Differences in navigation performance and postpartal striatal volume associated with pregnancy in humans.

Pregnancy is accompanied by prolonged exposure to high estrogen levels. Animal studies have shown that estrogen influences navigation strategies and, hence, affects navigation performance. High estrogen levels are related to increased use of hippocampal-based allocentric strategies and decreased use of striatal-based egocentric strategies. In humans, associations between hormonal shifts and navigation strategies are less well studied. This study compared 30 peripartal women (mean age 28 years) to an age-matched control group on allocentric versus egocentric navigation performance (measured in the last month of pregnancy) and gray matter volume (measured within two months after delivery). None of the women had a previous pregnancy before study participation. Relative to controls, pregnant women performed less well in the egocentric condition of the navigation task, but not the allocentric condition. A whole-brain group comparison revealed smaller left striatal volume (putamen) in the peripartal women. Across the two groups, left striatal volume was associated with superior egocentric over allocentric performance. Limited by the cross-sectional study design, the findings are a first indication that human pregnancy might be accompanied by structural brain changes in navigation-related neural systems and concomitant changes in navigation strategy

Predicting age from cortical structure across the lifespan

Despite inter-individual differences in cortical structure, cross-sectional and longitudinal studies have demonstrated a large degree of population-level consistency in age-related differences in brain morphology. The present study assessed how accurately an individual’s age could be predicted by estimates of cortical morphology, comparing a variety of structural measures, including thickness, gyrification, and fractal dimensionality. Structural measures were calculated across up to seven different parcellation approaches, ranging from 1 region to 1000 regions. The age-prediction framework was trained using morphological measures obtained from T1-weighted MRI volumes collected from multiple sites, yielding a training dataset of 1056 healthy adults, aged 18-97. Age predictions were calculated using a machine-learning approach that incorporated non-linear differences over the lifespan. In two independent, held-out test samples, age predictions had a median error of 6-7 years. Age predictions were best when using a combination of cortical metrics, both thickness and fractal dimensionality. Overall, the results reveal that age-related differences in brain structure are systematic enough to enable reliable age prediction based on metrics of cortical morphology