Feature-reduction and semi-simulated data in functional connectivity-based cortical parcellation.

Recently, resting-state functional magnetic resonance imaging has been used to parcellate the brain into functionally distinct regions based on the information available in functional connectivity maps. However, brain voxels are not independent units and adjacent voxels are always highly correlated, so functional connectivity maps contain redundant information, which not only impairs the computational efficiency during clustering, but also reduces the accuracy of clustering results. The aim of this study was to propose feature-reduction approaches to reduce the redundancy and to develop semi-simulated data with defined ground truth to evaluate these approaches. We proposed a feature-reduction approach based on the Affinity Propagation Algorithm (APA) and compared it with the classic featurereduction approach based on Principal Component Analysis (PCA). We tested the two approaches to the parcellation of both semi-simulated and real seed regions using the K-means algorithm and designed two experiments to evaluate their noiseresistance. We found that all functional connectivity maps (with/without feature reduction) provided correct information for the parcellation of the semisimulated seed region and the computational efficiency was greatly improved by both featurereduction approaches. Meanwhile, the APA-based feature-reduction approach outperformed the PCAbased approach in noise-resistance. The results suggested that functional connectivity maps can provide correct information for cortical parcellation, and feature-reduction does not significantly change the information. Considering the improvement in computational efficiency and the noise-resistance, feature-reduction of functional connectivity maps before cortical parcellation is both feasible and necessary.