Paper Info
Title
Temporal Concatenated Sparse Coding of Resting State fMRI Data Reveal Network Interaction Changes in mTBI.
Abstract
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.
Year
Venue
Field
2016
MICCAI
Network level,Pattern recognition,Computer science,Neural coding,Resting state fMRI,Speech recognition,Concatenation,Artificial intelligence,Statistical analysis,Dense graph
DocType
Citations 
PageRank 
Conference
1
0.35
References 
Authors
3
10
Name
Order
Citations
PageRank
Jinglei Lv120526.70
Armin Iraji2113.69
Fangfei Ge344.93
Shijie Zhao46610.85
Xintao Hu511813.53
Tuo Zhang623332.92
Junwei Han73501194.57
Lei Guo818111.67
Zhifeng Kou9102.29
Tianming Liu101033112.95