TY - GEN
T1 - Consciousness level and recovery outcome prediction using high-order brain functional connectivity network
AU - Jia, Xiuyi
AU - Zhang, Han
AU - Adeli, Ehsan
AU - Shen, Dinggang
N1 - Funding Information:
Acknowledgements. This work is partially supported by National Natural Science Foundation of China (Grant Nos. 61403200), Natural Science Foundation of Jiangsu Province (Grant No. BK20140800), and NIH grants (EB006733, EB008374, EB009634, MH107815, AG041721, and AG042599).
PY - 2017
Y1 - 2017
N2 - Based on the neuroimaging data from a large set of acquired brain injury patients, we investigate the feasibility of using machine learning for automatic prediction of individual consciousness level. Rather than using the traditional Pearson’s correlation-based brain functional network, which measures only the simple temporal synchronization of the BOLD signals from each pair of brain regions, we construct a high-order brain functional network that is capable of characterizing topographical information-based high-level functional associations among brain regions. In such a high-order brain network, each node represents the community of a brain region, described by a set of this region’s low-order functional associations with other brain regions, and each edge characterizes topographical similarity between a pair of such communities. Experimental results show that the high-order brain functional network enables a significant better classification for consciousness level and recovery outcome prediction.
AB - Based on the neuroimaging data from a large set of acquired brain injury patients, we investigate the feasibility of using machine learning for automatic prediction of individual consciousness level. Rather than using the traditional Pearson’s correlation-based brain functional network, which measures only the simple temporal synchronization of the BOLD signals from each pair of brain regions, we construct a high-order brain functional network that is capable of characterizing topographical information-based high-level functional associations among brain regions. In such a high-order brain network, each node represents the community of a brain region, described by a set of this region’s low-order functional associations with other brain regions, and each edge characterizes topographical similarity between a pair of such communities. Experimental results show that the high-order brain functional network enables a significant better classification for consciousness level and recovery outcome prediction.
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U2 - 10.1007/978-3-319-67159-8_3
DO - 10.1007/978-3-319-67159-8_3
M3 - Conference contribution
AN - SCOPUS:85029407400
SN - 9783319671581
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 17
EP - 24
BT - Connectomics in NeuroImaging - 1st International Workshop, CNI 2017 Held in Conjunction with MICCAI 2017, Proceedings
A2 - Bonilha, Leonardo
A2 - Wu, Guorong
A2 - Laurienti, Paul
A2 - Munsell, Brent C.
PB - Springer Verlag
T2 - 1st International Workshop on Connectomics in NeuroImaging, CNI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
Y2 - 14 September 2017 through 14 September 2017
ER -