Distribution of important and word-cued autobiographical memories in 20-, 35-, and 70-year-old adults.

For word-cued autobiographical memories, older adults had an increase, or bump, from the ages 10 to 30. All age groups had fewer memories from childhood than from other years and a power-function retention for memories from the most recent 10 years. There were no consistent differences in reaction times and rating scale responses across decades. Concrete words cued older memories, but no property of the cues predicted which memories would come from the bump. The 5 most important memories given by 20- and 35-year-old participants were distributed similarly to their word-cued memories, but those given by 70-year-old participants came mostly from the single 20-to-30 decade. No theory fully accounts for the bump.

ENIGMA and the individual: Predicting factors that affect the brain in 35 countries worldwide.

In this review, we discuss recent work by the ENIGMA Consortium (http://enigma.ini.usc.edu) - a global alliance of over 500 scientists spread across 200 institutions in 35 countries collectively analyzing brain imaging, clinical, and genetic data. Initially formed to detect genetic influences on brain measures, ENIGMA has grown to over 30 working groups studying 12 major brain diseases by pooling and comparing brain data. In some of the largest neuroimaging studies to date - of schizophrenia and major depression - ENIGMA has found replicable disease effects on the brain that are consistent worldwide, as well as factors that modulate disease effects. In partnership with other consortia including ADNI, CHARGE, IMAGEN and others(1), ENIGMA's genomic screens - now numbering over 30,000 MRI scans - have revealed at least 8 genetic loci that affect brain volumes. Downstream of gene findings, ENIGMA has revealed how these individual variants - and genetic variants in general - may affect both the brain and risk for a range of diseases. The ENIGMA consortium is discovering factors that consistently affect brain structure and function that will serve as future predictors linking individual brain scans and genomic data. It is generating vast pools of normative data on brain measures - from tens of thousands of people - that may help detect deviations from normal development or aging in specific groups of subjects. We discuss challenges and opportunities in applying these predictors to individual subjects and new cohorts, as well as lessons we have learned in ENIGMA's efforts so far.