The Japanese journal of neuropsychology

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ArticleTitle	Graph Theory
Language	J
AuthorList	Tetsuya Shimokawa
Affiliation	Center for Information and Neural Networks (CiNet), NICT and Osaka University
Publication	Japanese Journal of Neuropsychology: 34 (3), 200-208, 2018
Received
Accepted
Abstract	Graph is one of mathematics dealing with topology composed of nodes and links, and in recent years it has been drawing attention as a basic theory of network analysis also in the field of brain research. In networks such as friendship and the Internet, nodes and links are already defined. However, in the case of a brain network, we must start with the definition and estimation of nodes and links. In this paper, we first introduce the minimum graph theory necessary for brain network analysis. Furthermore, we describe problems specific to brain network analysis, such as definition and estimation of nodes and links. For example, in the case of functional connectivity, the link is estimated from the correlation coefficient of the time series data obtained from each of the two brain parts. If the correlation coefficient is higher than a certain threshold, the two nodes are connected. There are many ways to apply such thresholds, and the method chosen may have a significant impact on conclusions. In this paper, we introduce and explain these various methodologies related to thresholds.
Keywords	resting state, fMRI, graph theory, network analysis, brain science