Functional connectivity analysis of cerebellum using spatially constrained spectral clustering

Abstract

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.

Publication DOI: https://doi.org/10.1109/JBHI.2018.2868918
Divisions: Life & Health Sciences
Additional Information: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: Cerebellum,gender,minimum spanning trees,resting-state fMRI,spatially constrained spectral clustering,Biotechnology,Computer Science Applications,Electrical and Electronic Engineering,Health Information Management
Full Text Link:
Related URLs: https://ieeexpl ... cument/8456502/ (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2019-07-01
Published Online Date: 2018-09-06
Accepted Date: 2018-09-01
Authors: Pezoulas, Vasileios C.
Michalopoulos, Kostas
Klados, Manousos ( 0000-0002-1629-6446)
Micheloyannis, Sifis
Bourbakis, Nikolaos
Zervakis, Michalis

Download

[img]

Version: Accepted Version

| Preview

Export / Share Citation


Statistics

Additional statistics for this record