Intrinsic functional connectivity pattern based brain parcellation using normalized cut

In imaging data based brain network analysis, a necessary precursor for constructing meaningful brain networks is to identify functionally homogeneous regions of interest (ROIs) for defining network nodes. For parcellating the brain based on resting state fMRI data, normalized cut is one widely used clustering algorithm which groups voxels according to the similarity of functional signals. Due to low signal to noise ratio (SNR) of resting state fMRI signals, spatial constraint is often applied to functional similarity measures to generate smooth parcellation. However, improper spatial constraint might alter the intrinsic functional connectivity pattern, thus yielding biased parcellation results. To achieve reliable and least biased parcellation of the brain, we propose an optimization method for the spatial constraint to functional similarity measures in normalized cut based brain parcellation. Particularly, we first identify the space of all possible spatial constraints that are able to generate smooth parcellation, then find the spatial constraint that leads to the brain parcellation least biased from the intrinsic function pattern based parcellation, measured by the minimal Ncut value calculated based on the functional similarity measure of original functional signals. The proposed method has been applied to the parcellation of medial superior frontal cortex for 20 subjects based on their resting state fMRI data. The experiment results indicate that our method can generate meaningful parcellation results, consistent with existing functional anatomy knowledge.