Functional connectivity analysis of cerebellum using spatially constrained spectral clustering

Vasileios C. Pezoulas, Kostas Michalopoulos, Manousos Klados, Sifis Micheloyannis, Nikolaos Bourbakis, Michalis Zervakis

Research output: Contribution to journalArticlepeer-review


The human cerebellum contains almost 50% of the neurons in the brain, although its volume does not exceed 10% of the total brain volume. The goal of this study is to derive the functional network of the cerebellum during the resting-state and then compare the ensuing group networks between males and females. Toward this direction, a spatially constrained version of the classic spectral clustering algorithm is proposed and then compared against conventional spectral graph theory approaches, such as spectral clustering, and N-cut, on synthetic data as well as on resting-state fMRI data obtained from the Human Connectome Project (HCP). The extracted atlas was combined with the anatomical atlas of the cerebellum resulting in a functional atlas with 46 regions of interest. As a final step, a gender-based network analysis of the cerebellum was performed using the data-driven atlas along with the concept of the minimum spanning trees. The simulation analysis results confirm the dominance of the spatially constrained spectral clustering approach in discriminating activation patterns under noisy conditions. The network analysis results reveal statistically significant differences in the optimal tree organization between males and females. In addition, the dominance of the left VI lobule in both genders supports the results reported in a previous study of ours. To our knowledge, the extracted atlas comprises the first resting-state atlas of the cerebellum based on HCP data.

Original languageEnglish
Article number8456502
Pages (from-to)1710 - 1719
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
Issue number4
Early online date6 Sept 2018
Publication statusPublished - 1 Jul 2019

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  • Cerebellum
  • gender
  • minimum spanning trees
  • resting-state fMRI
  • spatially constrained spectral clustering


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