Temporal isolation of neural processes underlying face preference decisions

Decisions about whether we like someone are often made so rapidly from first impressions that it is difficult to examine the engagement of neural structures at specific points in time. Here, we used a temporally extended decision-making paradigm to examine brain activation with functional MRI (fMRI) at sequential stages of the decision-making process. Activity in reward-related brain structures—the nucleus accumbens (NAC) and orbitofrontal cortex (OFC)—was found to occur at temporally dissociable phases while subjects decided which of two unfamiliar faces they preferred. Increases in activation in the OFC occurred late in the trial, consistent with a role for this area in computing the decision of which face to choose. Signal increases in the NAC occurred early in the trial, consistent with a role for this area in initial preference formation. Moreover, early signal increases in the NAC also occurred while subjects performed a control task (judging face roundness) when these data were analyzed on the basis of which of those faces were subsequently chosen as preferred in a later task. The findings support a model in which rapid, automatic engagement of the NAC conveys a preference signal to the OFC, which in turn is used to guide choice

Design of the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS)

The Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) is a multi‐component epidemiological and neurobiological study designed to generate actionable evidence‐based recommendations to reduce US Army suicides and increase basic knowledge about the determinants of suicidality. This report presents an overview of the designs of the six components of the Army STARRS. These include: an integrated analysis of the Historical Administrative Data Study (HADS) designed to provide data on significant administrative predictors of suicides among the more than 1.6 million soldiers on active duty in 2004–2009; retrospective case‐control studies of suicide attempts and fatalities; separate large‐scale cross‐sectional studies of new soldiers (i.e. those just beginning Basic Combat Training [BCT], who completed self‐administered questionnaires [SAQs] and neurocognitive tests and provided blood samples) and soldiers exclusive of those in BCT (who completed SAQs); a pre‐post deployment study of soldiers in three Brigade Combat Teams about to deploy to Afghanistan (who completed SAQs and provided blood samples) followed multiple times after returning from deployment; and a platform for following up Army STARRS participants who have returned to civilian life. Department of Defense/Army administrative data records are linked with SAQ data to examine prospective associations between self‐reports and subsequent suicidality. The presentation closes with a discussion of the methodological advantages of cross‐component coordination. Copyright © 2013 John Wiley & Sons, Ltd .Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102177/1/mpr1401.pd

Prognostic Indicators of Persistent Post-Concussive Symptoms after Deployment-Related Mild Traumatic Brain Injury: A Prospective Longitudinal Study in U.S. Army Soldiers

Mild traumatic brain injury (mTBI), or concussion, is prevalent in the military. The course of recovery can be highly variable. This study investigates whether deployment-acquired mTBI is associated with subsequent presence and severity of post-concussive symptoms (PCS) and identifies predictors of persistent PCS among US Army personnel who sustained mTBI while deployed to Afghanistan. We used data from a prospective longitudinal survey of soldiers assessed 1?2 months before a 10-month deployment to Afghanistan (T0), on redeployment to the United States (T1), approximately 3 months later (T2), and approximately 9 months later (T3). Outcomes of interest were PCS at T2 and T3. Predictors considered were: sociodemographic factors, number of previous deployments, pre-deployment mental health and TBI history, and mTBI and other military-related stress during the index deployment. The study sample comprised 4518 soldiers, 822 (18.2%) of whom experienced mTBI during the index deployment. After adjusting for demographic, clinical, and deployment-related factors, deployment-acquired mTBI was associated with nearly triple the risk of reporting any PCS and with increased severity of PCS when symptoms were present. Among those who sustained mTBI, severity of PCS at follow-up was associated with history of pre-deployment TBI(s), pre-deployment psychological distress, more severe deployment stress, and loss of consciousness or lapse of memory (versus being ?dazed? only) as a result of deployment-acquired mTBI. In summary, we found that sustaining mTBI increases risk for persistent PCS. Previous TBI(s), pre-deployment psychological distress, severe deployment stress, and loss of consciousness or lapse of memory resulting from mTBI(s) are prognostic indicators of persistent PCS after an index mTBI. These observations may have actionable implications for prevention of chronic sequelae of mTBI in the military and other settings.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140173/1/neu.2015.4320.pd

Field procedures in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS)

The Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) is a multi‐component epidemiological and neurobiological study of unprecedented size and complexity designed to generate actionable evidence‐based recommendations to reduce US Army suicides and increase basic knowledge about determinants of suicidality by carrying out coordinated component studies. A number of major logistical challenges were faced in implementing these studies. The current report presents an overview of the approaches taken to meet these challenges, with a special focus on the field procedures used to implement the component studies. As detailed in the paper, these challenges were addressed at the onset of the initiative by establishing an Executive Committee, a Data Coordination Center (the Survey Research Center [SRC] at the University of Michigan), and study‐specific design and analysis teams that worked with staff on instrumentation and field procedures. SRC staff, in turn, worked with the Office of the Deputy Under Secretary of the Army (ODUSA) and local Army Points of Contact (POCs) to address logistical issues and facilitate data collection. These structures, coupled with careful fieldworker training, supervision, and piloting, contributed to the major Army STARRS data collection efforts having higher response rates than previous large‐scale studies of comparable military samples. Copyright © 2013 John Wiley & Sons, Ltd .Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102238/1/mpr1400.pd

Response bias, weighting adjustments, and design effects in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS)

The Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) is a multi‐component epidemiological and neurobiological study designed to generate actionable recommendations to reduce US Army suicides and increase knowledge about determinants of suicidality. Three Army STARRS component studies are large‐scale surveys: one of new soldiers prior to beginning Basic Combat Training (BCT; n  = 50,765 completed self‐administered questionnaires); another of other soldiers exclusive of those in BCT ( n  = 35,372); and a third of three Brigade Combat Teams about to deploy to Afghanistan who are being followed multiple times after returning from deployment ( n  = 9421). Although the response rates in these surveys are quite good (72.0–90.8%), questions can be raised about sample biases in estimating prevalence of mental disorders and suicidality, the main outcomes of the surveys based on evidence that people in the general population with mental disorders are under‐represented in community surveys. This paper presents the results of analyses designed to determine whether such bias exists in the Army STARRS surveys and, if so, to develop weights to correct for these biases. Data are also presented on sample inefficiencies introduced by weighting and sample clustering and on analyses of the trade‐off between bias and efficiency in weight trimming. Copyright © 2013 John Wiley & Sons, Ltd .Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102203/1/mpr1399.pd

Incident hyperglycaemia among older adults with or at-risk for HIV infection

HIV infection has been associated with development of prediabetes and diabetes. Optimum screening practices for these disorders in HIV-infected populations remain unclear

Including information about co-morbidity in estimates of disease burden: results from the World Health Organization World Mental Health Surveys

Background The methodology commonly used to estimate disease burden, featuring ratings of severity of individual conditions, has been criticized for ignoring co-morbidity. A methodology that addresses this problem is proposed and illustrated here with data from the World Health Organization World Mental Health Surveys. Although the analysis is based on self-reports about one's own conditions in a community survey, the logic applies equally well to analysis of hypothetical vignettes describing co-morbid condition profiles. Method Face-to-face interviews in 13 countries (six developing, nine developed; n=31 067; response rate=69.6%) assessed 10 classes of chronic physical and nine of mental conditions. A visual analog scale (VAS) was used to assess overall perceived health. Multiple regression analysis with interactions for co-morbidity was used to estimate associations of conditions with VAS. Simulation was used to estimate condition-specific effects. Results The best-fitting model included condition main effects and interactions of types by numbers of conditions. Neurological conditions, insomnia and major depression were rated most severe. Adjustment for co-morbidity reduced condition-specific estimates with substantial between-condition variation (0.24-0.70 ratios of condition-specific estimates with and without adjustment for co-morbidity). The societal-level burden rankings were quite different from the individual-level rankings, with the highest societal-level rankings associated with conditions having high prevalence rather than high individual-level severity. Conclusions Plausible estimates of disorder-specific effects on VAS can be obtained using methods that adjust for co-morbidity. These adjustments substantially influence condition-specific rating

Clinical reappraisal of the Composite International Diagnostic Interview Screening Scales (CIDI‐SC) in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS)

A clinical reappraisal study was carried out in conjunction with the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) All‐Army Study (AAS) to evaluate concordance of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM‐IV) diagnoses based on the Composite International Diagnostic Interview Screening Scales (CIDI‐SC) and post‐traumatic stress disorder (PTSD) checklist (PCL) with diagnoses based on independent clinical reappraisal interviews (Structured Clinical Interview for DSM‐IV [SCID]). Diagnoses included: lifetime mania/hypomania, panic disorder, and intermittent explosive disorder; six‐month adult attention‐deficit/hyperactivity disorder; and 30‐day major depressive episode, generalized anxiety disorder, PTSD, and substance (alcohol or drug) use disorder (abuse or dependence). The sample ( n  = 460) was weighted for over‐sampling CIDI‐SC/PCL screened positives. Diagnostic thresholds were set to equalize false positives and false negatives. Good individual‐level concordance was found between CIDI‐SC/PCL and SCID diagnoses at these thresholds (area under curve [AUC] = 0.69–0.79). AUC was considerably higher for continuous than dichotomous screening scale scores (AUC = 0.80–0.90), arguing for substantive analyses using not only dichotomous case designations but also continuous measures of predicted probabilities of clinical diagnoses. Copyright © 2013 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102145/1/mpr1398.pd