Abstract
Using sophisticated graph-theoretical analyses, modern magnetic resonance imaging techniques have allowed us to model the human brain as a brain connectivity network or a graph. In a brain network, the nodes of the network correspond to a set of brain regions and the link or edges correspond to the functional or structural connectivity between these regions. The linkage structure in brain networks can encode valuable information about the organizational properties of the human brain as a whole. However, the complexity of such linkage information raises major challenges in the era of big data in brain informatics. Conventional approaches on brain networks primarily focus on local patterns within select brain regions or pairwise connectivity between regions. By contrast, in this study, we proposed a graph mining framework based on state-of-the-art data mining techniques. Using a statistical test based on the G-test, we validated this framework in a sample of euthymic bipolar I subjects, and identified abnormal subgraph patterns in the rsfMRI networks of these subjects relative to healthy controls.
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Cao, B. et al. (2015). Identification of Discriminative Subgraph Patterns in fMRI Brain Networks in Bipolar Affective Disorder. In: Guo, Y., Friston, K., Aldo, F., Hill, S., Peng, H. (eds) Brain Informatics and Health. BIH 2015. Lecture Notes in Computer Science(), vol 9250. Springer, Cham. https://doi.org/10.1007/978-3-319-23344-4_11
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DOI: https://doi.org/10.1007/978-3-319-23344-4_11
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