3D statistical neuroanatomical models from 305 MRI volumes

Recently, there has been a rapid growth in the use of 3D multi-modal correlative imaging for studies of the human brain. Regional cerebral blood flow (CBF) changes indicate brain areas involved in stimulus processing. These focal changes are often too small (<10%) to be discerned from a single subject and the experiment is repeated in a series of individuals. To investigate the extent of residual variability the authors have collected over 300 MRI volumetric datasets from normal individuals and transformed these datasets into stereotaxic space using a 3D linear re-sampling algorithm. The authors then generated a series of statistical measures which express this population nonlinear variability in the form of parametric volumes, e.g. mean intensity, intensity variance. A model for anatomical variability, expressed as the width of a Gaussian blurring kernel applied to an ideal single subject, was developed and tested against the observed data.<>