Identifying consistent brain networks via maximizing predictability of functional connectome from structural connectome

Recent studies have suggested that structural brain connectivity is strongly correlated with functional connectivity. However, the relationship between structural and functional connectivity at the whole brain connectome scale has been rarely explored. This paper presents a novel framework to infer...

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Vydáno v:2013 IEEE 10th International Symposium on Biomedical Imaging s. 978 - 981
Hlavní autoři: Hanbo Chen, Kaiming Li, Dajiang Zhu, Tianming Liu
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.04.2013
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ISBN:1467364568, 9781467364560
ISSN:1945-7928
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Shrnutí:Recent studies have suggested that structural brain connectivity is strongly correlated with functional connectivity. However, the relationship between structural and functional connectivity at the whole brain connectome scale has been rarely explored. This paper presents a novel framework to infer brain networks that are consistent across multiple neuroimaging modalities and across individuals at the connectome scale. Our basic premise is that the predictability of functional connectivity from structural connectivity within each brain network should be maximized, which is formulated by and solved via a novel feedback-regulated multi-view spectral clustering algorithm. We applied and tested the proposed algorithm on the multimodal structural and functional brain connectomes of 50 healthy subjects, and obtained promising results. Our validation experiments demonstrated that the derived brain networks are in agreement with current neuroscience knowledge and offer novel insights into the close relationship between brain structure and function at the connectome scale.
ISBN:1467364568
9781467364560
ISSN:1945-7928
DOI:10.1109/ISBI.2013.6556640