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BackgroundSmartphone-based symptom monitoring has gained increased attention in psychiatric research as a cost-efficient tool for prospective and ecologically valid assessments based on participants’ self-reports. However, a meaningful interpretation of smartphone-based assessments requires knowledge about their psychometric properties, especially their validity. ObjectiveThe goal of this study is to systematically investigate the validity of smartphone-administered assessments of self-reported affective symptoms using the Remote Monitoring Application in Psychiatry (ReMAP). MethodsThe ReMAP app was distributed to 173 adult participants of ongoing, longitudinal psychiatric phenotyping studies, including healthy control participants, as well as patients with affective disorders and anxiety disorders; the mean age of the sample was 30.14 years (SD 11.92). The Beck Depression Inventory (BDI) and single-item mood and sleep information were assessed via the ReMAP app and validated with non–smartphone-based BDI scores and clinician-rated depression severity using the Hamilton Depression Rating Scale (HDRS). ResultsWe found overall high comparability between smartphone-based and non–smartphone-based BDI scores (intraclass correlation coefficient=0.921; P

Background: Brain extraction in magnetic resonance imaging (MRI) data is an important segmentation step in many neuroimaging preprocessing pipelines. Image segmentation is one of the research fields in which deep learning had the biggest impact in recent years. Consequently, traditional brain extraction methods are now being replaced by deep learning-based methods., Method: Here, we used a unique dataset compilation comprising 7837 T1-weighted (T1w) MR images from 191 different OpenNeuro datasets in combination with advanced deep learning methods to build a fast, high-precision brain extraction tool called deepbet., Results: deepbet sets a novel state-of-the-art performance during cross-dataset validation with a median Dice score (DSC) of 99.0 on unseen datasets, outperforming the current best performing deep learning (DSC=97.9) and classic (DSC=96.5) methods. While current methods are more sensitive to outliers, deepbet achieves a Dice score of >97.4 across all 7837 images from 191 different datasets. This robustness was additionally tested in 5 external datasets, which included challenging clinical MR images. During visual exploration of each method's output which resulted in the lowest Dice score, major errors could be found for all of the tested tools except deepbet. Finally, deepbet uses a compute efficient variant of the UNet architecture, which accelerates brain extraction by a factor of ≈10 compared to current methods, enabling the processing of one image in ≈2 s on low level hardware., Conclusions: In conclusion, deepbet demonstrates superior performance and reliability in brain extraction across a wide range of T1w MR images of adults, outperforming existing top tools. Its high minimal Dice score and minimal objective errors, even in challenging conditions, validate deepbet as a highly dependable tool for accurate brain extraction. deepbet can be conveniently installed via "pip install deepbet" and is publicly accessible at https://github.com/wwu-mmll/deepbet., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background and Objective: Flow cytometry is a widely used technique for identifying cell populations in patient-derived fluids, such as peripheral blood (PB) or cerebrospinal fluid (CSF). Despite its ubiquity in research and clinical practice, the process of gating, i.e., manually identifying cell types, is labor-intensive and error-prone. The objective of this study is to address this challenge by introducing GateNet, a neural network architecture designed for fully end-to-end automated gating without the need for correcting batch effects., Methods: For this study a unique dataset is used which comprises over 8,000,000 events from N = 127 PB and CSF samples which were manually labeled independently by four experts. Applying cross-validation, the classification performance of GateNet is compared to the human experts performance. Additionally, GateNet is applied to a publicly available dataset to evaluate generalization. The classification performance is measured using the F1 score., Results: GateNet achieves F1 scores ranging from 0.910 to 0.997 demonstrating human-level performance on samples unseen during training. In the publicly available dataset, GateNet confirms its generalization capabilities with an F1 score of 0.936. Importantly, we also show that GateNet only requires ≈10 samples to reach human-level performance. Finally, gating with GateNet only takes 15 microseconds per event utilizing graphics processing units (GPU)., Conclusions: GateNet enables fully end-to-end automated gating in flow cytometry, overcoming the labor-intensive and error-prone nature of manual adjustments. The neural network achieves human-level performance on unseen samples and generalizes well to diverse datasets. Notably, its data efficiency, requiring only ∼10 samples to reach human-level performance, positions GateNet as a widely applicable tool across various domains of flow cytometry., Competing Interests: Declaration of competing interest We, the undersigned, confirm that the manuscript represents our own work, is original and has not been copyrighted, published, submitted, or accepted for publication elsewhere. We further confirm that we all have fully read the manuscript and give consent to be co-authors of the manuscript., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Importance: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, major depressive disorder (MDD), no informative biomarkers have been identified., Objective: To evaluate whether machine learning (ML) can identify a multivariate biomarker for MDD., Design, Setting, and Participants: This study used data from the Marburg-Münster Affective Disorders Cohort Study, a case-control clinical neuroimaging study. Patients with acute or lifetime MDD and healthy controls aged 18 to 65 years were recruited from primary care and the general population in Münster and Marburg, Germany, from September 11, 2014, to September 26, 2018. The Münster Neuroimaging Cohort (MNC) was used as an independent partial replication sample. Data were analyzed from April 2022 to June 2023., Exposure: Patients with MDD and healthy controls., Main Outcome and Measure: Diagnostic classification accuracy was quantified on an individual level using an extensive ML-based multivariate approach across a comprehensive range of neuroimaging modalities, including structural and functional magnetic resonance imaging and diffusion tensor imaging as well as a polygenic risk score for depression., Results: Of 1801 included participants, 1162 (64.5%) were female, and the mean (SD) age was 36.1 (13.1) years. There were a total of 856 patients with MDD (47.5%) and 945 healthy controls (52.5%). The MNC replication sample included 1198 individuals (362 with MDD [30.1%] and 836 healthy controls [69.9%]). Training and testing a total of 4 million ML models, mean (SD) accuracies for diagnostic classification ranged between 48.1% (3.6%) and 62.0% (4.8%). Integrating neuroimaging modalities and stratifying individuals based on age, sex, treatment, or remission status does not enhance model performance. Findings were replicated within study sites and also observed in structural magnetic resonance imaging within MNC. Under simulated conditions of perfect reliability, performance did not significantly improve. Analyzing model errors suggests that symptom severity could be a potential focus for identifying MDD subgroups., Conclusion and Relevance: Despite the improved predictive capability of multivariate compared with univariate neuroimaging markers, no informative individual-level MDD biomarker-even under extensive ML optimization in a large sample of diagnosed patients-could be identified.

Many therapeutic interventions in psychiatry can be viewed as attempts to influence the brain's large-scale, dynamic network state transitions. Building on connectome-based graph analysis and control theory, Network Control Theory is emerging as a powerful tool to quantify network controllability-i.e., the influence of one brain region over others regarding dynamic network state transitions. If and how network controllability is related to mental health remains elusive. Here, from Diffusion Tensor Imaging data, we inferred structural connectivity and inferred calculated network controllability parameters to investigate their association with genetic and familial risk in patients diagnosed with major depressive disorder (MDD, n = 692) and healthy controls (n = 820). First, we establish that controllability measures differ between healthy controls and MDD patients while not varying with current symptom severity or remission status. Second, we show that controllability in MDD patients is associated with polygenic scores for MDD and psychiatric cross-disorder risk. Finally, we provide evidence that controllability varies with familial risk of MDD and bipolar disorder as well as with body mass index. In summary, we show that network controllability is related to genetic, individual, and familial risk in MDD patients. We discuss how these insights into individual variation of network controllability may inform mechanistic models of treatment response prediction and personalized intervention-design in mental health., (© 2023. The Author(s).)

Electroconvulsive Therapy (ECT) is arguably the most effective intervention for treatment-resistant depression. While large interindividual variability exists, a theory capable of explaining individual response to ECT remains elusive. To address this, we posit a quantitative, mechanistic framework of ECT response based on Network Control Theory (NCT). Then, we empirically test our approach and employ it to predict ECT treatment response. To this end, we derive a formal association between Postictal Suppression Index (PSI)-an ECT seizure quality index-and whole-brain modal and average controllability, NCT metrics based on white-matter brain network architecture, respectively. Exploiting the known association of ECT response and PSI, we then hypothesized an association between our controllability metrics and ECT response mediated by PSI. We formally tested this conjecture in N = 50 depressive patients undergoing ECT. We show that whole-brain controllability metrics based on pre-ECT structural connectome data predict ECT response in accordance with our hypotheses. In addition, we show the expected mediation effects via PSI. Importantly, our theoretically motivated metrics are at least on par with extensive machine learning models based on pre-ECT connectome data. In summary, we derived and tested a control-theoretic framework capable of predicting ECT response based on individual brain network architecture. It makes testable, quantitative predictions regarding individual therapeutic response, which are corroborated by strong empirical evidence. Our work might constitute a starting point for a comprehensive, quantitative theory of personalized ECT interventions rooted in control theory., (© The Author(s) 2023. Published by Oxford University Press on behalf of National Academy of Sciences.)

Importance: Identifying neurobiological differences between patients with major depressive disorder (MDD) and healthy individuals has been a mainstay of clinical neuroscience for decades. However, recent meta-analyses have raised concerns regarding the replicability and clinical relevance of brain alterations in depression., Objective: To quantify the upper bounds of univariate effect sizes, estimated predictive utility, and distributional dissimilarity of healthy individuals and those with depression across structural magnetic resonance imaging (MRI), diffusion-tensor imaging, and functional task-based as well as resting-state MRI, and to compare results with an MDD polygenic risk score (PRS) and environmental variables., Design, Setting, and Participants: This was a cross-sectional, case-control clinical neuroimaging study. Data were part of the Marburg-Münster Affective Disorders Cohort Study. Patients with depression and healthy controls were recruited from primary care and the general population in Münster and Marburg, Germany. Study recruitment was performed from September 11, 2014, to September 26, 2018. The sample comprised patients with acute and chronic MDD as well as healthy controls in the age range of 18 to 65 years. Data were analyzed from October 29, 2020, to April 7, 2022., Main Outcomes and Measures: Primary analyses included univariate partial effect size (η2), classification accuracy, and distributional overlapping coefficient for healthy individuals and those with depression across neuroimaging modalities, controlling for age, sex, and additional modality-specific confounding variables. Secondary analyses included patient subgroups for acute or chronic depressive status., Results: A total of 1809 individuals (861 patients [47.6%] and 948 controls [52.4%]) were included in the analysis (mean [SD] age, 35.6 [13.2] years; 1165 female patients [64.4%]). The upper bound of the effect sizes of the single univariate measures displaying the largest group difference ranged from partial η2 of 0.004 to 0.017, and distributions overlapped between 87% and 95%, with classification accuracies ranging between 54% and 56% across neuroimaging modalities. This pattern remained virtually unchanged when considering either only patients with acute or chronic depression. Differences were comparable with those found for PRS but substantially smaller than for environmental variables., Conclusions and Relevance: Results of this case-control study suggest that even for maximum univariate biological differences, deviations between patients with MDD and healthy controls were remarkably small, single-participant prediction was not possible, and similarity between study groups dominated. Biological psychiatry should facilitate meaningful outcome measures or predictive approaches to increase the potential for a personalization of the clinical practice.

The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk marker of cross-disorder brain changes, growing into a cornerstone of biological age research. However, machine learning models underlying the field do not consider uncertainty, thereby confounding results with training data density and variability. Also, existing models are commonly based on homogeneous training sets, often not independently validated, and cannot be shared because of data protection issues. Here, we introduce an uncertainty-aware, shareable, and transparent Monte Carlo dropout composite quantile regression (MCCQR) Neural Network trained on N = 10,691 datasets from the German National Cohort. The MCCQR model provides robust, distribution-free uncertainty quantification in high-dimensional neuroimaging data, achieving lower error rates compared with existing models. In two examples, we demonstrate that it prevents spurious associations and increases power to detect deviant brain aging. We make the pretrained model and code publicly available.

Background: Smartphone-based monitoring constitutes a cost-effective instrument to assess and predict affective symptom trajectories. Large-scale transdiagnostic studies utilizing this methodology are yet lacking in psychiatric research. Thus, we introduce the Remote Monitoring Application in Psychiatry (ReMAP) and evaluate its feasibility and adherence in a large transdiagnostic sample., Methods: The ReMAP app was distributed among n = 997 healthy control participants and psychiatric patients, including affective, anxiety, and psychotic disorders. Passive sensor data (acceleration, geolocation, walking distance, steps), optional standardized self-reports on mood and sleep, and voice samples were assessed. Feasibility and adherence were evaluated based on frequency of transferred data, and participation duration. Preliminary results are presented while data collection is ongoing., Results: Retention rates of 90.25% for the minimum study duration of two weeks and 33.09% for one year were achieved (median participation 135 days, IQR=111). Participants transferred an average of 51.83 passive events per day. An average of 34.59 self-report events were transferred per user, with a considerable range across participants (0-552 events). Clinical and non-clinical subgroups did not differ in participation duration or rate of data transfer. The mean rate of days with passive data was higher and less heterogeneous in iOS (91.85%, SD=21.25) as compared to Android users (63.04%, SD=35.09)., Limitations: Subjective user experience was not assessed limiting conclusions about app acceptance., Conclusions: ReMAP is a technically feasible tool to assess affective symptoms with high temporal resolution in large-scale transdiagnostic samples with good adherence. Future studies should account for differences between operating systems., (Copyright © 2021. Published by Elsevier B.V.)

PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development. It functions as a unifying framework allowing the user to easily access and combine algorithms from different toolboxes into custom algorithm sequences. It is especially designed to support the iterative model development process and automates the repetitive training, hyperparameter optimization and evaluation tasks. Importantly, the workflow ensures unbiased performance estimates while still allowing the user to fully customize the machine learning analysis. PHOTONAI extends existing solutions with a novel pipeline implementation supporting more complex data streams, feature combinations, and algorithm selection. Metrics and results can be conveniently visualized using the PHOTONAI Explorer and predictive models are shareable in a standardized format for further external validation or application. A growing add-on ecosystem allows researchers to offer data modality specific algorithms to the community and enhance machine learning in the areas of the life sciences. Its practical utility is demonstrated on an exemplary medical machine learning problem, achieving a state-of-the-art solution in few lines of code. Source code is publicly available on Github, while examples and documentation can be found at www.photon-ai.com., Competing Interests: The authors have declared that no competing interests exist.

We currently observe a disconcerting phenomenon in machine learning studies in psychiatry: While we would expect larger samples to yield better results due to the availability of more data, larger machine learning studies consistently show much weaker performance than the numerous small-scale studies. Here, we systematically investigated this effect focusing on one of the most heavily studied questions in the field, namely the classification of patients suffering from Major Depressive Disorder (MDD) and healthy controls based on neuroimaging data. Drawing upon structural MRI data from a balanced sample of N = 1868 MDD patients and healthy controls from our recent international Predictive Analytics Competition (PAC), we first trained and tested a classification model on the full dataset which yielded an accuracy of 61%. Next, we mimicked the process by which researchers would draw samples of various sizes (N = 4 to N = 150) from the population and showed a strong risk of misestimation. Specifically, for small sample sizes (N = 20), we observe accuracies of up to 95%. For medium sample sizes (N = 100) accuracies up to 75% were found. Importantly, further investigation showed that sufficiently large test sets effectively protect against performance misestimation whereas larger datasets per se do not. While these results question the validity of a substantial part of the current literature, we outline the relatively low-cost remedy of larger test sets, which is readily available in most cases.

Neuroticism has been shown to act as an important risk factor for major depressive disorder (MDD). Genetic and neuroimaging research has independently revealed biological correlates of neurotic personality including cortical alterations in brain regions of high relevance for affective disorders. Here we investigated the influence of a polygenic score for neuroticism (PGS) on cortical brain structure in a joint discovery sample of n = 746 healthy controls (HC) and n = 268 MDD patients. Findings were validated in an independent replication sample (n = 341 HC and n = 263 MDD). Subgroup analyses stratified for case-control status and analyses of associations between neurotic phenotype and cortical measures were carried out. PGS for neuroticism was significantly associated with a decreased cortical surface area of the inferior parietal cortex, the precuneus, the rostral cingulate cortex and the inferior frontal gyrus in the discovery sample. Similar associations between PGS and surface area of the inferior parietal cortex and the precuneus were demonstrated in the replication sample. Subgroup analyses revealed negative associations in the latter regions between PGS and surface area in both HC and MDD subjects. Neurotic phenotype was negatively correlated with surface area in similar cortical regions including the inferior parietal cortex and the precuneus. No significant associations between PGS and cortical thickness were detected. The morphometric overlap of associations between both PGS and neurotic phenotype in similar cortical regions closely related to internally focused cognition points to the potential relevance of genetically shaped cortical alterations in the development of neuroticism.

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.

While the hippocampus remains a region of high interest for neuropsychiatric research, the precise contributors to hippocampal morphometry are still not well understood. We and others previously reported a hippocampus specific effect of a tescalcin gene (TESC) regulating single nucleotide polymorphism (rs7294919) on gray matter volume. Here we aimed to replicate and extend these findings. Two complementary morphometric approaches (voxel based morphometry (VBM) and automated volumetric segmentation) were applied in a well-powered cohort from the Marburg-Münster Affective Disorder Cohort Study (MACS) including N=1137 participants (n=636 healthy controls, n=501 depressed patients). rs7294919 homozygous T-allele genotype was significantly associated with lower hippocampal gray matter density as well as with reduced hippocampal volume. Exploratory whole brain VBM analyses revealed no further associations with gray matter volume outside the hippocampus. No interaction effects of rs7294919 with depression nor with childhood trauma on hippocampal morphometry could be detected. Hippocampal subfield analyses revealed similar effects of rs7294919 in all hippocampal subfields. In sum, our results replicate a hippocampus specific effect of rs7294919 on brain structure. Due to the robust evidence for a pronounced association between the reported polymorphism and hippocampal morphometry, future research should consider investigating the potential clinical and functional relevance of the reported association., Competing Interests: Conflict of interest Tilo Kircher received unrestricted educational grants from Servier, Janssen, Recordati, Aristo, Otsuka, neuraxpharm. Markus Wöhr is scientific advisor of Avisoft Bioacoustics. No further conflicts of interest are declared., (Copyright © 2020 Elsevier B.V. and ECNP. All rights reserved.)

Background: Electroconvulsive therapy (ECT) is a fast-acting intervention for major depressive disorder. Previous studies indicated neurotrophic effects following ECT that might contribute to changes in white matter brain structure. We investigated the influence of ECT in a non-randomized prospective study focusing on white matter changes over time., Methods: Twenty-nine severely depressed patients receiving ECT in addition to inpatient treatment, 69 severely depressed patients with inpatient treatment (NON-ECT) and 52 healthy controls (HC) took part in a non-randomized prospective study. Participants were scanned twice, approximately 6 weeks apart, using diffusion tensor imaging, applying tract-based spatial statistics. Additional correlational analyses were conducted in the ECT subsample to investigate the effects of seizure duration and therapeutic response., Results: Mean diffusivity (MD) increased after ECT in the right hemisphere, which was an ECT-group-specific effect. Seizure duration was associated with decreased fractional anisotropy (FA) following ECT. Longitudinal changes in ECT were not associated with therapy response. However, within the ECT group only, baseline FA was positively and MD negatively associated with post-ECT symptomatology., Conclusion: Our data suggest that ECT changes white matter integrity, possibly reflecting increased permeability of the blood-brain barrier, resulting in disturbed communication of fibers. Further, baseline diffusion metrics were associated with therapy response. Coherent fiber structure could be a prerequisite for a generalized seizure and inhibitory brain signaling necessary to successfully inhibit increased seizure activity.

Background: Childhood maltreatment is a leading environmental risk factor for an unfavourable course of disease in major depressive disorder. Both maltreatment and major depressive disorder are associated with similar brain structural alterations suggesting that brain structural changes could mediate the adverse influence of maltreatment on clinical outcome in major depressive disorder. However, longitudinal studies have not been able to confirm this hypothesis. We therefore aimed to clarify the relationship between childhood trauma, brain structural alterations, and depression relapse in a longitudinal design., Methods: We recruited participants at the Department of Psychiatry, University of Münster, Germany, from the Münster Neuroimage Cohort for whom 2-year longitudinal clinical data were available. Baseline data acquisition comprised clinical assessments, structural MRI, and retrospective assessment of the extent of childhood maltreatment experiences using the Childhood Trauma Questionnaire. Clinical follow-up assessments were conducted in all participants 2 years after initial recruitment., Findings: Initial recruitment was March 21, 2010-Jan 29, 2016; follow-up reassessment Sept 7, 2012-March 9, 2018. 110 patients with major depressive disorder participated in this study. 35 patients were relapse-free, whereas 75 patients had experienced depression relapse within the 2-year follow-up period. Childhood maltreatment was significantly associated with depression relapse during follow-up (odds ratio [OR] 1·035, 95% CI 1·001-1·070; p=0·045). Both previous childhood maltreatment experiences and future depression relapse were associated with reduced cortical surface area (OR 0·996, 95% CI 0·994-0·999; p=0·001), primarily in the right insula at baseline (r=-0·219, p=0·023). Insular surface area was shown to mediate the association between maltreatment and future depression relapse (indirect effect: coefficient 0·0128, SE 0·0081, 95% CI 0·0024-0·0333)., Interpretation: Early life stress has a detrimental effect on brain structure, which increases the risk of unfavourable disease courses in major depression. Clinical and translational research should explore the role of childhood maltreatment as causing a potential clinically and biologically distinct subtype of major depressive disorder., Funding: The German Research Foundation, the Interdisciplinary Centre for Clinical Research, and the Deanery of the Medical Faculty of the University of Münster., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Obesity is a clinically relevant and highly prevalent somatic comorbidity of major depression (MDD). Genetic predisposition and history of childhood trauma have both independently been demonstrated to act as risk factors for obesity and to be associated with alterations in reward related brain structure and function. We therefore aimed to investigate the influence of childhood maltreatment and genetic risk for obesity on structural and functional imaging correlates associated with reward processing in MDD. 161 MDD patients underwent structural and functional MRI during a frequently used card guessing paradigm. Main and interaction effects of a polygenic risk score for obesity (PRS) and childhood maltreatment experiences as assessed using the Childhood Trauma Questionnaire (CTQ) were investigated. We found that maltreatment experiences and polygenic risk for obesity significantly interacted on a) body mass index b) gray matter volume of the orbitofrontal cortex as well as on c) BOLD response in the right insula during reward processing. While polygenic risk for obesity was associated with elevated BMI as well as with decreased OFC gray matter and increased insular BOLD response in non-maltreated patients, these associations were absent in patients with a history of childhood trauma. No significant main effect of PRS or maltreatment on gray matter or BOLD response could be detected at the applied thresholds. The present study suggests that childhood maltreatment moderates the influence of genetic load for obesity on BMI as well as on altered brain structure and function in reward related brain circuits in MDD., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Obesity has been associated with a variety of neurobiological alterations. Recent neuroimaging research has pointed to the relevance of brain structural and functional alterations in the development of obesity. However, while the role of gray matter atrophy in obesity has been evidenced in several well powered studies, large scale evidence for altered white matter integrity in obese subjects is still absent. With this study, we therefore aimed to investigate potential associations between white matter abnormalities and body mass index (BMI) in two large independent samples of healthy adults. Associations between BMI values and whole brain fractional anisotropy (FA) were investigated in two independent cohorts: A sample of n = 369 healthy subjects from the Münster Neuroimaging Cohort (MNC), as well as a public available sample of n = 1064 healthy subjects of the Humane Connectome Project (HCP) were included in the present study. Tract based spatial statistics (TBSS) analyses of BMI on whole brain FA were conducted including age and sex as nuisance covariates using the FMRIB library (FSL Version 5.0). Threshold-free cluster enhancement was applied to control for multiple comparisons. In both samples higher BMI was significantly associated with strong and widespread FA reductions. These effects were most pronounced in the corpus callosum, bilateral posterior thalamic radiation, bilateral internal capsule and external capsule, bilateral inferior longitudinal fasciculus and inferior fronto-occipital fasciculus. The association was found to be independent of age, sex and other cardiovascular risk factors. No significant positive associations between BMI and FA occurred. With this highly powered study, we provide robust evidence for globally reduced white matter integrity associated with elevated BMI including replication in an independent sample. The present work thus points out the relevance of white matter alterations as a neurobiological correlate of obesity., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Leukemias are exceptionally well studied at the molecular level and a wealth of high-throughput data has been published. But further utilization of these data by researchers is severely hampered by the lack of accessible integrative tools for viewing and analysis. We developed the Leukemia Gene Atlas (LGA) as a public platform designed to support research and analysis of diverse genomic data published in the field of leukemia. With respect to leukemia research, the LGA is a unique resource with comprehensive search and browse functions. It provides extensive analysis and visualization tools for various types of molecular data. Currently, its database contains data from more than 5,800 leukemia and hematopoiesis samples generated by microarray gene expression, DNA methylation, SNP and next generation sequencing analyses. The LGA allows easy retrieval of large published data sets and thus helps to avoid redundant investigations. It is accessible at www.leukemia-gene-atlas.org.

Problems in the transition of care from hospital to the home situation have led to the introduction of the discharge liaison nurse role in the Netherlands. A nation-wide hospital survey was carried out to gain insight into the role and function of discharge professionals. It was found that 56 of the 117 hospitals in the Netherlands (48%) have a special discharge professional. The discharge professional is a relatively new concept. On average it covers a full-time position. The function differs greatly between hospitals. Three working profiles can be distinguished: the organizational type, the advisory type and the policy-making type. In most cases the discharge professional is a nurse from a community agency based in the hospital and therefore best fits the description of a discharge liaison nurse. Of the 56 hospital-based initiatives involving discharge professionals, 11 (20%) have been systematically evaluated. A critical review of these evaluation studies showed a positive outcome on some aspects of quality of care, but no results were given on efficiency aspects. There was general appreciation of the discharge liaison nurse and continuation of the function was widely recommended. The quality of the evaluation studies was rather poor, and it is suggested that more substantial research should be carried out on this relatively new function.

After discharge from the hospital, patients with cancer can have several problems at home. In this project, patients with cancer, who at time of discharge from the hospital were not indicated for nursing care at home, were offered three home visits by a community nurse. A prospective, descriptive study was undertaken to assess indicators of usefulness of these "continuity visits." It was registered how many and what patients [sex, age, (time of) diagnosis, social support, therapy] wanted to receive the visit. Care needs, as mentioned by the patients during the continuity visits, were reported after the visit by the community nurse. Both patients and community nurses completed an evaluation form after the first visit. A continuity visit was offered to 337 patients; 112 patients received a first, 50 a second, and 24 a third continuity visit. Older patients, patients without social support, and those diagnosed less than half a year before more often agreed to received a first visit. Reasons for patients not receiving a second or third visit were either that patients did not want one or on the contrary they were in need of immediate nursing care or had died before the visit. Two weeks after discharge, 93% of the patients experienced one or more physical, psychological, or social problems; 70% mentioned a need for information; and 47% needed emotional support. Both patients and community nurses evaluate the first visit positively. The findings suggest that continuation of the offer of the first continuity visit could be useful.

Objective: To investigate the feasibility of home care for continuous ambulatory peritoneal dialysis (CAPD) by district nurses., Design: Descriptive study., Setting: Free University Hospital in Amsterdam., Patients: Patients with end-stage renal disease who were eligible for CAPD but were not able to carry out the CAPD treatment themselves were given assistance by district nurses. During the study period from December 1991 to December 1992 the patients' clinical scores were recorded every 3 months, and questionnaires were sent to both the district and the hospital nurses., Results: During the study period 58 months of CAPD home care were given to ten patients (average age 74 years) by 159 community nurses. These had received a preliminary training by the staff of the dialysis department and they were supported by consultation. Three patients died during the study period. Five patients continued the CAPD home care treatment after December 1992., Conclusion: CAPD home care by district nurses is feasible on the following conditions: extra financing, preliminary training and support for the district nurses from the hospital staff by consultation.