Abstract
Community detection methods have been widely used for studying the modular structure of the brain. However, few of these methods exploit the intrinsic properties of brain networks other than modularity to tackle the pronounced noise in neuroimaging data. We propose a random walker (RW) based approach that reflects how regions of a brain subnetwork tend to be inter-linked by a provincial hub. By using provincial hubs to guide seed setting, RW provides the exact posterior probability of a brain region belonging to each given subnetwork, which mitigates forced hard assignments of brain regions to subnetworks as is the case in most existing methods. We further present an extension that enables multimodal integration for exploiting complementary information from functional Magnetic Resonance Imaging (fMRI) and diffusion MRI (dMRI) data. On synthetic data, our approach achieves higher accuracy in subnetwork extraction than unimodal and existing multimodal approaches. On real data from the Human Connectome Project (HCP), our estimated subnetworks match well with established brain systems and attain higher inter-subject reproducibility.
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Wang, C., Ng, B., Abugharbieh, R. (2017). Multimodal Brain Subnetwork Extraction Using Provincial Hub Guided Random Walks. In: Niethammer, M., et al. Information Processing in Medical Imaging. IPMI 2017. Lecture Notes in Computer Science(), vol 10265. Springer, Cham. https://doi.org/10.1007/978-3-319-59050-9_23
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