Insights about personality traits and cognitive performance and decline in adults 51-59 Years old from the Wisconsin longitudinal study.

OBJECTIVES: To delineate midlife personality dimensions of early cognitive change in an age-homogenous sample of U.S. older adults. DESIGN: Longitudinal study of 6133 adults from the Wisconsin Longitudinal Study (WLS). MEASURES: Middle-aged participants (mean age = 53.2; SD = 0.6) from the WLS completed the 'Big-5' personality assessment in 1992. Mixed effects models examined whether midlife personality traits were associated with change in cognitive performance from participant's mid-60s (2004-2005) to early 70s (2011). The cognitive battery assessed abstract reasoning (AR), category fluency (CF), working memory (WM), and delayed verbal memory (DVM). Models adjusted for sex, education, and subjective health. RESULTS: High Openness was a significant predictor of change in AR, CF, and DVM. These cognitive outcomes declined less among those with high Openness, but the effect sizes for Openness by time were small (R2 s < 0.01). AR and CF were characterized by higher overall performance with high Openness, but with relatively parallel change for the highest and lowest Openness quartiles. There was no advantage of Openness to DVM by the second assessment. High Conscientiousness was a predictor of more change for DVM, though the effect size was small (R 2  < 0.01). CONCLUSIONS: None of the midlife personality traits were uniformly associated with change in cognitive performance in early older adulthood. High midlife Openness had the most noteworthy impact on cognition. Interventions designed to target Openness have potential to elevate and maintain a higher threshold of performance in some cognitive domains, but may only have a small impact on cognitive change.

DTI measures identify mild and moderate TBI cases among patients with complex health problems: A receiver operating characteristic analysis of U.S. veterans.

Standard MRI methods are often inadequate for identifying mild traumatic brain injury (TBI). Advances in diffusion tensor imaging now provide potential biomarkers of TBI among white matter fascicles (tracts). However, it is still unclear which tracts are most pertinent to TBI diagnosis. This study ranked fiber tracts on their ability to discriminate patients with and without TBI. We acquired diffusion tensor imaging data from military veterans admitted to a polytrauma clinic (Overall n = 109; Age: M = 47.2, SD = 11.3; Male: 88%; TBI: 67%). TBI diagnosis was based on self-report and neurological examination. Fiber tractography analysis produced 20 fiber tracts per patient. Each tract yielded four clinically relevant measures (fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity). We applied receiver operating characteristic (ROC) analyses to identify the most diagnostic tract for each measure. The analyses produced an optimal cutpoint for each tract. We then used kappa coefficients to rate the agreement of each cutpoint with the neurologist's diagnosis. The tract with the highest kappa was most diagnostic. As a check on the ROC results, we performed a stepwise logistic regression on each measure using all 20 tracts as predictors. We also bootstrapped the ROC analyses to compute the 95% confidence intervals for sensitivity, specificity, and the highest kappa coefficients. The ROC analyses identified two fiber tracts as most diagnostic of TBI: the left cingulum (LCG) and the left inferior fronto-occipital fasciculus (LIF). Like ROC, logistic regression identified LCG as most predictive for the FA measure but identified the right anterior thalamic tract (RAT) for the MD, RD, and AD measures. These findings are potentially relevant to the development of TBI biomarkers. Our methods also demonstrate how ROC analysis may be used to identify clinically relevant variables in the TBI population.