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

Area of Science:

• Neuroimaging
• Neuroscience
• Biomedical Engineering

Background:

• Diffusion Weighted Imaging (DWI) offers non-invasive insights into brain microstructure.
• Advanced DWI techniques like tensor, NODDI, and MAP-MRI provide distinct microstructural information.
• Understanding grey matter microstructure's link to cognition in aging is crucial.

Purpose of the Study:

• To investigate if combined DWI metrics (tensor, NODDI, MAP-MRI) predict cognitive performance in older adults.
• To assess the replicability of these findings across different datasets.
• To determine if combined metrics offer predictive power beyond individual metrics.

Main Methods:

• Utilized diffusion tensor imaging, neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator (MAP) MRI.
• Applied these metrics to grey matter regions in older adults.
• Correlated diffusion metrics with cognitive performance measures (RAVLT, Trails B, Digit Symbol Substitution Task, working memory).

Main Results:

• All diffusion metric combinations reliably predicted participant characteristics and were significant predictors of age.
• A combination of Tensor, NODDI, and MAP-MRI metrics in the hippocampus significantly predicted RAVLT performance.
• Combined metrics predicted working memory performance but not performance in specific prefrontal regions (Brodmann Areas 9 and 46).

Conclusions:

• Individual diffusion metrics (tensor, NODDI, MAP-MRI) provide valuable, independent information on grey matter microstructure.
• Combining NODDI and MAP-MRI derived information from multi-shell diffusion scans enhances predictive power for cognitive performance.
• The added scan time for multi-shell acquisitions is justified by the improved insights into brain microstructure and cognition.