TY - GEN
T1 - Discovering network-level functional interactions from working memory fMRI data
AU - Jiang, Xi
AU - Lv, Jinglei
AU - Zhu, Dajiang
AU - Zhang, Tuo
AU - Li, Xiang
AU - Hu, Xintao
AU - Guo, Lei
AU - Liu, Tianming
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/7/29
Y1 - 2014/7/29
N2 - It is widely believed that working memory process involves large-scale functional interactions among multiple brain networks. However, network-level functional interactions across large-scale brain networks in working memory have been rarely explored yet in the literature. In this paper, we propose a novel framework for modeling network-level functional interactions in working memory based on our publicly released 358 DICCCOL landmarks. First, 14 DICCCOLs are detected as group-wise activated ROIs via GLM and compose the 'basic network' of working memory. Second, the time-frequency functional interaction patterns of each pair of activated DICCCOL and other DICCCOLs are calculated using cross-wavelet transform. Third, the common functional interaction patterns and corresponding brain networks are learned via effective online dictionary learning and sparse coding methods. Experimental results showed that multiple brain networks are involved in working memory processes. More importantly, each brain network interacts with the 'basic network' via a specific functionally meaningful time-frequency interaction pattern.
AB - It is widely believed that working memory process involves large-scale functional interactions among multiple brain networks. However, network-level functional interactions across large-scale brain networks in working memory have been rarely explored yet in the literature. In this paper, we propose a novel framework for modeling network-level functional interactions in working memory based on our publicly released 358 DICCCOL landmarks. First, 14 DICCCOLs are detected as group-wise activated ROIs via GLM and compose the 'basic network' of working memory. Second, the time-frequency functional interaction patterns of each pair of activated DICCCOL and other DICCCOLs are calculated using cross-wavelet transform. Third, the common functional interaction patterns and corresponding brain networks are learned via effective online dictionary learning and sparse coding methods. Experimental results showed that multiple brain networks are involved in working memory processes. More importantly, each brain network interacts with the 'basic network' via a specific functionally meaningful time-frequency interaction pattern.
KW - Functional interaction
KW - Network-level
KW - Task fMRI
KW - Working memory
UR - http://www.scopus.com/inward/record.url?scp=84927919099&partnerID=8YFLogxK
U2 - 10.1109/isbi.2014.6867797
DO - 10.1109/isbi.2014.6867797
M3 - 会议稿件
AN - SCOPUS:84927919099
T3 - 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
SP - 13
EP - 16
BT - 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
Y2 - 29 April 2014 through 2 May 2014
ER -