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 PARTICIPANTSThis 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 (η 2 ), 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 η 2 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.

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Background: 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), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over- as well as underestimation of true model accuracy. Methods: Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression. Findings: Training and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification. Interpretation: Although multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification — even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments — does not reach clinically relevant performance. Based on this evidence, we sketch a course of action for Precision Psychiatry and future MDD biomarker research. Funding: The German Research Foundation, and the Interdisciplinary Centre for Clinical Research of the University of Münster.

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Background: Digital acquisition of risk factors and symptoms based on patients self-reports represents a promising, cost-efficient and increasingly prevalent approach for standardized data collection in psychiatric clinical routine. While the feasibility of digital data collection has been demonstrated across a range of psychiatric disorders, studies investigating digital data collection in schizophrenia spectrum disorder patients are scarce. Hence, up to now our knowledge about the acceptability and feasibility of digital data collection in patients with a schizophrenia spectrum disorder remains critically limited. Objective: The objective of this study was to explore the acceptance towards and performance with digitally acquired assessments of risk and symptom profiles in patients with a schizophrenia spectrum disorder in comparison with patients with an affective disorder. Methods: We investigated the acceptance, the required support and the data entry pace of patients during a longitudinal digital data collection system of risk and symptom profiles using self-reports on tablet computers throughout inpatient treatment in patients with a schizophrenia spectrum disorder. As a benchmark comparison, findings in patients with schizophrenia spectrum disorder were evaluated in direct comparison with findings in affective disorder patients. The influence of sociodemographic data and clinical characteristics on the assessment was explored. The study was performed at the Department of Psychiatry at the University of Muenster between February 2020 and February 2021. Results: Of 82 patients diagnosed with a schizophrenia spectrum disorder who were eligible for inclusion 59.8% (n=49) agreed to participate in the study of whom 54.2% (n=26) could enter data without any assistance. Inclusion rates, drop-out rates and subjective experience ratings did not differ between patients with a schizophrenia spectrum disorder and patients with an affective disorder. Out of all participating patients, 98% reported high satisfaction with the digital assessment. Patients with a schizophrenia spectrum disorder needed more support and more time for the assessment compared to patients with an affective disorder. The extent of support of patients with a schizophrenia spectrum disorder was predicted by age, whereas the feeling of self-efficacy predicted data entry pace. Conclusion: Our results indicate that, although patients with a schizophrenia spectrum disorder need more support and more time for data entry than patients with an affective disorder, digital data collection using patients self-reports is a feasible and well-received method. Future clinical and research efforts on digitized assessments in psychiatry should include patients with a schizophrenia spectrum disorder and offer adequate support to reduce digital exclusion of these patients.

For artificial intelligence (AI) based systems to become clinically relevant, they must perform well. Machine Learning (ML) based AI systems require a large amount of labelled training data to achieve this level. In cases of a shortage of such large amounts, Generative Adversarial Networks (GAN) are a standard tool for synthesising artificial training images that can be used to augment the data set. We investigated the quality of synthetic wound images regarding two aspects: (i) improvement of wound-type classification by a Convolutional Neural Network (CNN) and (ii) how realistic such images look to clinical experts (n = 217). Concerning (i), results show a slight classification improvement. However, the connection between classification performance and the size of the artificial data set is still unclear. Regarding (ii), although the GAN could produce highly realistic images, the clinical experts took them for real in only 31% of the cases. It can be concluded that image quality may play a more significant role than data size in improving the CNN-based classification result.

Introduction:Statistical effect sizes are systematically overestimated in small samples, leading to poor generalizability and replicability of findings in all areas of research. Due to the large number of variables, this is particularly problematic in neuroimaging research. While cross-validation is frequently used in multivariate machine learning approaches to assess model generalizability and replicability, the benefits for mass-univariate brain analysis are yet unclear. We investigated the impact of cross-validation on effect size estimation in univariate voxel-based brain-wide associations, using body mass index (BMI) as an exemplary predictor.Methods:A total of n=3401 adults were pooled from three independent cohorts. Brain-wide associations between BMI and gray matter structure were tested using a standard linear mass-univariate voxel-based approach. First, a traditional non-cross-validated analysis was conducted to identify brain-wide effect sizes in the total sample (as an estimate of a realistic reference effect size). The impact of sample size (bootstrapped samples ranging from n=25 to n=3401) and cross-validation on effect size estimates was investigated across selected voxels with differing underlying effect sizes (including the brain-wide lowest effect size). Linear effects were estimated within training sets and then applied to unseen test set data, using 5-fold cross-validation. Resulting effect sizes (explained variance) were investigated.Results:Analysis in the total sample (n=3401) without cross-validation yielded mainly negative correlations between BMI and gray matter density with a maximum effect size of R2p=.036 (peak voxel in the cerebellum). Effects were overestimated exponentially with decreasing sample size, with effect sizes up to R2p=.535 in samples of n=25 for the voxel with the brain-wide largest effect and up to R2p=.429 for the voxel with the brain-wide smallest effect. When applying cross-validation, linear effects estimated in small samples did not generalize to an independent test set. For the largest brain-wide effect a minimum sample size of n=100 was required to start generalizing (explained variance >0 in unseen data), while n=400 were needed for smaller effects of R2p=.005 to generalize. For a voxel with an underlying null effect, linear effects found in non-cross-validated samples did not generalize to test sets even with the maximum sample size of n=3401. Effect size estimates obtained with and without cross-validation approached convergence in large samples.Discussion:Cross-validation is a useful method to counteract the overestimation of effect size particularly in small samples and to assess the generalizability of effects. Train and test set effect sizes converge in large samples which likely reflects a good generalizability for models in such samples. While linear effects start generalizing to unseen data in samples of n>100 for large effect sizes, the generalization of smaller effects requires larger samples (n>400). Cross-validation should be applied in voxel-based mass-univariate analysis to foster accurate effect size estimation and improve replicability of neuroimaging findings. We provide open-source python code for this purpose (https://osf.io/cy7fp/?view_only=a10fd0ee7b914f50820b5265f65f0cdb).

Background/Objective: A detrimental impact of narcissistic personality traits on depressive symptomatology, therapeutic alliance, and treatment outcome, even in the absence of narcissistic personality disorder (NPD), has been theorized. However, the evidence base in clinical settings is lacking. As research classification systems such as the ICD-11 and DSM-5 are moving towards a dimensional operationalization of personality disorders, it appears imperative to examine narcissism as a multifaceted construct and its impact on depressive symptom severity across mental disorders and different treatment settings. Moreover, due to the common interpersonal challenges associated with narcissism, the therapeutic alliance might be a key mechanism to understand narcissism related poorer treatment response.Methods: We examined the effect of narcissism and its facets admiration and rivalry on baseline as well as post-treatment depressive symptoms in two independent samples: one sample from a cognitive behavioral treatment setting, pooled from an inpatient psychiatric clinic and a cooperating outpatient treatment service (CBT; n = 1569) and an inpatient clinic with psychodynamic treatment focus (PIT; n = 802). An additional mediation analysis for the effect of the therapeutic alliance on the association between narcissism and depression severity after treatment was conducted in the outpatient CBT subsample.Results: Narcissistic rivalry was associated with higher depressive symptom load at baseline, while narcissistic admiration showed the opposite effect in both samples. Core narcissism was not related to depression severity before treatment. Poorer treatment response was predicted by core narcissism and narcissistic rivalry in the CBT sample while no effect of narcissism on treatment outcome was discernible in the PIT sample. Therapeutic alliance mediated the effect of narcissism on post-treatment depression severity in the outpatient CBT sample.Conclusions: As narcissism affects depression severity before and after treatment across psychiatric disorders even in the absence of NPD, the inclusion of dimensional assessments of narcissism should be considered in future research and clinical routine. Building on this, the observed relevance of the therapeutic alliance and the therapeutic strategy might be leveraged to guide personalized treatment approaches.