Topological properties of the structural brain network constructed using the -neighbor method.

IEEE Trans Biomed Eng. 2018 Jan 15;:

Authors: Lee MH, Kim DY, Chung MK, Alexander A, Davidson RJ

Abstract
OBJECTIVE: Structural characteristics of the brain can be analyzed based on structural brain networks constructed by diffusion tensor imaging (DTI). When a brain network is constructed by the existing parcellation method, the structure of the network changes depending on the scale of parcellation and arbitrary thresholding. To overcome these issues, we propose to construct brain networks using the improved μ-neighbor construction method, which is a parcellation free technique.
METHODS: We acquired DTI data from 14 control subjects and 15 subjects with autism. We examined the differences in topological properties of the brain networks constructed using the proposed method and existing parcellation between the two groups.
RESULTS: As the number of nodes increased, the connectedness of the network decreased in the parcellation method. However, for brain networks constructed using our proposed method, connectedness remained at a high level even with an increase in the number of nodes. We found significant differences in several topological properties of brain networks constructed using the proposed method, whereas topological properties were not significantly different for the parcellation method.
CONCLUSION: The brain networks constructed using the proposed method are considered as more realistic than a parcellation method with respect to the stability of connectedness. We found that subjects with autism showed the abnormal characteristics in the brain networks. These results demonstrate that the proposed method may provide new insights to analysis in the structural brain network.
SIGNIFICANCE: We proposed the novel brain network construction method to overcome the shortcoming in the existing parcellation method.

PMID: 29993531 [PubMed – as supplied by publisher]

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