ratio” domain. Specificity values for most psychiatric disorders were high; the sensitivity values were to somewhat lower. Furthermore, SCID-5-RV categorical diagnoses demonstrated an acceptable construct validity based on the significant differences between the clinical and non-clinical samples in all subscales of BSI except for phobia as well as all clinical subscales of MCMI-III. Conclusions: In general, the Persian translation of SCID-5-RV represented acceptable reliability and validity for various categorical diagnoses in different clinical settings.

Validity and utility of Hierarchical Taxonomy of Psychopathology (HiTOP): I. Psychosis superspectrum

The Hierarchical Taxonomy of Psychopathology (HiTOP) is a scientific effort to address shortcomings of traditional mental disorder diagnoses, which suffer from arbitrary boundaries between psychopathology and normality, frequent disorder co-occurrence, heterogeneity within disorders, and diagnostic instability. This paper synthesizes evidence on the validity and utility of the thought disorder and detachment spectra of HiTOP. These spectra are composed of symptoms and maladaptive traits currently subsumed within schizophrenia, other psychotic disorders, and schizotypal, paranoid and schizoid personality disorders. Thought disorder ranges from normal reality testing, to maladaptive trait psychoticism, to hallucinations and delusions. Detachment ranges from introversion, to maladaptive detachment, to blunted affect and avolition. Extensive evidence supports the validity of thought disorder and detachment spectra, as each spectrum reflects common genetics, environmental risk factors, childhood antecedents, cognitive abnormalities, neural alterations, biomarkers, and treatment response. Some of these characteristics are specific to one spectrum and others are shared, suggesting the existence of an overarching psychosis superspectrum. Further research is needed to extend this model, such as clarifying whether mania and dissociation belong to thought disorder, and explicating processes that drive development of the spectra and their subdimensions. Compared to traditional diagnoses, the thought disorder and detachment spectra demonstrated substantially improved utility: greater reliability, larger explanatory and predictive power, and higher acceptability to clinicians. Validated measures are available to implement the system in practice. The more informative, reliable and valid characterization of psychosis-related psychopathology offered by HiTOP can make diagnosis more useful for research and clinical care.

Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders

A pivotal aim of psychiatric and neurological research is to promote the translation of the findings into clinical practice to improve diagnostic and prognostic assessment of individual patients. Structural neuroimaging holds much promise, with neuroanatomical measures accounting for up to 40% of the variance in clinical outcome. Building on these findings, a number of imaging-based clinical tools have been developed to make diagnostic and prognostic inferences about individual patients from their structural Magnetic Resonance Imaging scans. This systematic review describes and compares the technical characteristics of the available tools, with the aim to assess their translational potential into real-world clinical settings. The results reveal that a total of eight tools. All of these were specifically developed for neurological disorders, and as such are not suitable for application to psychiatric disorders. Furthermore, most of the tools were trained and validated in a single dataset, which can result in poor generalizability, or using a small number of individuals, which can cause overoptimistic results. In addition, all of the tools rely on two strategies to detect brain abnormalities in single individuals, one based on univariate comparison, and the other based on multivariate machine-learning algorithms. We discuss current barriers to the adoption of these tools in clinical practice and propose a checklist of pivotal characteristics that should be included in an "ideal" neuroimaging-based clinical tool for brain disorders.

Integrating the Hierarchical Taxonomy of Psychopathology (HiTOP) into clinical practice

OBJECTIVE

Diagnosis is a cornerstone of clinical practice for mental health care providers, yet traditional diagnostic systems have well-known shortcomings, including inadequate reliability, high comorbidity, and marked within-diagnosis heterogeneity. The Hierarchical Taxonomy of Psychopathology (HiTOP) is a data-driven, hierarchically based alternative to traditional classifications that conceptualizes psychopathology as a set of dimensions organized into increasingly broad, transdiagnostic spectra. Prior work has shown that using a dimensional approach improves reliability and validity, but translating a model like HiTOP into a workable system that is useful for health care providers remains a major challenge.

METHOD

The present work outlines the HiTOP model and describes the core principles to guide its integration into clinical practice.

RESULTS

Potential advantages and limitations of the HiTOP model for clinical utility are reviewed, including with respect to case conceptualization and treatment planning. A HiTOP approach to practice is illustrated and contrasted with an approach based on traditional nosology. Common barriers to using HiTOP in real-world health care settings and solutions to these barriers are discussed.

CONCLUSIONS

HiTOP represents a viable alternative to classifying mental illness that can be integrated into practice today, although research is needed to further establish its utility. (PsycINFO Database Record (c) 2019 APA, all rights reserved).