[The German research network for mental disorders]

Motion Artifact Detection for T1-Weighted Brain MR Images Using Convolutional Neural Networks

Quality assessment (QA) of magnetic resonance imaging (MRI) encompasses several factors such as noise, contrast, homogeneity, and imaging artifacts. Quality evaluation is often not standardized and relies on the expertise, and vigilance of the personnel, posing limitations especially with large datasets. Machine learning based on convolutional neural networks (CNNs) is a promising approach to address these challenges by performing automated inspection of MR images. In this study, a CNN for the detection of random head motion artifacts (RHM) in T1-weighted MRI as one aspect of image quality is proposed. A two-step approach aimed to first identify images exhibiting pronounced motion artifacts, and second to evaluate the feasibility of a more detailed three-class classification. The utilized dataset consisted of 420 T1-weighted whole-brain image volumes with isotropic resolution. Human experts assigned each volume to one of three classes of artifact prominence. Results demonstrate an accuracy of 95% for the identification of images with pronounced artifact load. The addition of an intermediate class retained an accuracy of 76%. The findings highlight the potential of CNN-based approaches to increase the efficiency of post-hoc QAs in large datasets by flagging images with potentially relevant artifact loads for closer inspection.

The associations of Positive and Negative Valence Systems, Cognitive Systems and Social Processes on disease severity in anxiety and depressive disorders

Background

Anxiety and depressive disorders share common features of mood dysfunctions. This has stimulated interest in transdiagnostic dimensional research as proposed by the Research Domain Criteria (RDoC) approach by the National Institute of Mental Health (NIMH) aiming to improve the understanding of underlying disease mechanisms. The purpose of this study was to investigate the processing of RDoC domains in relation to disease severity in order to identify latent disorder-specific as well as transdiagnostic indicators of disease severity in patients with anxiety and depressive disorders.

Methods

Within the German research network for mental disorders, 895 participants (n = 476 female, n = 602 anxiety disorder, n = 257 depressive disorder) were recruited for the Phenotypic, Diagnostic and Clinical Domain Assessment Network Germany (PD-CAN) and included in this cross-sectional study. We performed incremental regression models to investigate the association of four RDoC domains on disease severity in patients with affective disorders: Positive (PVS) and Negative Valance System (NVS), Cognitive Systems (CS) and Social Processes (SP).

Results

The results confirmed a transdiagnostic relationship for all four domains, as we found significant main effects on disease severity within domain-specific models (PVS: β = -0.35; NVS: β = 0.39; CS: β = -0.12; SP: β = -0.32). We also found three significant interaction effects with main diagnosis showing a disease-specific association.

Limitations

The cross-sectional study design prevents causal conclusions. Further limitations include possible outliers and heteroskedasticity in all regression models which we appropriately controlled for.

Conclusion

Our key results show that symptom burden in anxiety and depressive disorders is associated with latent RDoC indicators in transdiagnostic and disease-specific ways.

Traces of health-A landscape design task as a diagnostic aid for detecting mental burden? A qualitative focus group study

Background

Mental disorders are most common causes of illness worldwide. Studies on art and drawing tasks, such as the tree-drawing test have already proven their prognostic quality for the diagnosis of Alzheimer's disease, depression or trauma. In the depiction of art in public space, gardens and landscapes are one of the oldest human forms of artistic expression. This study thus aims at exploring the impact of a landscape design task as a prognostic tool to detect mental burden.

Materials and methods

A total of 15 individuals (eight female) aged between 19 and 60 years completed the Brief Symptom Inventory BSI-18 and the State Trait Anxiety Inventory (STAI-S) before being asked to design a landscape in a 3 × 3 m squared area. Material to be used included plants, flowers, branches, and stones. The complete process of landscape design was videotaped and the tapes were analyzed in a two-step focus group analysis from a group of gardening trainees, psychology students and students of arts therapies. Results were condensed in a second step into major categories.

Results

Scores of the BSI-18 showed a range of 2-21 points and STAI-S scores ranged between 29 and 54 points indicating a light to moderate mental burden. Focus group participants identified three mutually perpendicular major components associated with mental health: "Movement and Activity," "Material Selection and Design," and "Connectedness to the task." In a subsample of the three least and three most mentally stressed subjects (based on their GSI and STAI-S scorings), clear differences were found in body posture, action planning and the choice of material and aspects of design.

Discussion

In addition to the well-known therapeutic potential of gardening, this study for the first time showed that gardening and landscape design contains diagnostic elements. Our preliminary findings are in coherence with similar research indicating a high association of movement and design patterns with mental burden. However, due to the pilot nature of the study, the results should be interpreted cautiously. Based on the findings further studies are currently planned.

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

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

[Potentials and limits of aging cohort studies for geriatric psychiatry]

The sizeable number of population-based cohort studies of aging in Germany have provided highly valuable contributions for the specification of risk factors and predictors for frequent mental disorders in old age, especially dementia and depression. The results from these cohort studies enable the specification of mechanisms for the development of and preventative interventions for common mental disorders in old age. On the other hand, there is a significant paucity of clinical cohort studies investigating disease trajectories and possible markers for specific individualized interventions of frequent mental disorders in old age. In this article, we report selected key findings from cohort studies of aging and discuss novel approaches for the integration and harmonization of population-based and clinical cohort studies.

[Biobanking in Psychiatry]

Medical biobanking is concerned with establishing and maintaining large-scale repositories of biological specimens combined with comprehensive archives of clinical and biographical information on donors. This aims for controlled high and consistent quality of specimens for future biomedical research. One major objective is to assemble multiple blood components for various types of biochemical analysis and experimentation including different isolated cell types. With proper cryo-conservation, blood-derived cells can be conserved and revitalized after thawing and employed as in-vitro cell models carrying specific biological traits of donors. Optimizing pre-analytical methods can reduce pre-analytical variance thereby reducing imprecision of analytical data. This is particularly valuable for multivariate analyses of biological systems ("omics") and biomarker research. Introducing biobanking to psychiatry carries the challenge of making diagnostic allocation more compatible with biological entities than is achieved with current diagnostic categories of ICD-10 or DSM-V. Diagnostic or transdiagnostic subgroups can be stratified using biologically anchored clinical criteria. An important ethical issue of biobanking is the need for broad consent by the donors for specimen use in not yet defined future research projects. The organizational, logistic and financial costs of establishing and maintaining a biobank are considerable, but seem well warranted in view of the gainable advances in biomedical research quality, translations and clinical applications.

[A selective review of recent research results in biological psychiatry]

In accordance with the motto of this year's German Society for Psychiatry, Psychotherapy and Psychosomatics (DGPPN) conference, this article surveys very recent developments in biological psychiatry and neurosciences that have the potential to open up new vistas for the psychiatry and psychotherapy of the future. The work reported includes progress in genome-wide association studies, the implications of these findings for psychiatric nosology and gene-environment interactions, new methods to characterize mechanisms of altered brain function in animal models and humans and the translation of these findings into new therapies. As a core methodology for the psychiatry of the future, biological and applied neuroscience approaches should benefit from sustained structural funding to ensure that these advances impact real-world patient care.

Study protocol of the ASD-Net, the German research consortium for the study of Autism Spectrum Disorder across the lifespan: from a better etiological understanding, through valid diagnosis, to more effective health care

BACKGROUND

Autism Spectrum Disorder (ASD) is a severe, lifelong neurodevelopmental disorder with early onset that places a heavy burden on affected individuals and their families. Due to the need for highly specialized health, educational and vocational services, ASD is a cost-intensive disorder, and strain on health care systems increases with increasing age of the affected individual.

METHODS

The ASD-Net will study Germany's largest cohort of patients with ASD over the lifespan. By combining methodological expertise from all levels of clinical research, the ASD-Net will follow a translational approach necessary to identify neurobiological pathways of different phenotypes and their appropriate identification and treatment. The work of the ASD-Net will be organized into three clusters concentrating on diagnostics, therapy and health economics. In the diagnostic cluster, data from a large, well-characterized sample (N = 2568) will be analyzed to improve the efficiency of diagnostic procedures. Pattern classification methods (machine learning) will be used to identify algorithms for screening purposes. In a second step, the developed algorithm will be tested in an independent sample. In the therapy cluster, we will unravel how an ASD-specific social skills training with concomitant oxytocin administration can modulate behavior through neurobiological pathways. For the first time, we will characterize long-term effects of a social skills training combined with oxytocin treatment on behavioral and neurobiological phenotypes. Also acute effects of oxytocin will be investigated to delineate general and specific effects of additional oxytocin treatment in order to develop biologically plausible models for symptoms and successful therapeutic interventions in ASD. Finally, in the health economics cluster, we will assess service utilization and ASD-related costs in order to identify potential needs and cost savings specifically tailored to Germany. The ASD-Net has been established as part of the German Research Network for Mental Disorders, funded by the BMBF (German Federal Ministry of Education and Research).

DISCUSSION

The highly integrated structure of the ASD-Net guarantees sustained collaboration of clinicians and researchers to alleviate individual distress, harm, and social disability of patients with ASD and reduce costs to the German health care system.

TRIAL REGISTRATION

Both clinical trials of the ASD-Net are registered in the German Clinical Trials Register: DRKS00008952 (registered on August 4, 2015) and DRKS00010053 (registered on April 8, 2016).

Diagnoses of autism spectrum disorders in Germany: Time trends in administrative prevalence and diagnostic stability

For Germany, no data on trends in autism spectrum disorder diagnoses are available. The primary aim of this study was to establish the time trends in the administrative prevalence of autism spectrum disorder diagnoses. The second aim was to assess the stability of autism spectrum disorder diagnoses over time. We analysed administrative outpatient data (2006-2012) from a nationwide health insurance fund and calculated the prevalence of autism spectrum disorder diagnoses for each year, stratified by age and sex. Additionally, we studied a cohort with a first-time diagnosis of autism spectrum disorder in 2007 through 2012, investigating the percentage of retained autism spectrum disorder diagnoses. From 2006 to 2012, the prevalence of autism spectrum disorder diagnoses in 0- to 24-year-olds increased from 0.22% to 0.38%. In insurees with a first-time autism spectrum disorder diagnosis in 2007, this diagnosis was carried on in all years through 2012 in 33.0% (The International Classification of Diseases, Tenth Revision diagnoses: F84.0/F84.1/F84.5) and 11.2% (F84.8/F84.9), respectively. In Germany, like in other countries, there has been an increase in the administrative prevalence of autism spectrum disorder diagnoses. Yet, prevalences are still lower than in some other Western countries. The marked percentage of autism spectrum disorder diagnoses which were not retained could indicate a significant portion of autism spectrum disorder misdiagnoses, which might contribute to rising autism spectrum disorder prevalences.