Both normal aging and neurodegenerative diseases such as Alzheimer's disease cause morphological changes of the brain. To better distinguish between normal and abnormal cases, it is necessary to model changes in brain morphology owing to normal aging.

To this end, we developed a method for analyzing and visualizing these changes for the entire brain morphology distribution in a general ageing population. The method uses a groupwise image registration in which brain scans are simultaneously registered to a common template space. Using partial least squares regression (PLSR), the components of deformation with the highest covariance with age are estimated, yielding corresponding scores for each loading. With the LMS method we model the distribution of these scores by calculating smooth percentiles as a function of age. Using the PLSR loadings and the percentiles we calculate smooth spatiotemporal deformations, allowing for visualization of the brain morphology distribution of the population across aging.

The method is applied to 1000 subjects from a large population imaging study in the elderly and the resulting percentile curves and the spatiotemporal atlases are publicly available on this web page.


W. Huizinga, D.H.J. Poot, G. Roshchupkin, E.E. Bron, M.A. Ikram, M.W. Vernooij, D. Rueckert, W.J. Niessen and S. Klein, Modeling the brain morphology distribution in the general aging population, Proc. SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 97880I (March 29, 2016); doi:10.1117/12.2207228

Full text can be downloaded at: researchgate.