Abstract #1661

An algorithm for generating uniform random parcellations

Hu Cheng¹, Andrea Koenigsberger¹, Sharlene Newman¹, and Olaf Sporns¹

¹Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States

Random parcellations have some advantages over template-based parcellations in network analysis of the brain. An important criterion for assessing the “goodness” of a random parcellation is the parcel size variability. A new algorithm is proposed to create more homogeneous random parcellations than previously reported. The new algorithm takes the actual distance between voxels and local voxel density into account in placing the random seeds. With many random parcellations using our approach, global network properties exhibit normal distribution and the variability across different repetitions of the random parcellation is comparable with inter-subject variability.

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