TY - GEN
T1 - Novel effective connectivity network inference for MCI identification
AU - Li, Yang
AU - Yang, Hao
AU - Li, Ke
AU - Yap, Pew Thian
AU - Kim, Minjeong
AU - Wee, Chong Yaw
AU - Shen, Dinggang
N1 - Publisher Copyright:
© 2017, Springer International Publishing AG.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017
Y1 - 2017
N2 - Inferring effective brain connectivity network is a challenging task owing to perplexing noise effects, the curse of dimensionality, and inter-subject variability. However, most existing network inference methods are based on correlation analysis and consider the datum points individually, revealing limited information of the neuron interactions and ignoring the relations amongst the derivatives of the data. Hence, we proposed a novel ultra group-constrained sparse linear regression model for effective connectivity inference. This model utilizes not only the discrepancy between observed signals and the model prediction, but also the discrepancy between the associated weak derivatives of the observed and the model signals for a more accurate effective connectivity inference. What’s more, a group constraint is applied to minimize the inter-subject variability and the proposed modeling was validated on a mild cognitive impairment dataset with superior results achieved.
AB - Inferring effective brain connectivity network is a challenging task owing to perplexing noise effects, the curse of dimensionality, and inter-subject variability. However, most existing network inference methods are based on correlation analysis and consider the datum points individually, revealing limited information of the neuron interactions and ignoring the relations amongst the derivatives of the data. Hence, we proposed a novel ultra group-constrained sparse linear regression model for effective connectivity inference. This model utilizes not only the discrepancy between observed signals and the model prediction, but also the discrepancy between the associated weak derivatives of the observed and the model signals for a more accurate effective connectivity inference. What’s more, a group constraint is applied to minimize the inter-subject variability and the proposed modeling was validated on a mild cognitive impairment dataset with superior results achieved.
UR - http://www.scopus.com/inward/record.url?scp=85029720764&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85029720764&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-67389-9_37
DO - 10.1007/978-3-319-67389-9_37
M3 - Conference contribution
AN - SCOPUS:85029720764
SN - 9783319673882
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 316
EP - 324
BT - Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings
A2 - Shi, Yinghuan
A2 - Suk, Heung-Il
A2 - Suzuki, Kenji
A2 - Wang, Qian
PB - Springer Verlag
T2 - 8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
Y2 - 10 September 2017 through 10 September 2017
ER -