The formulation of a scientific opinion on whether the individual who committed a crime should be held responsible for his/her actions or should be considered not responsible by reason of insanity is very difficult. Indeed, forensic psychopathological decision on insanity is highly prone to errors and is affected by human cognitive biases, resulting in low inter-rater reliability. In this context, artificial intelligence can be extremely useful to improve the inter-subjectivity of insanity evaluation. In this paper, we discuss the possible applications of artificial intelligence in this field as well as the challenges and pitfalls that hamper the effective implementation of AI in insanity evaluation. In particular, thus far, it is possible to apply only supervised algorithms without knowing which is the ground truth and which data should be used to train and test the algorithms. In addition, it is not known which percentage of accuracy of the algorithms is sufficient to support partial or total insanity, nor which are the boundaries between sanity and partial or total insanity. Finally, ethical aspects have not been sufficiently investigated. We conclude that these pitfalls should be resolved before AI can be safely and reliably applied in criminal trials.

Remimazolam is a novel short-acting benzodiazepine. This study compared the effects of remimazolam and propofol on cognitive function in adult patients after surgery or other procedures.

We searched electronic databases, including PubMed, Embase, CENTRAL, Web of Science, and Scopus, for relevant studies. The primary outcome was the proportion of participants who experienced delirium or impaired cognitive function postoperatively. Secondary outcomes included the incidence of hypotension, bradycardia, and postoperative nausea and vomiting. We estimated the odds ratios (OR) and mean differences (MD) with 95% CIs using a random-effects model.

In total, 1295 patients from 11 randomized controlled trials were included. The incidence of postoperative delirium was 8.0% in the remimazolam group and 10.4% in the propofol group that was not significantly different (OR: 0.74, 95% CI [0.39-1.42], P = 0.369, I2 = 32%). More favorable cognitive function, as assessed using the Mini-Mental State Examination, was observed in the remimazolam group compared to the propofol group (MD: 1.06, 95% CI [0.32-1.80], P = 0.005, I2 = 89%). Remimazolam lowered the incidence of hypotension (OR: 0.28, 95% CI [0.21-0.37], P = 0.000, I2 = 0%) compared to propofol.

Remimazolam did not increase the risk of postoperative delirium and maintained cognitive function well, providing hemodynamic stability during surgery compared to propofol.

Large language models (LLMs) offer promising applications in mental health care to address gaps in treatment and research. By leveraging clinical notes and transcripts as data, LLMs could improve diagnostics, monitoring, prevention, and treatment of mental health conditions. However, several challenges persist, including technical costs, literacy gaps, risk of biases, and inequalities in data representation. In this Viewpoint, we propose a sociocultural-technical approach to address these challenges. We highlight five key areas for development: (1) building a global clinical repository to support LLMs training and testing, (2) designing ethical usage settings, (3) refining diagnostic categories, (4) integrating cultural considerations during development and deployment, and (5) promoting digital inclusivity to ensure equitable access. We emphasise the need for developing representative datasets, interpretable clinical decision support systems, and new roles such as digital navigators. Only through collaborative efforts across all stakeholders, unified by a sociocultural-technical framework, can we clinically deploy LLMs while ensuring equitable access and mitigating risks.

The Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5; American Psychiatric Association, 2013), includes 10 categorical personality disorders (PD) in Section II (Section II PD) and a dimensional alternative model of PD (AMPD) in Section III. We compared the two models in explaining concurrent psychosocial functioning levels in psychiatric outpatients and community residents screened as at risk for PD pathology (N = 600). The AMPD's fully dimensional form showed stronger associations with psychosocial difficulties and explained more of their variance compared with the categorical Section II PD. AMPD Criterion A (personality functioning impairment) and Criterion B (pathological traits) incrementally predicted psychosocial functioning about equally with some unique predictions. Finally, AMPD's six categorical PD diagnoses did not show stronger associations with psychosocial functioning than the corresponding Section II PD diagnoses. Findings directly comparing the two models remain important and timely for informing future conceptualizations of PD in the diagnostic system.

Methods to determine integrity of integrated neural systems engaged in functional processes have proven elusive. This study sought to determine the extent to which a machine learning retaliation classifier (retaliation vs unfair offer) developed from a sample of typically developing (TD) adolescents could be applied to an independent sample of clinically concerning youth and the classifier-determined functional integrity for retaliation was associated with antisocial behavior and proactive and reactive aggression.

Blood oxygen level-dependent response data were collected from 82 TD and 120 clinically concerning adolescents while they performed a retaliation task. The support vector machine algorithm was applied to the TD sample and tested on the clinically concerning sample (adolescents with externalizing and internalizing diagnoses).

The support vector machine algorithm was able to distinguish the offer from the retaliation phase after training in the TD sample (accuracy = 92.48%, sensitivity = 89.47%, and specificity = 93.18%) that was comparably successful in distinguishing function in the test sample. Increasing retaliation distance from the hyperplane was associated with decreasing conduct problems and proactive aggression.

The current study provides preliminary data of the importance of a retaliation endophenotype whose functional integrity is associated with reported levels of conduct problems and proactive aggression.

We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. We worked to ensure sex balance in the selection of non-human subjects. We worked to ensure diversity in experimental samples through the selection of the cell lines. We worked to ensure diversity in experimental samples through the selection of the genomic datasets. Diverse cell lines and/or genomic datasets were not available. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list. While citing references scientifically relevant for this work, we also actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our reference list. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work.

Early detection of depression is crucial because depression can lead to suicide if the symptoms are left unrecognized or untreated. In hospitals, self-administered questionnaires and interviews are employed to diagnose depression. Although doctors spend considerable time interviewing patients to understand their conditions, depression is a heterogeneous syndrome that makes accurate diagnosis challenging. Therefore, the biological aspects of depression must be investigated to address the limitations of traditional diagnostic methods. Audio data can be easily collected in daily life. Hence, we propose a multimodal fusion cross-modality model that applies audio and text to detect depression. The proposed model achieved F1-scores of 0.67, 0.81, and 0.61 on the Distress Analysis Interview Corpus, Emotional Audio and Textual Depression Corpus, and Korean Depression datasets. The model is designed to be lightweight, reducing the number of parameters while maintaining model accuracy with fewer parameters so that it can be employed in pervasive devices. We used English, Chinese, and Korean depression datasets to evaluate the performance of the proposed model across languages. The cross-language experiments confirm that the proposed model can be applied in other languages, even if the model is not trained in the same vocabulary. This finding suggests that the model has learned distinctive depression characteristics by combining nonlinguistic speech features and linguistic textual features. Therefore, this research is expected to detect depression in everyday life across languages and devices.

Adolescence is a period of change and increased mental health difficulties, which are important for lifetime outcomes. Adolescent mental health is therefore an active research area, with large samples often drawing on self-report general measures (ie, not disorder-specific or focused on a narrow outcome). We argue that these measures have a key role in our understanding of issues such as prevalence, antecedents, prevention, and intervention, however, measurement has been given little attention and high-quality measures do not tend to be available or used. We offer insights into historical and psychometric challenges that have contributed to current problems and highlight the implications of relying on poor measures, which at their worst can be biased and unethical. We make recommendations for research and practice on selecting measures and improving the evidence base and make a call to action to reject low-quality measurement in this field.

Laboratory studies show brain maturation involves synaptic pruning and cognitive development. Human studies suggest links between early cognitive performance and later mental health, but inconsistencies remain. It is unclear if specific brain regions mediate this relationship, and the molecular underpinnings are not well understood. Here, our longitudinal analyses in both the Adolescent Brain Cognitive Development and IMAGEN cohorts establish inverted U-shaped relationships between baseline executive function and subsequent symptom trajectories in the high-symptom individuals, whose externalizing (n = 963) or internalizing (n = 1762) symptoms exceed a clinical threshold at any point during the follow-up period, but not in the control group (n = 4291). Volumetric changes in the left lateral occipital cortex (LOC) mediated the relationship with externalizing symptoms (outwardly directed behaviors such as aggression), while changes in the right LOC and pars triangularis mediated the relationship with internalizing symptoms (inwardly directed emotional problems such as anxiety). Transcriptomic and genomic findings highlighted synaptic biology and particularly the gene ADCY1, which is implicated in synaptic pruning, as underlying both moderate executive function and its associated brain mediators. Notably, preadolescent cognitive performance predicts late-onset externalizing symptoms and remitting internalizing symptoms with high accuracies (area under the curve: 0.87 and 0.79). Our findings highlight the predictive value of cognitive performance for adolescent mental health trajectories, and indicate how this is mediated by specific brain regions, and underpinned by particular molecular pathways.

Unobtrusively collected objective sensor data from everyday devices like smartphones provide a novel paradigm to infer mental health symptoms. This process, called smart sensing, allows a fine-grained assessment of various features (eg, time spent at home based on the GPS sensor). Based on its prevalence and impact, depression is a promising target for smart sensing. However, currently, it is unclear which sensor-based features should be used in depression severity prediction and if they hold an incremental benefit over established fine-grained assessments like the ecological momentary assessment (EMA).

The aim of this study was to investigate various features based on the smartphone screen, app usage, and call sensor alongside EMA to infer depression severity. Bivariate, cluster-wise, and cluster-combined analyses were conducted to determine the incremental benefit of smart sensing features compared to each other and EMA in parsimonious regression models for depression severity.

In this exploratory observational study, participants were recruited from the general population. Participants needed to be 18 years of age, provide written informed consent, and own an Android-based smartphone. Sensor data and EMA were collected via the INSIGHTS app. Depression severity was assessed using the 8-item Patient Health Questionnaire. Missing data were handled by multiple imputations. Correlation analyses were conducted for bivariate associations; stepwise linear regression analyses were used to find the best prediction models for depression severity. Models were compared by adjusted R2. All analyses were pooled across the imputed datasets according to Rubin's rule.

A total of 107 participants were included in the study. Ages ranged from 18 to 56 (mean 22.81, SD 7.32) years, and 78% of the participants identified as female. Depression severity was subclinical on average (mean 5.82, SD 4.44; Patient Health Questionnaire score ≥10: 18.7%). Small to medium correlations were found for depression severity and EMA (eg, valence: r=-0.55, 95% CI -0.67 to -0.41), and there were small correlations with sensing features (eg, screen duration: r=0.37, 95% CI 0.20 to 0.53). EMA features could explain 35.28% (95% CI 20.73% to 49.64%) of variance and sensing features (adjusted R2=20.45%, 95% CI 7.81% to 35.59%). The best regression model contained EMA and sensing features (R2=45.15%, 95% CI 30.39% to 58.53%).

Our findings underline the potential of smart sensing and EMA to infer depression severity as isolated paradigms and when combined. Although these could become important parts of clinical decision support systems for depression diagnostics and treatment in the future, confirmatory studies are needed before they can be applied to routine care. Furthermore, privacy, ethical, and acceptance issues need to be addressed.

With the growing scholarly and clinical fascination with somatic symptom disorder (SSD), a bibliometric analysis is lacking.

To conduct a bibliometric analysis to investigate the current status and frontiers of SSD.

The documents related to SSD are obtained from the web of science core collection database (WoSCC), and VOSviewer 1.6.16 from January 1, 2000 to December 31, 2023, and the WoSCC's literature analysis wire were used to conduct the bibliometric analysis.

A total of 567 documents related to SSD were included, and 2325 authors across 947 institutions from 57 countries/regions have contributed to SSD research, published in 277 journals. The most productive author, institution, country and journal were Löwe B, University of Hamburg, Germany, and Journal of Psychosomatic Research respectively. The first high-cited document was published in the Journal of Psychosomatic Research in 2013 by Dimsdale JE and colleagues, which explored the rationale behind the SSD diagnosis introduction in diagnostic and statistical manual of mental disorders.

In conclusion, the main research hotspots and frontiers in the field of SSD are validity and reliability of the SSD criteria, functional impairment of SSD, and the treatment for SSD. More high-quality studies are needed to assess the diagnosis and treatment of SSD.

Major depressive disorder (MDD) is a debilitating and heterogeneous disease. Many MDD patients experience concurrent anxiety symptoms, often referred to as anxious depression (MDD-ANX). The relationships between network alterations in structural connectivity (SC) and functional connectivity (FC) in MDD and its anxiety-related subtype remain areas that require further investigation.

We investigated SC-FC coupling at the system and regional levels in 80 never-treated first-episode MDD patients and 80 healthy control (HC) subjects. For brain systems and regions showing significant between-group coupling differences, we further conducted subgroup comparisons between MDD-ANX, non-anxious depression (MDD-NANX) and HC. We also investigated topological features at the corresponding levels, and assessed the correlation patterns between significant coupling alterations and the topological and clinical characteristics.

Relative to HC, MDD patients showed increased SC-FC coupling in the temporal system (right hippocampus and left superior temporal gyrus [STG]) but decreased coupling in the parietal system (right postcentral gyrus and left angular gyrus). These systems and regions were further characterized by disturbed inter-module connections and impaired structural network efficiency in MDD. Notably, SC-FC coupling of the right hippocampus was significantly increased in MDD-ANX compared to MDD-NANX, which further showed distinct correlation patterns with structural network efficiency.

Alterations in both SC-FC coupling and topological properties in the temporal and parietal regions provide insights into the interplay between the structural and functional network abnormalities in MDD. SC-FC coupling alterations in the right hippocampus, associated with structural nodal efficiency, may be implicated in the neuropathology of anxious depression.

The use of machine learning to classify diagnostic cases versus controls defined based on diagnostic ontologies such as the International Classification of Diseases, Tenth Revision (ICD-10) from neuroimaging features is now commonplace across a wide range of diagnostic fields. However, transdiagnostic comparisons of such classifications are lacking. Such transdiagnostic comparisons are important to establish the specificity of classification models, set benchmarks, and assess the value of diagnostic ontologies.

We investigated case-control classification accuracy in 17 different ICD-10 diagnostic groups from Chapter V (mental and behavioral disorders) and Chapter VI (diseases of the nervous system) using data from the UK Biobank. Classification models were trained using either neuroimaging (structural or functional brain magnetic resonance imaging feature sets) or sociodemographic features. Random forest classification models were adopted using rigorous shuffle-splits to estimate stability as well as accuracy of case-control classifications. Diagnostic classification accuracies were benchmarked against age classification (oldest vs. youngest) from the same feature sets and against additional classifier types (k-nearest neighbors and linear support vector machine). In contrast to age classification accuracy, which was high for all feature sets, few ICD-10 diagnostic groups were classified significantly above chance (namely, demyelinating diseases based on structural neuroimaging features and depression based on sociodemographic and functional neuroimaging features).

These findings highlight challenges with the current disease classification system, leading us to recommend caution with the use of ICD-10 diagnostic groups as target labels in brain-based disease prediction studies.

Background: The integration of artificial intelligence (AI), particularly large language models (LLMs) like ChatGPT-4, is transforming healthcare. ChatGPT's potential to assist in decision-making for complex cases, such as spinal metastasis treatment, is promising but widely untested. Especially in cancer patients who develop spinal metastases, precise and personalized treatment is essential. This study examines ChatGPT-4's performance in treatment planning for spinal metastasis cases compared to experienced spine surgeons. Materials and Methods: Five spine metastasis cases were randomly selected from recent literature. Consequently, five spine surgeons and ChatGPT-4 were tasked with providing treatment recommendations for each case in a standardized manner. Responses were analyzed for frequency distribution, agreement, and subjective rater opinions. Results: ChatGPT's treatment recommendations aligned with the majority of human raters in 73% of treatment choices, with moderate to substantial agreement on systemic therapy, pain management, and supportive care. However, ChatGPT's recommendations tended towards generalized statements, with raters noting its generalized answers. Agreement among raters improved in sensitivity analyses excluding ChatGPT, particularly for controversial areas like surgical intervention and palliative care. Conclusions: ChatGPT shows potential in aligning with experienced surgeons on certain treatment aspects of spinal metastasis. However, its generalized approach highlights limitations, suggesting that training with specific clinical guidelines could potentially enhance its utility in complex case management. Further studies are necessary to refine AI applications in personalized healthcare decision-making.

Depression in male and female are commonly associated with different prevalence, severity, and, in some cases, distinct syndromes or subtypes. However, only a small amount of research has been conducted to completely understand the underlying neuroanatomical mechanisms. The goal of the current study was to provide neural markers for specific depression therapies by demonstrating the differences in aberrant prefrontal activity between male and female depressed subjects during an emotional autobiographical memory test. The study included 127 young adults who were randomly assigned to one of two groups: male depression (62 participants) or female depression (65 participants). The average oxyhemoglobin levels in the dorsolateral prefrontal cortex throughout the emotional autobiographical memory task were assessed utilizing 53-channel functional near-infrared spectroscopy imaging equipment. The oxy-Hb activation in the left dorsolateral prefrontal cortex (lDLPFC) and right dorsolateral prefrontal cortex (rDLPFC) had no significant interaction between groups and emotional valences. A significant main effect was found between male and female, with female depression groups showing lower oxy-Hb activity in lDLPFC and rDLPFC than male depression groups. Male and female depression patients showed distinct brain activation in the DLPFC during an emotional autobiographical memory test, suggesting potential specific neurological indicators for varied somatic symptoms in male and female depression patients. These distinctions should be taken into account while creating preventive measures.

Psychiatric disorders are a common cause of severe long-term disability and socioeconomic burden worldwide. Although our understanding of these disorders has advanced substantially over the last few years, little has changed the standards of care for these illnesses. Fast-spiking parvalbumin-positive interneurons (PVIs), a subpopulation of gamma-aminobutyric acid (GABA)ergic interneurons, are widely distributed in the hippocampus and have been reported to play an important role in various mental disorders. However, the mechanisms underlying the regulation of the molecular networks relevant to depression and schizophrenia (SCZ) are unknown. Here, we discuss the functions of PVIs in psychiatric disorders, including depression and SCZ. After reviewing several studies, we concluded that dysfunction in PVIs could cause depression-like behavior, as well as cognitive categories in SCZ, which might be mediated in large part by greater synaptic variability. In summary, this scientific review aims to discuss the current knowledge regarding the function of PVIs in depression and SCZ. Moreover, we highlight the importance of neurogenesis and synaptic plasticity in the pathogenesis of depression and SCZ, which seem to be mediated by PVIs activity. These findings provide a better understanding of the role of PVIs in psychiatric disorders.

The brain's complex distributed dynamics are typically quantified using a limited set of manually selected statistical properties, leaving the possibility that alternative dynamical properties may outperform those reported for a given application. Here, we address this limitation by systematically comparing diverse, interpretable features of both intra-regional activity and inter-regional functional coupling from resting-state functional magnetic resonance imaging (rs-fMRI) data, demonstrating our method using case-control comparisons of four neuropsychiatric disorders. Our findings generally support the use of linear time-series analysis techniques for rs-fMRI case-control analyses, while also identifying new ways to quantify informative dynamical fMRI structures. While simple statistical representations of fMRI dynamics performed surprisingly well (e.g., properties within a single brain region), combining intra-regional properties with inter-regional coupling generally improved performance, underscoring the distributed, multifaceted changes to fMRI dynamics in neuropsychiatric disorders. The comprehensive, data-driven method introduced here enables systematic identification and interpretation of quantitative dynamical signatures of multivariate time-series data, with applicability beyond neuroimaging to diverse scientific problems involving complex time-varying systems.

The past decade witnessed substantial discoveries related to the psychosis spectrum. Many of these discoveries resulted from pursuits of objective and quantifiable biomarkers in tandem with the application of analytical tools such as machine learning. These approaches provided exciting new insights that significantly helped improve precision in diagnosis, prognosis, and treatment. This article provides an overview of how machine learning has been employed in recent biomarker discovery research in the psychosis spectrum, which includes schizophrenia, schizoaffective disorders, bipolar disorder with psychosis, first episode psychosis, and clinical high risk for psychosis. It highlights both human and animal model studies and explores a varying range of the most impactful biomarkers including cognition, neuroimaging, electrophysiology, and digital markers. We specifically highlight new applications and opportunities for machine learning to impact noninvasive symptom monitoring, prediction of future diagnosis and treatment outcomes, integration of new methods with traditional clinical research and practice, and personalized medicine approaches.

Estimates of the occurrence of bipolar disorder among adolescents vary from country to country and from time to time. Long delays from first symptoms to diagnosis of bipolar disorder have been suggested. Studies among adults suggest increased mortality, particularly due to suicide and cardiovascular diseases. We set out to study the prognosis of adolescent onset bipolar disorder in terms of rehospitalizations, diagnostic stability, and mortality.

The study comprised a register-based follow-up of all adolescents admitted to psychiatric inpatient care for the first time in their lives at age 13-17 during the period 1980-2010. They were followed up in the National Care Register for Health Care and Causes of death registers until 31 December 2014.

Incidence of bipolar disorder among 13- to 17-year-old adolescents over the whole study period was 2.8 per 100, 000 same aged adolescents, and across decades, the incidence increased six-fold. Patients with bipolar disorder during their first-ever inpatient treatment were rehospitalized more often than those treated for other reasons. Conversion from bipolar disorder to other diagnoses was far more common than the opposite. Mortality did not differ between those firstdiagnosed with bipolar disorder and those treated for other reasons.

The incidence of adolescent onset bipolar disorder has increased across decades. The present study does not call for attention to delayed diagnosis of bipolar disorder. Adolescent onset bipolar disorders are severe disorders that often require rehospitalization, but diagnostic stability is modest. Mortality is comparable to that in other equally serious disorders.

Objective: In-office tympanostomy tube insertion (TTI) is becoming more available in the practice of pediatric otolaryngology. This study evaluated the tolerability of this procedure in young children. Method: Four methods were used to assess tolerability. (1) Standardized video recordings were collected from 30 patients under 24 months who underwent in-office TTI with a single-pass insertion device with topical anesthesia alone. The videos were then reviewed by 3 independent experts in pediatric care and rated at 5 procedural time points using a defined response scale developed with the U.S. Food and Drug Administration. (2) Separately, overall tolerability was subjectively assessed by the same experts. (3) Patient recovery was assessed by the participating otolaryngologist and support staff. (4) Patient caregivers were surveyed for their impressions of the procedure, including whether they would recommend it to other caregivers. Results: In a total of 90 reviews, 100% of children were successfully treated and were rated as having acceptably tolerated the in-office tympanostomy tube procedure. All patients returned to an acceptable baseline without inappropriate crying and were assessed as fully recovered immediately following the procedure or by the time the child was leaving clinic. For caregivers, 93% agreed or strongly agreed that they would recommend the use of in-office TTI to other caregivers. Conclusions: In-office TTI in young children was determined to be universally well tolerated in young children and is a procedure that patient caregivers would recommend to other caregivers for their children. These results should help reassure otolaryngology specialists and caregivers alike that in-office ear tube placement is a viable option for young children with middle ear disease.

Machine learning (ML) has developed classifiers differentiating patient groups despite concerns regarding diagnostic reliability. An alternative strategy, used here, is to develop a functional classifier (hyperplane) (e.g. distinguishing the neural responses to received reward v. received punishment in typically developing (TD) adolescents) and then determine the functional integrity of the response (reward response distance from the hyperplane) in adolescents with externalizing and internalizing conditions and its associations with symptom clusters.

Two hundred and ninety nine adolescents (mean age = 15.07 ± 2.30 years, 117 females) were divided into three groups: a training sample of TD adolescents where the Support Vector Machine (SVM) algorithm was applied (N = 65; 32 females), and two test groups- an independent sample of TD adolescents (N = 39; 14 females) and adolescents with a psychiatric diagnosis (major depressive disorder (MDD), generalized anxiety disorder (GAD), attention deficit hyperactivity disorder (ADHD) & conduct disorder (CD); N = 195, 71 females).

SVM ML analysis identified a hyperplane with accuracy = 80.77%, sensitivity = 78.38% and specificity = 88.99% that implicated feature neural regions associated with reward v. punishment (e.g. nucleus accumbens v. anterior insula cortices). Adolescents with externalizing diagnoses were significantly less likely to show a normative and significantly more likely to show a deficient reward response than the TD samples. Deficient reward response was associated with elevated CD, MDD, and ADHD symptoms.

Distinguishing the response to reward relative to punishment in TD adolescents via ML indicated notable disruptions in this response in patients with CD and ADHD and associations between reward responsiveness and CD, MDD, and ADHD symptom severity.

Psychiatry mainly deals with conditions that are mediated by brain function but are not directly attributable to specific brain abnormalities. Given the lack of concrete biological markers, such as laboratory tests or imaging results, the development of diagnostic systems is difficult.

This narrative review evaluated 9 diagnostic approaches. The validity of the DSM and the International Classification of Disorders (ICD) is limited. The Research Domain Criteria is a research framework, not a diagnostic system. The clinical utility of the quantitatively derived, dimensional Hierarchical Taxonomy of Psychopathology is questionable. The Psychodynamic Diagnostic Manual Version 2 follows psychoanalytic theory and focuses on personality. Unlike the personality assessments in ICD-11 or DSM-5's alternative model, based on pathological extremes of the big 5 traits (extraversion, agreeableness, openness, conscientiousness, and neuroticism), it lacks foundation in empirical evidence. Network analytic approaches are intriguing, but their complexity makes them difficult to implement. Staging would be easier if individually predictive biological markers were available. The problem with all these new approaches is that they abstract patient experiences into higher-order constructs, potentially obscuring individual symptoms so much that they no longer reflect patients' actual problems.

ICD and DSM diagnoses can be questioned, but the reality of psychopathological symptoms, such as hallucinations, depression, anxiety, compulsions, and the suffering stemming from them, cannot. Therefore, it may be advisable to primarily describe patients according to the psychopathological symptoms they present, and any resulting personal syndromes, embedded in a framework of contextual clinical characterization including personality assessment and staging. The DSM and ICD are necessary for reimbursement, but they should be simplified and merged. A primarily psychopathological symptoms-based, clinical characterization approach would be multidimensional and clinically useful, because it would lead to problem-oriented treatment and support transdiagnostic research. It should be based on a universally used instrument to assess psychopathology and structured clinical characterization.

Depression is a heterogenous diagnostic construct; however, dynamic interactions between specific depressive symptoms across their qualitatively different profiles remain largely unknown. The study aimed to recognize the most prevalent profiles of depressive symptoms and assess their dynamics in young adults without a history of psychiatric treatment. Depressive symptoms were recorded using the Patient Health Questionnaire-9 (PHQ-9). The data were assessed for all theoretical and empirical combinations of depressive symptoms in participants with a positive screening for depression. The profiles identified in the majority of participants were analyzed using partial correlation and Bayesian networks. Data from 3583 individuals with a positive screening for depression were analyzed. Out of 382 theoretical profiles, 150 profiles (39.3%) were present in this dataset. The majority of participants (56.8%) showed 4 profiles of depressive symptoms including the profile with all depressive symptoms present, the profile without suicidal ideation, the profile without psychomotor impairment, and the profile without both psychomotor impairment and suicidal ideation. The profiles differed largely in terms of their dynamics and symptoms that are necessary to activate the whole network. The network characteristics within specific profiles did not differ significantly across the level of difficulties attributable to depressive symptoms. Our findings indicate that depression emerging in young adults shows a limited number of symptom profiles. However, dynamics of depressive symptoms differs largely between specific profiles regardless of functional impairment indicating the need to personalize therapeutic approaches. Future studies should further disentangle the heterogeneity of depressive symptoms, e.g., by dissecting the symptoms that are combined together by single PHQ-9 items (i.e., hypersomnia and insomnia; psychomotor agitation and retardation).

Research into the brain basis of psychopathology is challenging due to the heterogeneity of psychiatric disorders, extensive comorbidities, underdiagnosis or overdiagnosis, multifaceted interactions with genetics and life experiences, and the highly multivariate nature of neural correlates. Therefore, increasingly larger datasets that measure more variables in larger cohorts are needed to gain insights. In this review, we present current "best practice" approaches for using existing databases, collecting and sharing new repositories for big data analyses, and future directions for big data in neuroimaging and psychiatry with an emphasis on contributing to collaborative efforts and the challenges of multi-study data analysis.

The Hierarchical Taxonomy of Psychopathology (HiTOP) is a dimensional framework for psychopathology advanced by a consortium of nosologists. In the HiTOP system, psychopathology is grouped hierarchically from super-spectra, spectra, and subfactors at the upper levels to homogeneous symptom components and maladaptive traits and their constituent symptoms, and maladaptive behaviors at the lower levels. HiTOP has the potential to improve clinical outcomes by planning treatment based on symptom severity rather than heterogeneous diagnoses, targeting treatment across different levels of the hierarchy, and assessing distress and impairment separately from the observed symptom profile. Assessments can be performed according to this framework with the recently developed HiTOP-Self-Report (HiTOP-SR). Examples of how to use HiTOP in clinical practice are provided for the internalizing spectrum, including the use of the Unified Protocol and other modularized treatments, measurement-based care, psychopharmacology, and in traditionally underserved populations. Future directions are discussed in this State of the Science review including HiTOP's use in further developing transdiagnostic treatments, extending the model to include other information such as environmental factors, establishing the treatment utility of clinical assessment for the HiTOP-SR, developing new treatments, and disseminating the model.

Researchers in ubiquitous computing have long promised that passive sensing will revolutionize mental health measurement by detecting individuals in a population experiencing a mental health disorder or specific symptoms. Recent work suggests that detection tools do not generalize well when trained and tested in more heterogeneous samples. In this work, we contribute a narrative review and findings from two studies with 41 mental health clinicians to understand these generalization challenges. Our findings motivate research on actionable sensing, as an alternative to detection research, studying how passive sensing can augment traditional mental health measures to support actions in clinical care. Specifically, we identify how passive sensing can support clinical actions by revealing patients' presenting problems for treatment and identifying targets for behavior change and symptom reduction, but passive data requires additional contextual information to be appropriately interpreted and used in care. We conclude by suggesting research at the intersection of actionable sensing and mental healthcare, to align technical research in ubiquitous computing with clinical actions and needs.

In a perfect world, scientists would develop analyses that are guaranteed to reveal the ground truth of a research question. In reality, there are countless viable workflows that produce distinct, often conflicting, results. Although reproducibility places a necessary bound on the validity of results, it is not sufficient for claiming underlying validity, eventual utility, or generalizability. In this work we focus on how embracing variability in data analysis can improve the generalizability of results. We contextualize how design decisions in brain imaging can be made to capture variation, highlight examples, and discuss how variability capture may improve the quality of results.

Restoration of the anterior knee compartment is increasingly studied with the development of personalized surgery. However, evaluating the patellar tracking during the surgery is still subjective and at the surgeon's discretion. This study aimed 1) to describe the assessment of the patellar tracking during robotic-assisted total knee arthroplasty (TKA), 2) to describe a new measurement technique for evaluating the evolution of this patellar tracking, and 3) to assess its reliability and repeatability.

This monocentric study assessed the evolution of patellar tracking for 20 robotic-assisted TKA. The sharp probe was used to perform patellar tracking in all the arcs of knee flexion before and after the bone cuts. The patella positioning was recorded every 10° of flexion between the full extension and 90° knee flexion and was assessed in the coronal and sagittal planes. For the measurements of the patellar tracking, we used a sagittal view and a coronal view of the knee on the MAKO software. From these two views, the difference between the patellar tracking before and after the bone cuts with the definitive implants was measured. Two independent reviewers performed the measurements to assess their reliability. To determine intraobserver variability, the first observer performed the measurements twice.

The mean age was 68.7 years old ± 5.2 [61; 75], the mean body mass index was 28.8 kg/m2 ± 4.2 [21.4; 36.2], the mean HKA angle was 176.3° ± 3.7° [174.1.4; 179.7]. The radiographic measurements showed very good to excellent intra-observer and inter-observer agreements (0.60 to 1.0).

This new measurement technique assessed the evolution of patellar tracking after TKA with good inter and intra-observer reliability.

Depression is a pervasive mental health condition, particularly affecting older adults, where early detection and intervention are essential to mitigate its impact. This study presents an explainable multi-layer dynamic ensemble framework designed to detect depression and assess its severity, aiming to improve diagnostic precision and provide insights into contributing health factors.

Using data from the National Social Life, Health, and Aging Project (NSHAP), this framework combines classical machine learning models, static ensemble methods, and dynamic ensemble selection (DES) approaches across two stages: detection and severity prediction. The depression detection stage classifies individuals as normal or depressed, while the severity prediction stage further classifies depressed cases as mild or moderate-severe. Finally, a confirmation depression scale prediction model estimates depression severity scores to support the two stages. Explainable AI (XAI) techniques are applied to improve model interpretability, making the framework more suitable for clinical applications.

The framework's FIRE-KNOP DES algorithm demonstrated high efficacy, achieving 88.33% accuracy in depression detection and 83.68% in severity prediction. XAI analysis identified mental and non-mental health indicators as significant factors in the framework's performance, emphasizing the value of these features for accurate depression assessment.

This study emphasizes the potential of dynamic ensemble learning in mental health assessments, particularly in detecting and evaluating depression severity. The findings provide a strong foundation for future use of dynamic ensemble frameworks in mental health assessments, demonstrating their potential for practical clinical applications.

Four-corner arthrodesis is commonly performed for advanced collapse patterns of wrist arthritis. Reduction of the capitolunate (CL) angle during four-corner fusion is crucial to allow for the greatest radiocarpal joint arc of motion. Previous studies demonstrate variable inter- and intraobserver reliability of measuring the CL angle. However, in a four-corner fusion, hardware implementation and scaphoid excision can complicate carpal alignment measurements. The purpose of this study is to investigate inter- and intraobserver reliability of measuring carpal alignment parameters following scaphoid excision and four-corner arthrodesis.

Three fellowship-trained orthopaedic hand surgeons evaluated 30 posteroanterior and lateral radiographs of wrists after scaphoid excision and four-corner fusion. Radiographic evaluation included analysis of the radiolunate angle (RL), CL angle, lunate posture, carpal height, carpal height ratio, hardware impingement, and arthrodesis technique. Intraclass correlation coefficients (ICCs) and kappa values were used to evaluate reliability of radiographic measurements.

RL and CL angles demonstrated very good inter- (ICCs: 0.657 and 0.693, respectively) and intraobserver agreement (ICCs: 0.576 to 0.924 and 0.596 to 0.811, respectively). Hardware impingement metrics by dorsal prominence and radiocarpal prominence had excellent interobserver reliability of 0.821 and 0.803, respectively. ICC values for arthrodesis technique were equal to 1.00. The inter- and intraobserver ICC values for the number of screws/staples used were in excellent agreement ranging from 0.910 to 1.000.

Our study demonstrated favorable intra- and interobserver reliability at assessing carpal alignment following scaphoid excision and four-corner arthrodesis and these metrics potentially could be used in future research to evaluate long-term surgical outcomes.

Level III, retrospective cohort study.

Psychiatric clinical diagnostic formulation has evolved over time. The changes alter our understanding and our ability to provide a public health perspective on the epidemiology of mental disorders in large populations. Epidemiology is an important perspective and set of tools to assess prevalence, treated prevalence, untreated prevalence, individual risks for mental disorders, and possible links to the etiology of disorders by following the trails of environmental exposures, biological measures, interpersonal dynamics, and genetic risk factors. As communities develop health-care services to treat individuals with mental disorders, knowledge about their prevalence and treatment requirements is also important. Since severe mental disorders may require institutional care, the diagnostic criteria used may either protect an individual's liability for dangerous behavior (i.e. the insanity defense) or be used to control political and social dissidents. The criteria may also be used to determine evidence-based treatment options and eligibility for disability benefits. In this paper, using my vantage points as a physician scientist and public health officer, with leadership positions in national federal and professional mental health organizations, I address the developments in these areas over the past 50 years that have influenced institutional positions in the U.S. National Institute of Mental Health, the World Health Organization, the American Psychiatric Association, and the Center for the Study of Traumatic Stress. These perspectives may aid the next generation of investigators to advance the epidemiological and mental disorder classification scientific fields.

Nonsuicidal self-injury disorder (NSSI-D) is presently a condition for further study in the Diagnostic & Statistical Manual of Mental Disorders (DSM-5-TR) (American Psychiatric Association, 2022). While previous studies focused on NSSI-D through the lens of experts, there is a shortage of research that explores the potential impact of NSSI-D from the perspective of those most directly affected - individuals with NSSI lived experience. The present study aimed to expand this limited literature and provide a more enhanced and nuanced understanding of the potential implications and consequences of NSSI-D from lived experience viewpoints. Adults with lifetime NSSI history (N = 38) completed a semi-structured interview that surveyed perspectives on NSSI-D, including the meaning of officially recognizing NSSI-D, potential impacts of receiving an NSSI-D diagnosis, and the impact of NSSI-D on one's decision to speak with a clinician. Results from our thematic analysis of the interview data suggested opinions about NSSI-D and its potential impact were generally positive (e.g., increased awareness, understanding, and validation, increased comfort with disclosing NSSI behavior, positive impacts on treatment, and improved functioning). Participants also highlighted potential concerns about the potential consequences of NSSI-D (e.g., negative self-perception, increased stigma, and concerns about the necessity and utility of NSSI-D), and some expressed neutral/indifferent opinions about NSSI-D. Overall, results provide valuable insights regarding potential implications and consequences of official recognition and diagnosis of NSSI-D and have relevant implications for client-clinician interactions. Results also highlight the importance and value of amplifying lived experience perspectives.

Experiences of intimate partner violence (IPV) are associated with a wide range of measures of poor mental health. While there is a body of evidence to suggest that IPV leads to poor mental health, there is some evidence to suggest the association between IPV and mental illness may be bi-directional. We take data from a long running cohort study to test the hypothesis that poor mental health experienced during the adolescent and young adult periods of the life course predict adult occurring IPV. At 14 years respondents were administered the Youth Self Report (YSR), and at 21 years they completed the Young Adult Self Report (YASR) as well as the Composite International Diagnostic Interview. At 30 years, respondents completed the Composite Abuse Scale (CAS), with five measures of IPV; Severe Combined Abuse, Physical Abuse, Emotional Abuse, Harassment and Coercive Control. After adjustment for possible confounding, measures of delinquency and substance use disorder at 21 years predicted all forms of IPV. For example, in the fully adjusted data, substance use disorders to 21 years predict Severe Combined Abuse (2.30:1.15, 4.61), Physical Abuse (1.67:1.11, 2.52), and Coercive Control (1.74:1.14, 2.65) at 30 years. The findings raise the possible benefits of early intervention programs to reduce adult occurring IPV.

More personalized alignment techniques in total knee arthroplasty (TKA) have recently been described particularly for the young and active patients. Performing the ideal tibial cut might be challenging with a conventional ancillary. Therefore the aims of this study were: (1) to describe specific tibial landmarks to optimize the tibial cut in TKA; (2) to compare the accuracy of the tibial cut with these landmarks compared to a conventional technique.

This retrospective case-control study compared primary TKAs performed using a conventional technique with extramedullary guide associated with specific tibial landmarks. For each case, one control patient was matched based on body mass index (BMI), age, preoperative Hip Knee Ankle (HKA) angle, and Medial Proximal Tibial Angle (MPTA). All control patients were operated by the same surgeon and similar conventional technique but without landmarks. The MPTA target was to reproduce preoperative deformity with a 3° of varus limit. 34 TKA were included in each group. There was no preoperative difference between both groups. Mean age was 63 years old ± 8. Mean BMI was 32 kg/m2 ± 5. Mean HKA was 170.6° ± 2.5. Mean MPTA was 85.1° ± 2.3. The radiographic assessment was performed preoperatively and at 2 months: HKA, mechanical Medial Distal Femoral Angle (mMDFA), MPTA, tibial slope, restoration of the joint line-height.

The tibial landmarks corresponded to the line of insertion of the deep medial collateral ligament fibers extended to the capsular insertion above the Gerdy tubercle. The postoperative MPTA was significantly more varus (87.2° ± 1.6 in landmark group versus 88.3° ± 2.2; p = 0.027) and closer to preoperative bone deformity in landmark group (p = 0.002) with significantly less outliers than in the conventional group. There was no significant difference between both groups postoperatively for HKA (175.4° ± 2.3 versus 175.9° ± 2.5; p = 0.42); mMDFA (88.9° ± 2.3 versus 88.2° ± 2.1; p = 0.18); tibial slope (82.6° ± 1.9 versus 82.4° ± 2.6; p = 0.67), the restoration of the joint line-height (1.5 mm ± 2 versus 1.8 mm ± 2; p = 0.56).

Specific tibial landmarks during TKA can be used to increase the accuracy of the tibial cut when using personalized alignment techniques in TKA.

IV.

Mental health for cancer survivors in both research and clinical applications has strongly adopted a traditional nosological approach, involving the classification of psychopathology into discrete disorders. However, this approach has recently faced considerable criticism due to issues such as high comorbidity and within-disorder symptom heterogeneity across populations. Moreover, there are additional specific issues impacting the validity of traditional approaches in cancer survivorship populations, including the physiological effects of cancer and its treatments. In response, we provide the case for the hierarchical dimensional approach within psycho-oncology, in particular the Hierarchical Taxonomy of Psychopathology (HiTOP). We discuss not only the potential utility of HiTOP to research and clinical applications within psycho-oncology, but also its limitations, and what is required to apply this approach within cancer survivorship.

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.

Introduction: This study explores using Neural Ordinary Differential Equations (NODEs) to analyze hormone dynamics in the hypothalamicpituitary-adrenal (HPA) axis during Trier Social Stress Tests (TSST) to classify patients with Major Depressive Disorder (MDD). Methods: Data from TSST were used, measuring plasma ACTH and cortisol concentrations. NODE models replicated hormone changes without prior knowledge of the stressor. The derived vector fields from NODEs were input into a Convolutional Neural Network (CNN) for patient classification, validated through cross-validation (CV) procedures. Results: NODE models effectively captured system dynamics, embedding stress effects in the vector fields. The classification procedure yielded promising results, with the 1x1 CV achieving an AUROC score that correctly identified 83% of Atypical MDD patients and 53% of healthy controls. The 2x2 CV produced similar outcomes, supporting model robustness. Discussion: Our results demonstrate the potential of combining NODEs and CNNs to classify patients based on disease state, providing a preliminary step towards further research using the HPA axis stress response as an objective biomarker for MDD.

Psychopathology, including depression, anxiety, and post-traumatic stress, is a significant yet inadequately addressed feature of moderate-severe traumatic brain injury (TBI). Progress in understanding and treating post-TBI psychopathology may be hindered by limitations associated with conventional diagnostic approaches, specifically the Diagnostic and Statistical Manual of Mental Disorders (DSM) and International Classification of Diseases (ICD). The Hierarchical Taxonomy of Psychopathology (HiTOP) offers a promising, transdiagnostic alternative to psychiatric classification that may more effectively capture the experiences of individuals with TBI. However, HiTOP lacks validation in the TBI population. To address this gap, we administered a comprehensive questionnaire battery, including 56 scales assessing homogeneous symptom components and maladaptive traits within HiTOP, to 410 individuals with moderate-severe TBI. We evaluated the reliability and unidimensionality of each scale and revised those with psychometric problems. Using a top-down, exploratory latent variable approach (bass-ackwards modeling), we subsequently constructed a hierarchical model of psychopathological dimensions tailored to TBI. The results showed that, relative to norms, participants with moderate-severe TBI experienced greater problems in the established HiTOP internalizing and detachment spectra, but fewer problems with thought disorder and antagonism. Fourteen of the 56 scales demonstrated psychometric problems, which often appeared reflective of the TBI experience and associated disability. The Hierarchical Taxonomy of Psychopathology Following Traumatic Brain Injury (HiTOP-TBI) model encompassed broad internalizing and externalizing spectra, splitting into seven narrower dimensions: Detachment, Dysregulated Negative Emotionality, Somatic Symptoms, Compensatory and Phobic Reactions, Self-Harm and Psychoticism, Rigid Constraint, and Harmful Substance Use. This study presents the most comprehensive empirical classification of psychopathology after TBI to date. It introduces a novel, TBI-specific transdiagnostic questionnaire battery and model, which addresses the limitations of conventional DSM and ICD diagnoses. The empirical structure of psychopathology after TBI largely aligned with the established HiTOP model (e.g., a detachment spectrum). However, these constructs need to be interpreted in relation to the unique experiences associated with TBI (e.g., considering the injury's impact on the person's social functioning). By overcoming the limitations of conventional diagnostic approaches, the HiTOP-TBI model has the potential to accelerate our understanding of the causes, correlates, consequences, and treatment of psychopathology after TBI.

Research on the Alternative DSM-5 Model for Personality Disorders (AMPD) in DSM-5's Section-III has demonstrated acceptable interrater reliability, a largely consistent latent structure, substantial correlations with theoretically and clinically relevant measures, and evidence for incremental concurrent and predictive validity after controlling for DSM-5's Section II categorical personality disorders (PDs). However, the AMPD is not yet widely used clinically. One clinician concern may be caseness - that the new model will diagnose a different set of PD patients from that with which they are familiar. The primary aim of this study is to determine whether this concern is valid, by testing how well the two models converge in terms of prevalence and coverage.

Participants were 305 psychiatric outpatients and 302 community residents not currently in mental-health treatment who scored above threshold on the Iowa Personality Disorder Screen (Langbehn et al., ). Participants were administered a semi-structured interview for DSM-5 PD, which was scored for both Section II and III PDs.

Convergence across the two PD models was variable for specific PDs, Good when specific PDs were aggregated, and Very Good for 'any PD.'

Results provide strong evidence that the AMPD yields the same overall prevalence of PD as the current model and, further, identifies largely the same overall population. It also addresses well-known problems of the current model, is more consistent with the ICD-11 PD model, and provides more complete, individualized characterizations of persons with PD, thereby offering multiple reasons for its implementation in clinical settings.

To conduct a bibliometric network analysis to explore the research landscape of immediate implant placement (IIP) and provide insights into its trends and contributors.

The Scopus database was utilized as the bibliographic source, and a search strategy was implemented to identify relevant research articles. Various bibliometric parameters were extracted, including publication year, journal, authors, citations, and funding. The analysis involved examining authorship patterns, international collaborations, level of evidence, Altmetric data, and funding analysis.

We identified a steady annual growth rate of 6.49% in IIP research. The top three countries contributing to research output were the USA, Italy, and China. Prolific authors were identified based on publication and citation metrics. International collaborations among different countries were observed. The level of evidence analysis revealed that over 30% of the articles fell into higher levels of evidence (levels 1 and 2). Altmetric data analysis indicated no significant correlations between citation counts and Altmetric Attention Score (AAS), and conversely a significant association with Mendeley readers count. Funding and open access did not significantly impact the bibliometric indices of the papers.

The focus of research on IIP has been evolving as indicated by an exponential growth rate in this study. Only approximately 16% of the articles fit into level 1 evidence, therefore, emphasizing on higher quality level research study shortage in this field. Modern indices can be used as new bibliometric indicators as they also cover social media and online attention scores.

Diaphragmatic sleep disordered breathing (dSDB) has been recently identified as sleep dysfunction secondary to diaphragmatic weakness in Duchenne muscular dystrophy (DMD). However, scoring criteria for the identification of dSDB are missing.This study aimed to define and validate dSDB scoring criteria and to evaluate whether dSDB severity correlates with respiratory progression in DMD.

Scoring criteria for diaphragmatic apnoea (dA) and hypopnoeas (dH) have been defined by the authors considering the pattern observed on cardiorespiratory polygraphy (CR) and the dSDB pathophysiology.10 sleep professionals (physiologists, consultants) blinded to each other were involved in a two-round Delphi survey to rate each item of the proposed dSDB criteria (Likert scale 1-5) and to recognise dSDB among other SDB. The scorers' accuracy was tested against the authors' panel.Finally, CR previously conducted in DMD in clinical setting were rescored and diaphragmatic Apnoea-Hypopnoea Index (dAHI) was derived. Pulmonary function (forced vital capacity per cent of predicted, FVC%pred), overnight oxygen saturation (SpO2) and transcutaneous carbon dioxide (tcCO2) were correlated with dAHI.

After the second round of Delphi, raters deemed each item of dA and dH criteria as relevant as 4 or 5. The agreement with the panel in recognising dSDB was 81%, kappa 0.71, sensitivity 77% and specificity 85%.32 CRs from DMD patients were reviewed. dSDB was previously scored as obstructive. The dAHI negatively correlated with FVC%pred (r=-0.4; p<0.05). The total number of dA correlated with mean overnight tcCO2 (r 0.4; p<0.05).

dSDB is a newly defined sleep disorder that correlates with DMD progression. A prospective study to evaluate dSDB as a respiratory measure for DMD in clinical and research settings is planned.

The brain's complex distributed dynamics are typically quantified using a limited set of manually selected statistical properties, leaving the possibility that alternative dynamical properties may outperform those reported for a given application. Here, we address this limitation by systematically comparing diverse, interpretable features of both intra-regional activity and inter-regional functional coupling from resting-state functional magnetic resonance imaging (rs-fMRI) data, demonstrating our method using case-control comparisons of four neuropsychiatric disorders. Our findings generally support the use of linear time-series analysis techniques for rs-fMRI case-control analyses, while also identifying new ways to quantify informative dynamical fMRI structures. While simple statistical representations of fMRI dynamics performed surprisingly well (e.g., properties within a single brain region), combining intra-regional properties with inter-regional coupling generally improved performance, underscoring the distributed, multifaceted changes to fMRI dynamics in neuropsychiatric disorders. The comprehensive, data-driven method introduced here enables systematic identification and interpretation of quantitative dynamical signatures of multivariate time-series data, with applicability beyond neuroimaging to diverse scientific problems involving complex time-varying systems.

The aim of this study is to consider limitations to the heuristics 'treatment-resistant depression' (TRD) and 'difficult-to-treat' depression (DTD) and to offer a revisionist model.

A number of limitations to the two constructs are noted, particularly the risk of each positioning clinical depression as an entity and then applying a linear sequencing management model.

Arguing that clinical depression is heterogenous in nature (with categorical and 'fuzzy set conditions), in cause and in response to treatment, allows an alternate model for addressing depressive conditions that are not readily responsive to treatment. A skeletal model for proceeding is offered for consideration and development.

If such a model is accepted, then differing criteria for defining treatment resistance and treatment failure might be generated for differing depressive conditions, and condition-specific sequencing algorithms (embracing drug and non-drug strategies) developed for their management.

Studying empathy in borderline personality disorder (BPD) is essential because difficulties with interpersonal functioning are integral.

This scoping and narrative review explores the aetiological theory that BPD is an innate anomaly of cognitive empathy, with a normal or heightened emotional empathy.

Ovid MEDLINE(R) ALL was searched using the terms empathy; theory of mind; mentalisation or mentalising; borderline empathy; emotion recognition and BPD. For inclusion in the scoping review, articles needed to empirically assess an empathic skill in people with BPD, or self-reported empathy in a BPD group compared to controls, or empathic skill as a 'borderline feature' in a nonclinical sample.

The results of empirical studies were categorised as per their methodological approach, with results in the BPD group reported as comparable, enhanced or reduced compared to controls.

320 articles were returned, with 38 eligible. The majority affirmed that people with BPD have an anomalous empathetic ability, especially a deficient cognitive empathy. Furthermore, this is trait, evident early in development, correlates with syndrome severity, and is mediated by atypical neural networks.

This substantiates the theory that BPD is, at least in major part, an innate empathy anomaly.