TY - GEN
T1 - Temporal concatenated sparse coding of resting state fMRI data reveal network interaction changes in mTBI
AU - Lv, Jinglei
AU - Iraji, Armin
AU - Ge, Fangfei
AU - Zhao, Shijie
AU - Hu, Xintao
AU - Zhang, Tuo
AU - Han, Junwei
AU - Guo, Lei
AU - Kou, Zhifeng
AU - Liu, Tianming
N1 - Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - Resting state fMRI (rsfMRI) has been a useful imaging modality for network level understanding and diagnosis of brain diseases,such as mild traumatic brain injury (mTBI). However,there call for effective methodologies which can detect group-wise and longitudinal changes of network interactions in mTBI. The major challenges are two folds: (1) There lacks an individualized and common network system that can serve as a reference platform for statistical analysis; (2) Networks and their interactions are usually not modeled in the same algorithmic structure,which results in bias and uncertainty. In this paper,we propose a novel temporal concatenated sparse coding (TCSC) method to address these challenges. Based on the sparse graph theory the proposed method can model the commonly shared spatial maps of networks and the local dynamics of the networks in each subject in one algorithmic structure. Obviously,the local dynamics are not comparable across subjects in rsfMRI or across groups; however,based on the correspondence established by the common spatial profiles,the interactions of these networks can be modeled individually and statistically assessed in a group-wise fashion. The proposed method has been applied on an mTBI dataset with acute and sub-acute stages,and experimental results have revealed meaningful network interaction changes in mTBI.
AB - Resting state fMRI (rsfMRI) has been a useful imaging modality for network level understanding and diagnosis of brain diseases,such as mild traumatic brain injury (mTBI). However,there call for effective methodologies which can detect group-wise and longitudinal changes of network interactions in mTBI. The major challenges are two folds: (1) There lacks an individualized and common network system that can serve as a reference platform for statistical analysis; (2) Networks and their interactions are usually not modeled in the same algorithmic structure,which results in bias and uncertainty. In this paper,we propose a novel temporal concatenated sparse coding (TCSC) method to address these challenges. Based on the sparse graph theory the proposed method can model the commonly shared spatial maps of networks and the local dynamics of the networks in each subject in one algorithmic structure. Obviously,the local dynamics are not comparable across subjects in rsfMRI or across groups; however,based on the correspondence established by the common spatial profiles,the interactions of these networks can be modeled individually and statistically assessed in a group-wise fashion. The proposed method has been applied on an mTBI dataset with acute and sub-acute stages,and experimental results have revealed meaningful network interaction changes in mTBI.
UR - http://www.scopus.com/inward/record.url?scp=84996588099&partnerID=8YFLogxK
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U2 - 10.1007/978-3-319-46720-7_6
DO - 10.1007/978-3-319-46720-7_6
M3 - Conference contribution
AN - SCOPUS:84996588099
SN - 9783319467191
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 46
EP - 54
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
A2 - Ourselin, Sebastian
A2 - Joskowicz, Leo
A2 - Sabuncu, Mert R.
A2 - Wells, William
A2 - Unal, Gozde
PB - Springer Verlag
T2 - 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
Y2 - 21 October 2016 through 21 October 2016
ER -