TY - GEN
T1 - Detection of differentially developed functional connectivity patterns in adolescents based on tensor discriminative analysis
AU - Fang, Jian
AU - Stephen, Julia
AU - Wilson, Tony
AU - Calhoun, Vince D.
AU - Wang, Yu Ping
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/23
Y1 - 2018/5/23
N2 - Adolescence is a crucial time for the development of functional connectivity, and the tuning process changes under different physical and cognitive status. However, there is still a lack of knowledge on where and how functional development changes across this age range. In this paper, we introduce a discriminative tensor decomposition method to detect differentially developed functional connectivity patterns. The method is a combination of the Fisher's discriminative analysis and CANDECOMP/PARAFAC(CP) decomposition, linked by a novel discriminative developmental ratio. A sequential orthogonal decomposition algorithm is then proposed to efficiently solve the problem. The method is validated by a real dataset, which contains functional connectivity maps for each individual. We separate the subjects by sex and use a sliding window approach over age to obtain two correlation tensors. Applying our method to the two tensors, we can detect connectivity patterns with differential development in sex. Our results show evidence for developmental differences between girls and boys that are largest during puberty.
AB - Adolescence is a crucial time for the development of functional connectivity, and the tuning process changes under different physical and cognitive status. However, there is still a lack of knowledge on where and how functional development changes across this age range. In this paper, we introduce a discriminative tensor decomposition method to detect differentially developed functional connectivity patterns. The method is a combination of the Fisher's discriminative analysis and CANDECOMP/PARAFAC(CP) decomposition, linked by a novel discriminative developmental ratio. A sequential orthogonal decomposition algorithm is then proposed to efficiently solve the problem. The method is validated by a real dataset, which contains functional connectivity maps for each individual. We separate the subjects by sex and use a sliding window approach over age to obtain two correlation tensors. Applying our method to the two tensors, we can detect connectivity patterns with differential development in sex. Our results show evidence for developmental differences between girls and boys that are largest during puberty.
KW - Adolescent
KW - Brain development
KW - Functional connectivity
KW - Tensor decomposition
UR - http://www.scopus.com/inward/record.url?scp=85048128868&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048128868&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2018.8363512
DO - 10.1109/ISBI.2018.8363512
M3 - Conference contribution
AN - SCOPUS:85048128868
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 10
EP - 14
BT - 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PB - IEEE Computer Society
T2 - 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Y2 - 4 April 2018 through 7 April 2018
ER -