Keresztes, A. | bioRxiv Science

bioRxiv2020

Tracking age differences in neural distinctiveness across representational levels

The authors have withdrawn the preprint due to two errors in the analyses: The first error was a failure to account for three dummy volumes (TR = 2s) collected at the beginning of each experimental run. The second error was a coding error related to the definition of the category-selective clusters. Since correcting for these errors drastically altered the results and conclusions of this paper, the authors have withdrawn the preprint and retracted the published paper. More information as well as a report comparing original and corrected results can be found on https://osf.io/t8dpv/. If you have any questions please contact the corresponding author (sander@mpib-berlin.mpg.de).

neuroscience↗

bioRxiv2019

Hippocampal subfields and limbic white matter jointly predict learning rate in older adults

Age-related memory impairments have been linked to differences in structural brain parameters, including cerebral white matter (WM) microstructure and hippocampal (HC) volume, but their combined influences are rarely investigated. In a population-based sample of 337 older participants 61-82 years of age (Mage=69.66, SDage=3.92 years) we modeled the independent and joint effects of limbic WM microstructure and HC subfield volumes on verbal learning. Participants completed a verbal learning task over five learning trials and underwent magnetic resonance imaging (MRI), including structural and diffusion scans. We segmented three HC subregions on high-resolution MRI data and sampled mean fractional anisotropy (FA) from bilateral limbic WM tracts identified via deterministic fiber tractography. Using structural equation modeling, we evaluated the associations between learning rate and latent factors representing FA sampled from limbic WM tracts, and HC subfield volumes, as well as their latent interaction. Results showed limbic WM and the interaction of HC and WM - but not HC volume alone - predicted verbal learning rates. Model decomposition revealed HC volume is only positively associated with learning rate in individuals with higher levels of WM anisotropy. We conclude that structural characteristics of limbic WM regions and HC volume jointly contribute to verbal learning in older adults.

neuroscience↗