Transparent Meta-Analysis: Does Aging Spare Prospective Memory with Focal vs. Non-Focal Cues?

Background: Prospective memory (ProM) is the ability to become aware of a previously-formed plan at the right time and place. For over twenty years, researchers have been debating whether prospective memory declines with aging or whether it is spared by aging and, most recently, whether aging spares prospective memory with focal vs. non-focal cues. Two recent meta-analyses examining these claims did not include all relevant studies and ignored prevalent ceiling effects, age confounds, and did not distinguish between prospective memory subdomains (e.g., ProM proper, vigilance, habitual ProM) (see Uttl, 2008, PLoS ONE). The present meta-analysis focuses on the following questions: Does prospective memory decline with aging? Does prospective memory with focal vs. non-focal cues decline with aging? Does the size of age-related declines with focal vs. non-focal cues vary across ProM subdomains? And are age-related declines in ProM smaller than agerelated declines in retrospective memory? Methods and Findings: A meta-analysis of event-cued ProM using data visualization and modeling, robust count methods, and conventional meta-analysis techniques revealed that first, the size of age-related declines in ProM with both focal and non-focal cues are large. Second, age-related declines in ProM with focal cues are larger in ProM proper and smaller in vigilance. Third, age-related declines in ProM proper with focal cues are as large as age-related declines in recall measures of retrospective memory

Identification of individual subjects on the basis of their brain anatomical features

Abstract We examined whether it is possible to identify individual subjects on the basis of brain anatomical features. For this, we analyzed a dataset comprising 191 subjects who were scanned three times over a period of two years. Based on FreeSurfer routines, we generated three datasets covering 148 anatomical regions (cortical thickness, area, volume). These three datasets were also combined to a dataset containing all of these three measures. In addition, we used a dataset comprising 11 composite anatomical measures for which we used larger brain regions (11LBR). These datasets were subjected to a linear discriminant analysis (LDA) and a weighted K-nearest neighbors approach (WKNN) to identify single subjects. For this, we randomly chose a data subset (training set) with which we calculated the individual identification. The obtained results were applied to the remaining sample (test data). In general, we obtained excellent identification results (reasonably good results were obtained for 11LBR using WKNN). Using different data manipulation techniques (adding white Gaussian noise to the test data and changing sample sizes) still revealed very good identification results, particularly for the LDA technique. Interestingly, using the small 11LBR dataset also revealed very good results indicating that the human brain is highly individual