Background Antidepressant medications (ADMs) are widely used and long-term use is increasing. Given this extensive use and recommendation of ADMs in guidelines, one would expect ADMs to be... Show moreBackground Antidepressant medications (ADMs) are widely used and long-term use is increasing. Given this extensive use and recommendation of ADMs in guidelines, one would expect ADMs to be universally considered effective. Surprisingly, that is not the case; fierce debate on their benefits and harms continues. This editorial seeks to understand why the controversy continues and how consensus can be achieved. Methods 'Position' paper. Critical analysis and synthesis of relevant literature. Results Advocates point at ADMs impressive effect size (number needed to treat, NNT = 6-8) in acute phase treatment and continuation/maintenance ADM treatment prevention relapse/recurrence in acute phase ADM responders (NNT = 3-4). Critics point at the limited clinically significant surplus value of ADMs relative to placebo and argue that effectiveness is overstated. We identified multiple factors that fuel the controversy: certainty of evidence is low to moderate; modest efficacy on top of strong placebo effects allows critics to focus on small net efficacy and advocates on large gross efficacy; ADM withdrawal symptoms masquerade as relapse/recurrence; lack of association between ADM treatment and long-term outcome in observational databases. Similar problems affect psychological treatments as well, but less so. We recommend four approaches to resolve the controversy: (1) placebo-controlled trials with relevant long-term outcome assessments, (2) inventive analyses of observational databases, (3) patient cohort studies including effect moderators to improve personalized treatment, and (4) psychological treatments as universal first-line treatment step. Conclusions Given the public health significance of depression and increased long-term ADM usage, new approaches are needed to resolve the controversy. Show less
BackgroundResearch in depression has progressed rapidly over the past four decades. Yet depression rates are not subsiding and treatment success is not improving. We examine the extent to which the... Show moreBackgroundResearch in depression has progressed rapidly over the past four decades. Yet depression rates are not subsiding and treatment success is not improving. We examine the extent to which the gap between science and practice is associated with the level of integration in how depression is considered in research and stakeholder-relevant documents.MethodsWe used a network-science perspective to analyze similar uses of depression relevant terms in the Google News corpus (approximately 1 billion words) and the Web of Science database (120 000 documents).ResultsThese analyses yielded consistent pictures of insular modules associated with: (1) patient/providers, (2) academics, and (3) industry. Within academia insular modules associated with psychology, general medical, and psychiatry/neuroscience/biology were also detected.ConclusionsThese analyses suggest that the domain of depression is fragmented, and that advancements of relevance to one stakeholder group (academics, industry, or patients) may not translate to the others. We consider potential causes and associated responses to this fragmentation that could help to unify and advance translation from research on depression to the clinic, largely involving harmonizing employed language, bridging conceptual domains, and increasing communication across stakeholder groups. Show less
Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common... Show moreAlthough it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (< 0.1 Hz)fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individual's "functional connectome." Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individual's functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain-behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. High-throughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at www.nitrc.org/projects/fcon_1000/. Show less
