A construction of an averaged representation of human cortical gyri using non-linear principal component analysis

Because of the complex shape of human cortical gyri and great variation between individuals, development of effective representation schemes which allow establishment of correspondence between individuals, extraction of average structure of a population, and co-registration has proved very difficult. We introduce an approach which extracts line representations of gyri at different depths from high resolution MRI, labels main gyri semi-automatically, and extracts a template from a population using non-linear principal component analysis. The method has been tested on data from 96 healthy human volunteers. The model captures the most salient shape features of all major cortical gyri, and can be used for inter-subject registration, for investigating regionalized inter-subject variability, and for inter-hemispheric comparisons.