Advanced diffusion imaging in grey matter reflects individual differences in cognitive ability in older adults.

Abstract

Diffusion Weighted Imaging is a tool that can non-invasively provide insights into the microstructure of a given brain region. Various advanced techniques exist within the diffusion weighted imaging space that each provide valuable insights into different aspects of microstructure. In the following study, we sought to examine whether the combination of derived diffusion metrics (tensors, neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator (MAP) MRI) in grey-matter regions could reliably predict cognitive performance in older adults, and whether these findings were replicable across datasets. First, we demonstrated that all combinations of diffusion metrics could reliably determine participant characteristics and were significant predictors of age. Second, we found that a combination of Tensor, NODDI, and MAP-MRI metrics within the hippocampus could predict RAVLT performance in older adults above and beyond any combination of two metrics alone. We also found these diffusion metrics were able to reliably predict RAVLT performance, but not Trails B or Digit Symbol Substitution Task performance. We also found that these same combinations of metrics could predict working memory performance, but not memory performance within a region associated with working memory (Brodmann Areas 9 and 46). Taken together, these findings indicate that these diffusion metrics provide valuable information on grey-matter microstructure independent of one another, and that the ability to obtain both NODDI and MAP-MRI based information from multi-shell diffusion scans more than justifies the added length.