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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.

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


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.


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

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