A Continuous Flow-Maximisation Approach to Connectivity-Driven Cortical Parcellation

Brain connectivity network analysis is a key step towards understanding the processes behind the brain’s development through ageing and disease. Parcellation of the cortical surface into distinct regions is an essential step in order to construct such networks. Anatomical and random parcellations are typically used for this task, but can introduce a bias and may not be aligned with the brain’s underlying organisation. To tackle this challenge, connectivity-driven parcellation methods have received increasing attention. In this paper, we propose a flexible continuous flow maximisation approach for connectivity driven parcellation that iteratively updates the parcels’ boundaries and centres based on connectivity information and smoothness constraints. We evaluate the method on 25 subjects with diffusion MRI data. Quantitative results show that the method is robust with respect to initialisation (average overlap 82%) and significantly outperforms the state of the art in terms of information loss and homogeneity.