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BackgroundMorphology of the human cerebral cortex differs across psychiatric disorders, with neurobiology and developmental origins mostly undetermined. Deviations in the tangential growth of the cerebral cortex during pre/perinatal periods may be reflected in individual variations in cortical surface area later in life.MethodsInterregional profiles of group differences in surface area between cases and controls were generated using T1-weighted magnetic resonance imaging from 27,359 individuals including those with attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, schizophrenia, and high general psychopathology (through the Child Behavior Checklist). Similarity of interregional profiles of group differences in surface area and prenatal cell-specific gene expression was assessed.ResultsAcross the 11 cortical regions, group differences in cortical area for attention-deficit/hyperactivity disorder, schizophrenia, and Child Behavior Checklist were dominant in multimodal association cortices. The same interregional profiles were also associated with interregional profiles of (prenatal) gene expression specific to proliferative cells, namely radial glia and intermediate progenitor cells (greater expression, larger difference), as well as differentiated cells, namely excitatory neurons and endothelial and mural cells (greater expression, smaller difference). Finally, these cell types were implicated in known pre/perinatal risk factors for psychosis. Genes coexpressed with radial glia were enriched with genes implicated in congenital abnormalities, birth weight, hypoxia, and starvation. Genes coexpressed with endothelial and mural genes were enriched with genes associated with maternal hypertension and preterm birth.ConclusionsOur findings support a neurodevelopmental model of vulnerability to mental illness whereby prenatal risk factors acting through cell-specific processes lead to deviations from typical brain development during pregnancy.

Human brain structure changes throughout the lifespan. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental and neurodegenerative diseases. In this study, we identified common genetic variants that affect rates of brain growth or atrophy in what is, to our knowledge, the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genes GPR139, DACH1 and APOE are associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and aging.

Simple Summary: This study explored the predictability of meningioma resection status (gross total vs. subtotal) based on clinical features. We analyzed 23 features to determine their effectiveness in forecasting resection outcomes, comparing predictions based on the Simpson grading system and postoperative tumor volume (POTV). Using data from 157 patients, our developed models demonstrated high accuracy with only two key features, achieving an average AUC of 0.885 and accuracy of 0.866. The final model, a simple decision tree, may be useful for guiding decisions for surgical planning and postoperative treatments. Background: Our aim was to investigate the predictability of postoperative meningioma resection status based on clinical features. Methods: We examined 23 clinical features to assess their effectiveness in distinguishing gross total resections (GTR) from subtotal resections (STR). We analyzed whether GTR/STR cases are better predictable if the classification is based on the Simpson grading or the postoperative operative tumor volume (POTV). Results: Using a study cohort comprising a total of 157 patients, multivariate models for the preoperative prediction of GTR/STR outcome in relation to Simpson grading and POTV were developed and subsequently compared. Including only two clinical features, our models showed a notable discriminatory power in predicting postoperative resection status. Our final model, a straightforward decision tree applicable in daily clinical practice, achieved a mean AUC of 0.885, a mean accuracy of 0.866, a mean sensitivity of 0.889, and a mean specificity of 0.772 based on independent test data. Conclusions: Such models can be a valuable tool both for surgical planning and for early planning of postoperative treatment, e.g., for additional radiotherapy/radiosurgery, potentially required in case of subtotal resections. [ABSTRACT FROM AUTHOR]

Background: Childhood maltreatment has been associated with reduced hippocampal volume in healthy individuals, whereas social support, a protective factor, has been positively associated with hippocampal volumes. In this study, we investigated how social support is associated with hippocampal volume in healthy people with previous experience of childhood maltreatment. Methods: We separated a sample of 446 healthy participants into 2 groups using the Childhood Trauma Questionnaire: 265 people without maltreatment and 181 people with maltreatment. We measured perceived social support using a short version of the Social Support Questionnaire. We examined hippocampal volume using automated segmentation (Freesurfer). We conducted a social support x group analysis of covariance on hippocampal volumes controlling for age, sex, total intracranial volume, site and verbal intelligence. Results: Our analysis revealed significantly lower left hippocampal volume in people with maltreatment (left [F.sub.1,432] = 5.686, p = 0.018; right [F.sub.1,433] = 3.371, p = 0.07), but no main effect of social support emerged. However, we did find a significant social support x group interaction for left hippocampal volume (left [F.sub.1,432] = 5.712, p = 0.017; right [F.sub.1,433] = 3.480, p = 0.06). In people without maltreatment, we observed a trend toward a positive association between social support and hippocampal volume. In contrast, social support was negatively associated with hippocampal volume in people with maltreatment. Limitations: Because of the correlative nature of our study, we could not infer causal relationships between social support, maltreatment and hippocampal volume. Conclusion: Our results point to a complex dynamic between environmental risk, protective factors and brain structure--in line with previous evidence--suggesting a detrimental effect of maltreatment on hippocampal development., Introduction Childhood maltreatment is an adverse social experience and is a risk factor for many mental disorders. (1) It is also associated with neuroendocrine dysregulation (e.g., hypercortisolism) and alterations in [...]

Background: Preliminary research suggests that major depressive disorder (MDD) is associated with structural alterations in the brain, as well as with low-grade peripheral inflammation. However, even though a link between inflammatory processes and altered brain structural integrity has been purported by experimental research, well-powered studies to confirm this hypothesis in patients with MDD have been lacking. We aimed to investigate the potential association between structural brain alterations and low-grade inflammation as interrelated biological correlates of MDD. Methods: In this cross-sectional study, 514 patients with MDD and 359 healthy controls underwent structural MRI. We used voxel-based morphometry to study local differences in grey matter volume. We also assessed serum levels of high-sensitivity C-reactive protein (hsCRP) in each participant. Results: Compared with healthy controls (age [mean [+ or -] standard deviation] 52.57 [+ or -] 7.94 yr; 50% male), patients with MDD (49.14 [+ or -] 7.28 yr, 39% male) exhibited significantly increased hsCRP levels (Z = -5.562, p < 0.001) and significantly decreased grey matter volume in the prefrontal cortex and the insula. Prefrontal grey matter volume reductions were significantly associated with higher hsCRP levels in patients with MDD (x = 50, y = 50, z = 8; [t.sub.1,501] = 5.15; k = 92; [p.sub.FWE] < 0.001). In the MDD sample, the significant negative association between hsCRP and grey matter appeared independent of age, sex, body mass index, current smoking status, antidepressant load, hospitalization and medical comorbidities. Limitations: This study had a cross-sectional design. Conclusion: The present study highlights the role of reduced grey matter volume and low- grade peripheral inflammation as interrelated biological correlates of MDD. The reported inverse association between peripheral low- grade inflammation and brain structural integrity in patients with MDD translates current knowledge from experimental studies to the bedside., Introduction Major depressive disorder (MDD) is one of the most debilitating disorders worldwide. (1) Neurobiological research on MDD has identified both peripheral low-grade inflammation and structural brain alterations as promising [...]

Background: Cross-sectional studies have repeatedly shown impaired white matter integrity in patients with major depressive disorder. Longitudinal analyses are missing from the current research and are crucial to elucidating the impact of disease trajectories on white matter impairment in major depressive disorder. Methods: Fifty-nine patients with major depressive disorder receiving inpatient treatment, as well as 49 healthy controls, took part in a prospective study. Participants were scanned twice (baseline and follow- up), approximately 2.25 years apart, using diffusion tensor imaging. We analyzed diffusion metrics using tract-based spatial statistics. Results: At baseline, patients had higher mean diffusivity in a large bilateral frontal cluster comprising the body and genu of the corpus callosum, the anterior and superior corona radiata, and the superior longitudinal fasciculus. A significant group x time interaction revealed a decrease of mean diffusivity in patients with major depressive disorder over time, abolishing group differences at follow-up. This effect was observed irrespective of disease course in the follow-up period. Limitations: Analyzing the course of illness is challenging because of recollection biases in patients with major depressive disorder. Conclusion: This study reports follow-up diffusion tensor imaging data in patients with major depressive disorder after an acute depressive episode. We demonstrated impaired prefrontal white matter microstructure (higher mean diffusivity) at baseline in patients with major depressive disorder, which normalized at follow-up after 2 years, irrespective of disease course. This might have been due to a general treatment effect and might have reflected recovery of white matter integrity., Introduction Major depressive disorder (MDD) is expected to become the second-highest contributor to global disease burden by the year 2020, (1) and yet our understanding of its pathophysiological basis remains [...]

Background: Identifying reliable trait markers of familial risk for major depressive disorder (MDD) is a challenge in translational psychiatric research. In individuals with acute MDD, dysfunctional connectivity patterns of prefrontal areas have been shown repeatedly. However, it has been unclear in which neuronal networks functional alterations in individuals at familial risk for MDD might be present and to what extent they resemble findings previously reported in those with acute MDD. Methods: We investigated differences in blood oxygen level-dependent (BOLD) response of the medial orbitofrontal cortex (OFC) and dorsolateral prefrontal cortex (DLPFC) to aversive stimuli between acute MDD and familial risk for the disorder in healthy first-degree relatives of acutely depressed patients with MDD (HC-FH+), healthy age- and sex-matched controls without any family history of depression (HC-FH-), and acutely depressed patients with MDD with (MDD-FH+) and without a family history of depression (MDD-FH-) during a frequently used emotional face-matching paradigm. Analyses of task-specific network connectivity were conducted in terms of psychophysiological interactions (PPI). Results: The present analysis included a total of 100 participants: 25 HC-FH+, 25 HC-FH-, 25 MDD-FH+ and 25 MDD-FH-. Patients with MDD exhibited significantly increased activation in the medial OFC to negative stimuli irrespective of familial risk status, whereas healthy participants at familial risk and patients with MDD alike showed significant hypoactivation in the DLPFC compared with healthy participants without familial risk. The PPI analyses revealed significantly enhanced task-specific coupling between the medial OFC and differing cortical areas in individuals with acute MDD and those with familial risk for the disorder. Limitations: The main limitation of our study is its cross-sectional design. Conclusion: Whereas hypoactivation during negative emotion processing in the DLPFC appears as a common feature in both healthy high-risk individuals and acutely depressed patients, activation patterns of the medial OFC and its underlying connectivity seem to distinguish familial risk from acute disorder., Introduction Major depressive disorder (MDD) ranks among the most disabling and thus most cost-producing health care issues in developed countries. (1) Enhanced prevention of this disorder is one of the [...]

Our aim is to investigate the added value of automated machine learning (AutoML) for potential future applications in cancer diagnostics. Using two important diagnostic questions, the non-invasive determination of IDH mutation status and ATRX status, we analyze whether it is possible to use AutoML to develop models that are comparable in performance to conventional machine learning models (ML) developed by experts. For this purpose, we develop AutoML models using different feature preselection methods and compare the results with previously developed conventional ML models. The cohort used for our study comprises T2-weighted MRI images of 124 patients with histologically confirmed gliomas. Using AutoML, we were able to develop sophisticated models in a very short time with only a few lines of computer code. In predicting IDH mutation status, we obtained a mean AUC of 0.7400 and a mean AUPRC of 0.8582. ATRX mutation status was predicted with very similar discriminatory power, with a mean AUC of 0.7810 and a mean AUPRC of 0.8511. In both cases, AutoML was even able to achieve a discriminatory power slightly above that of the respective conventionally developed models in a very short computing time, thus making such methods accessible to non-experts in the near future. [ABSTRACT FROM AUTHOR]