Constructing a dictionary of human brain folding patterns.

Brain imaging provides a wealth of information that computers can explore at a massive scale. Categorizing the patterns of the human cortex has been a challenging issue for neuroscience. In this paper, we propose a data mining approach leading to the construction of the first computerized dictionary of cortical folding patterns, from a database of 62 brains. The cortical folds are extracted using BrainVisa open software. The standard sulci are manually identified among the folds. 32 sets of sulci covering the cortex are selected. Clustering techniques are further applied to identify in each set the different patterns observed in the population. After affine global normalization, the geometric distance between sulci of two subjects is calculated using the Iterative Closest Point (ICP) algorithm. The dimension of the resulting distance matrix is reduced using Isomap algorithm. Finally, a dedicated hierarchical clustering algorithm is used to extract out the main patterns. This algorithm provides a score which evaluates the strengths of the patterns found. The score is used to rank the patterns for setting up a dictionary to characterize the variability of cortical anatomy.