TY - GEN
T1 - Development of a computer-aided system for an effective brain connectivity network
AU - Nie, Yaoxin
AU - Zhu, Linlin
AU - Su, Yipeng
AU - Li, Xudong
AU - Niu, Zhendong
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/1/17
Y1 - 2017/1/17
N2 - Currently, dynamic causal modeling (DCM) is one of the most widely used models for an effective brain connectivity network, but it also has some disadvantages (e.g., researchers' selection of cerebral regions of interest [ROIs] is subjective, a substantial time is required for computation, etc.). Statistical Parametric Mapping (SPM) is the most popular statistical data analysis software for brain function, but its settings cumbersome, especially the data preprocessing section. In response to these disadvantages of DCM and SPM, we designed and created a computer-aided system for an effective brain connectivity network, modularized the data preprocessing section of SPM, and we explored the cerebral ROIs and possible co-activation network based on our proposed approach. The co-activation network has as a prior interconnection relationship, and it is used to assist in the selection of ROIs in similar cognitive experiments; thus, the testing of meaningless noise connection modes by the DCM is prevented, the number of models DMC is decreased, and the accuracy of the conclusions and computational efficiency of the DCM are improved.
AB - Currently, dynamic causal modeling (DCM) is one of the most widely used models for an effective brain connectivity network, but it also has some disadvantages (e.g., researchers' selection of cerebral regions of interest [ROIs] is subjective, a substantial time is required for computation, etc.). Statistical Parametric Mapping (SPM) is the most popular statistical data analysis software for brain function, but its settings cumbersome, especially the data preprocessing section. In response to these disadvantages of DCM and SPM, we designed and created a computer-aided system for an effective brain connectivity network, modularized the data preprocessing section of SPM, and we explored the cerebral ROIs and possible co-activation network based on our proposed approach. The co-activation network has as a prior interconnection relationship, and it is used to assist in the selection of ROIs in similar cognitive experiments; thus, the testing of meaningless noise connection modes by the DCM is prevented, the number of models DMC is decreased, and the accuracy of the conclusions and computational efficiency of the DCM are improved.
KW - Co-activation relationship
KW - Dynamic causal modeling
KW - FMRI
KW - Neuroinformatics
UR - https://www.scopus.com/pages/publications/85013335531
U2 - 10.1109/BIBM.2016.7822774
DO - 10.1109/BIBM.2016.7822774
M3 - Conference contribution
AN - SCOPUS:85013335531
T3 - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
SP - 1703
EP - 1705
BT - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
A2 - Burrage, Kevin
A2 - Zhu, Qian
A2 - Liu, Yunlong
A2 - Tian, Tianhai
A2 - Wang, Yadong
A2 - Hu, Xiaohua Tony
A2 - Jiang, Qinghua
A2 - Song, Jiangning
A2 - Morishita, Shinichi
A2 - Burrage, Kevin
A2 - Wang, Guohua
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Y2 - 15 December 2016 through 18 December 2016
ER -