A Cortical and Sub-cortical Parcellation Clustering by Intrinsic Functional Connectivity

Lin, Ying-Chia and Gili, Tommaso and Tsaftaris, Sotirios A. and Gabrielli, Andrea and Iorio, Mariangela and Spalletta, Gianfranco and Caldarelli, Guido A Cortical and Sub-cortical Parcellation Clustering by Intrinsic Functional Connectivity. In: XXIII National Congress of the Italian Society of Psychophysiology, November 19-21, 2015, Lucca, Italy pp. 113-114. ISSN 1970-321X. (2015)

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Abstract

Network analysis of resting-state fMRI (rsfMRI) has been widely utilized to investigate the functional architecture of the whole brain. Such analysis can divide the brain into several discrete elements (nodes) connected by links (edges) representing the relation between two elements. The brain cortical and subcortical areas can be segmented or parcelled into several functional and/or structural regions. The connectome analysis of human-brain structure and functional connectivity provides a unique opportunity to understand the organisation of brain networks. However, such analyses require an appropriate definition of functional or structural nodes to efficiently represent cortical regions. In order to address this issue, here we propose a robust parcellation method based on resting-state fMRI, which can be generalized from the single-subject level to the multi-group one. Considering the input data of a single subject and constructing multi-resolution graph elements. We combined voting-based measurements to divide the cortical region into sub-regions in order to obtain the whole brain parcellation. Our parcellation relies on majority vote and poses spatial constraints within a hierarchical agglomerative clustering framework to define parcels that are spatially homogeneous. We used rsfMRI data collected from 40 healthy subjects and we showed that our purposed algorithm is able to compute stable and reproducible parcellations across the group of subjects at multi-resolution level. We find that, even though previous methods ensure on average larger overlap between parcels and regions in AAL atlas, the method proposed herein reduces inter-subject variability, especially when the number of parcels increases. Our high-resolution parcels seem to be functionally more consistent and reliable and can be a useful tool for future analysis that will aim to match functional and structural architecture of the brain.

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