Explore the vast range of titles available.

Clinically useful treatment moderators of Major Depressive Disorder (MDD) have not yet been identified, though some baseline predictors of treatment outcome have been proposed. The aim of iSPOT-D is to identify pretreatment measures that predict or moderate MDD treatment response or remission to escitalopram, sertraline or venlafaxine; and develop a model that incorporates multiple predictors and moderators. The International Study to Predict Optimized Treatment - in Depression (iSPOT-D) is a multi-centre, international, randomized, prospective, open-label trial. It is enrolling 2016 MDD outpatients (ages 18-65) from primary or specialty care practices (672 per treatment arm; 672 age-, sex- and education-matched healthy controls). Study-eligible patients are antidepressant medication (ADM) naïve or willing to undergo a one-week wash-out of any non-protocol ADM, and cannot have had an inadequate response to protocol ADM. Baseline assessments include symptoms; distress; daily function; cognitive performance; electroencephalogram and event-related potentials; heart rate and genetic measures. A subset of these baseline assessments are repeated after eight weeks of treatment. Outcomes include the 17-item Hamilton Rating Scale for Depression (primary) and self-reported depressive symptoms, social functioning, quality of life, emotional regulation, and side-effect burden (secondary). Participants may then enter a naturalistic telephone follow-up at weeks 12, 16, 24 and 52. The first half of the sample will be used to identify potential predictors and moderators, and the second half to replicate and confirm. First enrolment was in December 2008, and is ongoing. iSPOT-D evaluates clinical and biological predictors of treatment response in the largest known sample of MDD collected worldwide. International Study to Predict Optimised Treatment - in Depression (iSPOT-D) ClinicalTrials.gov Identifier: NCT00693849. URL: http://clinicaltrials.gov/ct2/show/NCT00693849?term=International+Study+to+Predict+Optimized+Treatment+for+Depression&rank=1

Approximately 50% of patients with major depressive disorder (MDD) do not respond optimally to antidepressant treatments. Given this is a large proportion of the patient population, pretreatment tests that predict which patients will respond to which types of treatment could save time, money and patient burden. Brain imaging offers a means to identify treatment predictors that are grounded in the neurobiology of the treatment and the pathophysiology of MDD. The international Study to Predict Optimized Treatment in Depression is a multi-center, parallel model, randomized clinical trial with an embedded imaging sub-study to identify such predictors. We focus on brain circuits implicated in major depressive disorder and its treatment. In the full trial, depressed participants are randomized to receive escitalopram, sertraline or venlafaxine-XR (open-label). They are assessed using standardized multiple clinical, cognitive-emotional behavioral, electroencephalographic and genetic measures at baseline and at eight weeks post-treatment. Overall, 2,016 depressed participants (18 to 65 years old) will enter the study, of whom a target of 10% will be recruited into the brain imaging sub-study (approximately 67 participants in each treatment arm) and 67 controls. The imaging sub-study is conducted at the University of Sydney and at Stanford University. Structural studies include high-resolution three-dimensional T1-weighted, diffusion tensor and T2/Proton Density scans. Functional studies include standardized functional magnetic resonance imaging (MRI) with three cognitive tasks (auditory oddball, a continuous performance task, and Go-NoGo) and two emotion tasks (unmasked conscious and masked non-conscious emotion processing tasks). After eight weeks of treatment, the functional MRI is repeated with the above tasks. We will establish the methods in the first 30 patients. Then we will identify predictors in the first half (n=102), test the findings in the second half, and then extend the analyses to the total sample. International Study to Predict Optimized Treatment--in Depression (iSPOT-D). ClinicalTrials.gov, NCT00693849.

Functional neuroimaging studies have implicated dysregulation of prefrontal circuits in major depressive disorder (MDD), and these circuits are a viable target for predicting treatment outcomes. However, because of the heterogeneity of tasks and samples used in studies to date, it is unclear whether the central dysfunction is one of prefrontal hyperreactivity or hyporeactivity. We used a standardized battery of tasks and protocols for functional magnetic resonance imaging, to identify the common vs the specific prefrontal circuits engaged by these tasks in the same 30 outpatients with MDD compared with 30 matched, healthy control participants, recruited as part of the International Study to Predict Optimized Treatment in Depression (iSPOT-D). Reflecting cognitive neuroscience theory and established evidence, the battery included cognitive tasks designed to assess functions of selective attention, sustained attention-working memory and response inhibition, and emotion tasks to assess explicit conscious and implicit nonconscious viewing of facial emotion. MDD participants were distinguished by a distinctive biosignature of: hypoactivation of the dorsolateral prefrontal cortex during working memory updating and during conscious negative emotion processing; hyperactivation of the dorsomedial prefrontal cortex during working memory and response inhibition cognitive tasks and hypoactivation of the dorsomedial prefrontal during conscious processing of positive emotion. These results show that the use of standardized tasks in the same participants provides a way to tease out prefrontal circuitry dysfunction related to cognitive and emotional functions, and not to methodological or sample variations. These findings provide the frame of reference for identifying prefrontal biomarker predictors of treatment outcomes in MDD.

Reduced gray matter (GM) volume may represent a hallmark of major depressive disorder (MDD) neuropathology, typified by wide-ranging distribution of structural alteration. In the study, we aimed to replicate and extend our previous finding of profound and widespread GM loss in MDD, and evaluate the diagnostic accuracy of a structural biomarker derived from GM volume in an interconnected pattern across the brain. In a sub-study of the International Study to Predict Optimized Treatment in Depression (iSPOT-D), two cohorts of clinically defined MDD participants \"Test\" (n = 98) and \"Replication\" (n = 131) were assessed alongside healthy controls (n = 66). Using 3T MRI T1-weighted volumes, GM volume differences were evaluated using voxel-based morphometry. Sensitivity, specificity, and area under the receiver operating characteristic curve were used to evaluate an MDD diagnostic biomarker based on a precise spatial pattern of GM loss constructed using principal component analysis. We demonstrated a highly conserved symmetric widespread pattern of reduced GM volume in MDD, replicating our previous findings. Three bilateral dominant clusters were observed: Cluster 1: midline/cingulate (GM reduction: Test: 6.4%, Replication: 5.3%), Cluster 2: medial temporal lobe (GM reduction: Test: 8.2%, Replication: 11.9%), Cluster 3: prefrontal cortex (GM reduction: Test: 12.1%, Replication: 23.2%). We developed a biomarker reflecting the global pattern of GM reduction, achieving good diagnostic classification performance (AUC: Test = 0.75, Replication = 0.84). This study establishes that a highly specific pattern of reduced GM volume is a feature of MDD, suggestive of a structural basis for this disease. We introduce and validate a novel diagnostic biomarker based on this pattern.

The author outlines the pros and cons of data sharing for neuroscientists and argues that continued progress in the field will depend on a cultural shift toward making primary data freely available. He argues in favor of distributed databases to maximize the efficient use of data.

Hypothesis driven research has been shown to be an excellent model for pursuing investigations in neuroscience. The Human Genome Project demonstrated the added value of discovery research, especially in areas where large amounts of data are produced. Neuroscience has become a data rich field, and one that would be enhanced by incorporating the discovery approach. Databases, as well as analytical, modeling and simulation tools, will have to be developed, and they will need to be interoperable and federated. This paper presents an overview of the development of the field of neuroscience databases and associate tools: Neuroinformatics. The primary focus is on the impact of NIH funding of this process. The important issues of data sharing, as viewed from the perspective of the scientist and private and public funding organizations, are discussed. Neuroinformatics will provide more than just a sophisticated array of information technologies to help scientists understand and integrate nervous system data. It will make available powerful models of neural functions and facilitate discovery, hypothesis formulation and electronic collaboration.

Few standardized tools are available for time‐efficient screening of emotional health status across diagnostic categories, especially in primary care. We evaluated the 45‐question Brief Risk‐resilience Index for SCreening (BRISC) and the 15‐question mini‐BRISC in identifying poor emotional health and coping capacity across a range of diagnostic groups – compared with a detailed clinical assessment – in a large sample of adult outpatients. Participants 18–60 years of age (n = 1079) recruited from 12 medical research and clinical sites completed the computerized assessments. Three index scores were derived from the full BRISC and the mini‐BRISC: one for risk (negativity–positivity bias) and two for coping (resilience and social capacity). Summed answers were converted to standardized z‐scores. BRISC scores were compared with detailed health assessment and diagnostic interview (for current psychiatric, psychological, and neurological conditions) by clinicians at each site according to diagnostic criteria. Clinicians were blinded to BRISC scores. Clinical assessment stratified participants as having “clinical” (n = 435) or “healthy” (n = 644) diagnostic status. Receiver operating characteristic analyses showed that a z‐score threshold of −1.57 on the full BRISC index of emotional health provided an optimal classification of “clinical” versus “healthy” status (sensitivity: 81.2%, specificity: 92.7%, positive predictive power: 80.2%, and negative predictive power: 93.1%). Comparable findings were revealed for the mini‐BRISC. Negativity–positivity bias index scores contributed the most to prediction. The negativity–positivity index of emotional health was most sensitive to classifying major depressive disorder (100%), posttraumatic stress disorder (95.8%), and panic disorder (88.7%). The BRISC and mini‐BRISC both offer a brief, clinically useful screen to identify individuals at risk of disorders characterized by poor emotion regulation, from those with good emotional health and coping. Few standardized tools are available for time‐efficient screening of emotional health status across diagnostic categories, especially in primary care. We report on the development of the Brief Risk‐resilience Index for SCreening (BRISC) in n = 1079 participants spanning healthy and six diagnostic groups. The BRISC offers a clinically useful screen, for distinguishing individuals with poor emotional regulation from those with good emotional health and coping, with high accuracy (81.2%, specificity: 92.7%, positive predictive power: 80.2%, and negative predictive power: 93.1%).

Abstract Few standardized tools are available for time‐efficient screening of emotional health status across diagnostic categories, especially in primary care. We evaluated the 45‐question Brief Risk‐resilience Index for SC reening ( BRISC ) and the 15‐question mini‐ BRISC in identifying poor emotional health and coping capacity across a range of diagnostic groups – compared with a detailed clinical assessment – in a large sample of adult outpatients. Participants 18–60 years of age ( n  = 1079) recruited from 12 medical research and clinical sites completed the computerized assessments. Three index scores were derived from the full BRISC and the mini‐ BRISC : one for risk (negativity–positivity bias) and two for coping (resilience and social capacity). Summed answers were converted to standardized z ‐scores. BRISC scores were compared with detailed health assessment and diagnostic interview (for current psychiatric, psychological, and neurological conditions) by clinicians at each site according to diagnostic criteria. Clinicians were blinded to BRISC scores. Clinical assessment stratified participants as having “clinical” ( n  = 435) or “healthy” ( n  = 644) diagnostic status. Receiver operating characteristic analyses showed that a z ‐score threshold of −1.57 on the full BRISC index of emotional health provided an optimal classification of “clinical” versus “healthy” status (sensitivity: 81.2%, specificity: 92.7%, positive predictive power: 80.2%, and negative predictive power: 93.1%). Comparable findings were revealed for the mini‐ BRISC . Negativity–positivity bias index scores contributed the most to prediction. The negativity–positivity index of emotional health was most sensitive to classifying major depressive disorder (100%), posttraumatic stress disorder (95.8%), and panic disorder (88.7%). The BRISC and mini‐ BRISC both offer a brief, clinically useful screen to identify individuals at risk of disorders characterized by poor emotion regulation, from those with good emotional health and coping.