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Introduction. Childhood maltreatment (CM) has been associated with changes in structural brain connectivity even in the absence of mental illness. Social support, an important protective factor in the presence of childhood maltreatment, has been positively linked to white matter integrity. However, investigating the shared effects of current social support and CM and their association with structural connectivity remain to be investigated. They might shed new light on the neurobiological basis of the protective mechanism of social support.Methods. Using connectome-based predictive modeling (CPM), diffusion-weighted-imaging-derived structural connectomes of N=904 healthy adults (mean age: 34 years, 64.4% female) were analyzed. CPM predicts phenotypes from structural connectivity through a cross-validation scheme. Distinct and shared networks of white matter tracts predicting childhood trauma questionnaire scores and the social support questionnaire were identified. Additional analyses were applied to assess the stability of the results.Results. CM and social support were predicted significantly from structural connectome data (all rs>=.119, all ps≤.016). Edges predicting CM and social support were inversely correlated, i.e., positively correlated with CM and negatively with social support, and vice versa, with a focus on frontal and temporal regions including the insula and superior temporal lobe.Discussion. CPM reveals the predictive value of the structural connectome for CM and current social support. Both constructs are inversely associated with connectivity strength in several brain tracts. While this underlines the interconnectedness of these interpersonal experiences, it suggests social support acts as a protective factor following adverse childhood experiences, compensating for brain network alterations. Future longitudinal studies should focus on putative moderating mechanisms buffering these adverse experiences.

Introduction: Reduced processing speed is a core deficit in major depressive disorder (MDD) and has been linked to altered structural brain network connectivity. Ample evidence highlights the involvement of genetic-immunological processes in MDD. Here, we extended these findings by examining associations between polygenic scores for tumor necrosis factor-α blood levels (TNF-α PGS), structural brain connectivity, and processing speed in a large sample of MDD patients. Methods: Processing speed of N=1402 acutely depressed or (partially) remitted patients and healthy controls (HC) was estimated based on five neuropsychological tests. Network-based statistic was used to identify a brain network associated with processing speed. We employed general linear models to examine the association between TNF-α PGS and processing speed. A mediation analysis was performed to investigate whether network connectivity mediates the association between TNF-α PGS and processing speed. Results: We identified a structural network positively associated with processing speed in the whole sample. We observed a significant negative association between TNF-α PGS and processing speed in acutely depressed patients, whereas no association was found in (partially) remitted patients and HC. The mediation analysis revealed that brain connectivity partially mediated the association between TNF-α PGS and processing speed in acute MDD.Discussion: The present study provides evidence that TNF-α PGS is associated with decreased processing speed exclusively in patients with acute depression. This association was partially mediated by structural brain connectivity. Using multimodal data, the current findings advance our understanding of cognitive dysfunction in MDD and highlight the involvement of genetic-immunological processes in its pathomechanisms.

Background Cognitive dysfunction and brain structural connectivity alterations have been observed in major depressive disorder (MDD). However, little is known about their interrelation. The present study follows a network approach to evaluate alterations in cognition-related brain structural networks. Methods Cognitive performance of n = 805 healthy and n = 679 acutely depressed or remitted individuals was assessed using 14 cognitive tests aggregated into cognitive factors. The structural connectome was reconstructed from structural and diffusion-weighted magnetic resonance imaging. Associations between global connectivity strength and cognitive factors were established using linear regressions. Network-based statistics were applied to identify subnetworks of connections underlying these global-level associations. In exploratory analyses, effects of depression were assessed by evaluating remission status-related group differences in subnetwork-specific connectivity. Partial correlations were employed to directly test the complete triad of cognitive factors, depressive symptom severity, and subnetwork-specific connectivity strength. Results All cognitive factors were associated with global connectivity strength. For each cognitive factor, network-based statistics identified a subnetwork of connections, revealing, for example, a subnetwork positively associated with processing speed. Within that subnetwork, acutely depressed patients showed significantly reduced connectivity strength compared to healthy controls. Moreover, connectivity strength in that subnetwork was associated to current depressive symptom severity independent of the previous disease course. Conclusions Our study is the first to identify cognition-related structural brain networks in MDD patients, thereby revealing associations between cognitive deficits, depressive symptoms, and reduced structural connectivity. This supports the hypothesis that structural connectome alterations may mediate the association of cognitive deficits and depression severity.

Neuroimaging studies point to neurostructural abnormalities in youth with anxiety disorders. Yet, findings are based on small-scale studies, often with small effect sizes, and have limited generalizability and clinical relevance. These issues have prompted a paradigm shift in the field towards highly powered (i.e., big data) individual-level inferences, which are data-driven, transdiagnostic, and neurobiologically informed. Here, we built and validated neurostructural machine learning (ML) models for individual-level inferences based on the largest-ever multi-site neuroimaging sample of youth with anxiety disorders (age: 10-25 years, N=3,343 individuals from 32 global sites), as compiled by three ENIGMA Anxiety Working Groups: Panic Disorder (PD), Generalized Anxiety Disorder (GAD), and Social Anxiety Disorder (SAD). ML classifiers were trained on MRI-derived regional measures of cortical thickness, surface area, and subcortical volumes to classify patients and healthy controls (HC) for each anxiety disorder separately and across disorders (transdiagnostic classification). Modest, yet robust, classification performance was achieved for PD vs. HC (AUC=0.62), but other disorder-specific and transdiagnostic classifications were not significantly different from chance. However, above chance-level transdiagnostic classifications were obtained in exploratory subgroup analyses of male patients vs. male HC, unmedicated patients vs. HC, and patients with low anxiety severity vs. HC (AUC 0.59-0.63). The above chance-level classifications were based on plausible and specific neuroanatomical features in fronto-striato-limbic and temporo-parietal regions. This study provides a realistic estimate of classification performance in a large, ecologically valid, multi-site sample of youth with anxiety disorders, and may as such serve as a benchmark.

Background: Cognitive reserve (CR) describes the brain's ability to buffer negative effects of disease-related structural brain alterations on cognitive functioning. Here, we analyze CR in major depressive disorder (MDD) by determining its effects on cognitive impairment, its ability to moderate the association between brain network pathology and cognitive performance, and its utility in predicting future disease progression in a two-year follow-up.Methods: CR of 539 MDD patients and 549 healthy controls was estimated from educational years, occupational attainment, and verbal IQ. We examined the effect of CR on cognitive impairment using logistic regression. We tested whether CR moderates the association of magnetic resonance imaging-based structural connectome alterations and cognitive impairment. Disease course characteristics from a two-year follow-up were predicted from baseline CR, which was benchmarked against predictive effects of environmental (childhood maltreatment) and genetic (polygenetic risk for MDD) determinants of disease course.Results: Low CR was associated with increased cognitive impairment, especially in MDD patients. CR moderated the association of mean cognitive performance with connectivity strength in a network of white matter connections. Low baseline CR predicted increased duration of depressive episodes and symptom severity after two years with predictive effect sizes on par with genetic and environmental risk factors.Conclusions: CR helps to identify MDD patients at risk for debilitating cognitive impairment and worse future disease trajectory. The moderating effect of CR on the cognition-connectome association suggests CR buffers neuropathological effects in MDD. These results call for a greater appreciation of CR in personalized treatment decisions and demand research agendas dedicated to investigating potential treatments focusing on CR.

Symptom fluctuation is one of the hallmarks of major depression. However, a principled, quantitative framework explaining when and how symptoms change remains elusive. Following a complex systems perspective, we model clusters of depressive symptomatology across N = 2059 longitudinal biweekly measurements using a mobile phone app from N = 113 patients based on Beck Depression Inventory-I (BDI-I). We show that 1) the five largest clusters cover more than 60% of all symptom profiles, 2) in close to 90% of all cases, major temporal fluctuations in depression scores (i.e. sum of BDI-I items) coincides temporally with the change between the symptom clusters, 3) the probability of an approaching transition increases the further away the symptoms move from the center of the clusters (r = 0.78, p < 0.008), and 4) the approaching symptoms clusters are those that are closest to the BDI measurement at any given moment (r = 0.42, p < 0.0001). Taken together these results support the notion that symptom fluctuations can be predicted with high precision, potentially opening the possibility to augment interventions by predicting upcoming shifts in symptom structures.

BackgroundControllability is a measure of the brain’s ability to orchestrate neural activity which can be quantified in terms of properties of the brain’s network connectivity. Evidence from the literature suggests that aging can exert a general effect on whole-brain controllability. Mounting evidence, on the other hand, suggests that parenthood and motherhood in particular lead to long-lasting changes in brain architecture that effectively slow down brain aging. We hypothesize that parenthood might preserve brain controllability properties from aging.MethodsIn a sample of 814 healthy individuals (aged 33.9±12.7 years, 522 females), we estimate whole-brain controllability and compare the aging effects in subjects with vs. those without children. We use diffusion tensor imaging (DTI) to estimate the brain structural connectome. The level of brain control is then calculated from the connectomic properties of the brain structure. Specifically, we measure the network control over many low-energy state transitions (average controllability) and the network control over difficult-to-reach states (modal controllability).Results and conclusionIn nulliparous females, whole-brain average controllability increases, and modal controllability decreases with age, a trend that we do not observe in parous females. Statistical comparison of the controllability metrics shows that modal controllability is higher and average controllability is lower in parous females compared to nulliparous females. In men, we observed the same trend, but the difference between nulliparous and parous males do not reach statistical significance. Our results provide strong evidence that parenthood contradicts aging effects on brain controllability and the effect is stronger in mothers.

Anorexia nervosa is characterised by high relapse rates and thus there is a need for strategies that reduce reoccurrence of illness. One way of achieving this is to integrate relapse prevention into treatment, but clearly this requires identification of risk and maintenance factors. The Maudsley Model of Anorexia Nervosa Treatment in Adults (MANTRA) by SchmidtTreasure has 5 major treatment stages. These include an initial stage of motivation and dialogue about change, an individual relapse formulation, improvement of cognitive and socio-emotional skills, work on the patient's identity and eventually a final stage of ending and parting. These treatment stages are derived from a maintenance model of AN by SchmidtTreasure and on evidence from recovered patients and part of their objective is to prevent relapse.