Optimizing affinity measures for parcellating brain structures based on resting state fMRI data: a validation on medial superior frontal cortex.

BACKGROUND

Parcellating brain structures into functionally homogeneous subregions based on resting state fMRI data could be achieved by grouping image voxels using clustering algorithms, such as normalized cut. The affinity between brain voxels adopted in the clustering algorithms is typically characterized by a combination of the similarity of their functional signals and their spatial distance with parameters empirically specified. However, improper parameter setting of the affinity measure may result in parcellation results biased to spatial smoothness.

NEW METHOD

To obtain a functionally homogeneous and spatially contiguous brain parcellation result, we propose to optimize the affinity measure of image voxels using a constrained bi-level programming optimization method. Particularly, we first identify the space of all possible parameters that are able to generate spatially contiguous brain parcellation results. Then, within the constrained parameter space we search those leading to the brain parcellation results with optimal functional homogeneity and spatial smoothness.

RESULTS AND COMPARISON WITH EXISTING METHODS

The method has successfully parcellated medial superior frontal cortex into supplementary motor area (SMA) and pre-SMA for 106 subjects based on their resting state fMRI data. These results have been validated through functional connectivity analysis and meta-analysis of existing functional imaging studies and compared with those obtained by state-of-the-art brain parcellation methods.

CONCLUSIONS

The validation results have demonstrated that our method could obtain brain parcellation results consistent with the existing functional anatomy knowledge, and the comparison results have further demonstrated that optimizing affinity measure could improve the brain parcellation's robustness and functional homogeneity.