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IEEE J Biomed Health Inform. 2018 Sep 6. doi: 10.1109/JBHI.2018.2868918. [Epub ahead of print]

Functional connectivity analysis of cerebellum using spatially constrained spectral clustering.

Abstract

The human cerebellum contains almost fifty percent of the neurons in the brain, although its volume does not exceed ten percent of the total brain volume. The goal of this study is to derive the functional network of the cerebellum during resting-state and then compare the ensuing group networks between males and females. Towards 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 cerebellum resulting to a functional atlas with 46 Regions of Interest (ROIs). As a final step, a gender-based network analysis of 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 cerebellum based on HCP data.

PMID:
30188842
DOI:
10.1109/JBHI.2018.2868918

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