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Front Neurosci. 2018 Dec 12;12:942. doi: 10.3389/fnins.2018.00942. eCollection 2018.

Multi-Granularity Whole-Brain Segmentation Based Functional Network Analysis Using Resting-State fMRI.

Gong Y1, Wu H1,2, Li J1,3, Wang N2, Liu H4,5, Tang X1.

Author information

1
Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China.
2
School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
3
Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, United States.
4
Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
5
Guangdong Provincial Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.

Abstract

In this work, we systematically analyzed the effects of various nodal definitions, as determined by a multi-granularity whole-brain segmentation scheme, upon the topological architecture of the human brain functional network using the resting-state functional magnetic resonance imaging data of 19 healthy, young subjects. A number of functional networks were created with their nodes defined according to two types of anatomical definitions (Type I and Type II) each of which consists of five granularity levels of whole brain segmentations with each level linked through ontology-based, hierarchical, structural relationships. Topological properties were computed for each network and then compared across levels within the same segmentation type as well as between Type I and Type II. Certain network architecture patterns were observed in our study: (1) As the granularity changes, the absolute values of each node's nodal degree and nodal betweenness change accordingly but the relative values within a single network do not change considerably; (2) The average nodal degree is generally affected by the sparsity level of the network whereas the other topological properties are more specifically affected by the nodal definitions; (3) Within the same ontology relationship type, as the granularity decreases, the network becomes more efficient at information propagation; (4) The small-worldness that we observe is an intrinsic property of the brain's resting-state functional network, independent of the ontology type and the granularity level. Furthermore, we validated the aforementioned conclusions and measured the reproducibility of this multi-granularity network analysis pipeline using another dataset of 49 healthy young subjects that had been scanned twice.

KEYWORDS:

brain network; fMRI; multi-atlas segmentation; multi-granularity; ontology relationship; resting-state; small-worldness

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