Importance Major depressive disorder (MDD) has been linked to imbalanced communication among large-scale brain networks, as reflected by abnormal resting-state functional connectivity (rsFC). However, given variable methods and results across studies, identifying consistent patterns of network dysfunction in MDD has been elusive. Objective To investigate network dysfunction in MDD through a meta-analysis of rsFC studies. Data Sources Seed-based voxelwise rsFC studies comparing individuals with MDD with healthy controls (published before June 30, 2014) were retrieved from electronic databases (PubMed, Web of Science, and EMBASE) and authors contacted for additional data. Study Selection Twenty-seven seed-based voxel-wise rsFC data sets from 25 publications (556 individuals with MDD and 518 healthy controls) were included in the meta-analysis. Data Extraction and Synthesis Coordinates of seed regions of interest and between-group effects were extracted. Seeds were categorized into seed-networks by their location within a priori functional networks. Multilevel kernel density analysis of between-group effects identified brain systems in which MDD was associated with hyperconnectivity (increased positive or reduced negative connectivity) or hypoconnectivity (increased negative or reduced positive connectivity) with each seed-network. Results Major depressive disorder was characterized by hypoconnectivity within the frontoparietal network, a set of regions involved in cognitive control of attention and emotion regulation, and hypoconnectivity between frontoparietal systems and parietal regions of the dorsal attention network involved in attending to the external environment. Major depressive disorder was also associated with hyperconnectivity within the default network, a network believed to support internally oriented and self-referential thought, and hyperconnectivity between frontoparietal control systems and regions of the default network. Finally, the MDD groups exhibited hypoconnectivity between neural systems involved in processing emotion or salience and midline cortical regions that may mediate top-down regulation of such functions. Conclusions and Relevance Reduced connectivity within frontoparietal control systems and imbalanced connectivity between control systems and networks involved in internal or external attention may reflect depressive biases toward internal thoughts at the cost of engaging with the external world. Meanwhile, altered connectivity between neural systems involved in cognitive control and those that support salience or emotion processing may relate to deficits regulating mood. These findings provide an empirical foundation for a neurocognitive model in which network dysfunction underlies core cognitive and affective abnormalities in depression.

https://jamanetwork.com/journals/jamapsychiatry/articlepdf/2203837/yoi150007.pdf

Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional Connectivity.

Anxiety disorders, including panic disorder with or without agoraphobia, generalized anxiety disorder, social anxiety disorder, specific phobias, and separation anxiety disorder, are the most prevalent mental disorders and are associated with immense health care costs and a high burden of disease. According to large population-based surveys, up to 33.7% of the population are affected by an anxiety disorder during their lifetime. Substantial underrecognition and undertreatment of these disorders have been demonstrated. There is no evidence that the prevalence rates of anxiety disorders have changed in the past years. In cross-cultural comparisons, prevalence rates are highly variable. It is more likely that this heterogeneity is due to differences in methodology than to cultural influences. Anxiety disorders follow a chronic course; however, there is a natural decrease in prevalence rates with older age. Anxiety disorders are highly comorbid with other anxiety disorders and other mental disorders.

Epidemiology of anxiety disorders in the 21st century.

Background
Few reports have aimed to describe the mediational effect of cognitive deficits on functional outcomes in major depressive disorder (MDD), and relatively few interventions are demonstrated to mitigate cognitive deficits in MDD.

Methods
Studies enrolling subjects between the ages of 18–65 were selected for review. Bibliographies from identified articles were reviewed to identify additional original reports aligned with our objectives.

Results
Cognitive deficits in MDD are consistent, replicable, nonspecific, and clinically significant. The aggregated estimated effect size of cognitive deficits in MDD is small to medium. Pronounced deficits in executive function (≥1 SD below the normative mean) are evident in ∼20–30% of individuals with MDD). Other replicated abnormalities are in the domains of working memory, attention, and psychomotor processing speed. Mediational studies indicate that cognitive deficits may account for the largest percentage of variance with respect to the link between psychosocial dysfunction (notably workforce performance) and MDD. No conventional antidepressant has been sufficiently studied and/or demonstrated robust procognitive effects in MDD.

Conclusions
Cognitive deficits in MDD are a principal mediator of psychosocial impairment, notably workforce performance. The hazards posed by cognitive deficits in MDD underscore the need to identify a consensus-based neurocognitive battery for research and clinical purposes. Interventions (pharmacological, behavioral, neuromodulatory) that engage multiple physiological systems implicated in cognitive deficits hold promise to reduce, reverse, and prevent cognitive deficits.

Cognitive deficits and functional outcomes in major depressive disorder: determinants, substrates, and treatment interventions.

Background The number of mental health apps (MHapps) developed and now available to smartphone users has increased in recent years. MHapps and other technology-based solutions have the potential to play an important part in the future of mental health care; however, there is no single guide for the development of evidence-based MHapps. Many currently available MHapps lack features that would greatly improve their functionality, or include features that are not optimized. Furthermore, MHapp developers rarely conduct or publish trial-based experimental validation of their apps. Indeed, a previous systematic review revealed a complete lack of trial-based evidence for many of the hundreds of MHapps available. Objective To guide future MHapp development, a set of clear, practical, evidence-based recommendations is presented for MHapp developers to create better, more rigorous apps. Methods A literature review was conducted, scrutinizing research across diverse fields, including mental health interventions, preventative health, mobile health, and mobile app design. Results Sixteen recommendations were formulated. Evidence for each recommendation is discussed, and guidance on how these recommendations might be integrated into the overall design of an MHapp is offered. Each recommendation is rated on the basis of the strength of associated evidence. It is important to design an MHapp using a behavioral plan and interactive framework that encourages the user to engage with the app; thus, it may not be possible to incorporate all 16 recommendations into a single MHapp. Conclusions Randomized controlled trials are required to validate future MHapps and the principles upon which they are designed, and to further investigate the recommendations presented in this review. Effective MHapps are required to help prevent mental health problems and to ease the burden on health systems.

/pdf/mental-health-smartphone-apps-review-and-evidence-based-3qgx7xdfov.pdf

Mental Health Smartphone Apps: Review and Evidence-Based Recommendations for Future Developments

The effects of social deprivation on adolescent development and mental health.

Estimates of 12-month and lifetime prevalence and of lifetime morbid risk (LMR) of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) anxiety and mood disorders are presented based on US epidemiological surveys among people aged 13+. The presentation is designed for use in the upcoming DSM-5 manual to provide more coherent estimates than would otherwise be available. Prevalence estimates are presented for the age groups proposed by DSM-5 workgroups as the most useful to consider for policy planning purposes. The LMR/12-month prevalence estimates ranked by frequency are as follows: major depressive episode: 29.9%/8.6%; specific phobia: 18.4/12.1%; social phobia: 13.0/7.4%; post-traumatic stress disorder: 10.1/3.7%; generalized anxiety disorder: 9.0/2.0%; separation anxiety disorder: 8.7/1.2%; panic disorder: 6.8%/2.4%; bipolar disorder: 4.1/1.8%; agoraphobia: 3.7/1.7%; obsessive-compulsive disorder: 2.7/1.2. Four broad patterns of results are most noteworthy: first, that the most common (lifetime prevalence/morbid risk) lifetime anxiety-mood disorders in the United States are major depression (16.6/29.9%), specific phobia (15.6/18.4%), and social phobia (10.7/13.0%) and the least common are agoraphobia (2.5/3.7%) and obsessive-compulsive disorder (2.3/2.7%); second, that the anxiety-mood disorders with the earlier median ages-of-onset are phobias and separation anxiety disorder (ages 15-17) and those with the latest are panic disorder, major depression, and generalized anxiety disorder (ages 23-30); third, that LMR is considerably higher than lifetime prevalence for most anxiety-mood disorders, although the magnitude of this difference is much higher for disorders with later than earlier ages-of-onset; and fourth, that the ratio of 12-month to lifetime prevalence, roughly characterizing persistence, varies meaningfully in ways consistent with independent evidence about differential persistence of these disorders.

Hans Ullrich Wittchen

Papers

Twelve‐month and lifetime prevalence and lifetime morbid risk of anxiety and mood disorders in the United States.