Paper Info
Title
Modeling Task FMRI Data via Supervised Stochastic Coordinate Coding.
Abstract
Task functional MRI fMRI has been widely employed to assess brain activation and networks. Modeling the rich information from the fMRI time series is challenging because of the lack of ground truth and the intrinsic complexity. Model-driven methods such as the general linear model GLM regresses exterior task designs from voxel-wise brain functional activity, which is confined because of ignoring the complexity and diversity of concurrent brain networks. Recently, dictionary learning and sparse coding method has attracted increasing attention in the fMRI analysis field. The major advantage of this methodology is its effectiveness in reconstructing concurrent brain networks automatically and systematically. However, the data-driven strategy is, to some extent, arbitrary due to ignoring the prior knowledge of task design and neuroscience knowledge. In this paper, we proposed a novel supervised stochastic coordinate coding SCC algorithm for fMRI data analysis, in which certain brain networks are learned with supervised information such as temporal patterns of task designs and spatial patterns of network templates, while other networks are learned automatically from the data. Its application on two independent fMRI datasets has shown the effectiveness of our methods.
Year
DOI
Venue
2015
10.1007/978-3-319-24553-9_30
MICCAI
Keywords
Field
DocType
Task fMRI,Supervised Stochastic Coordinate Coding,Brain network
Computer vision,Brain network,Dictionary learning,Pattern recognition,Computer science,Neural coding,General linear model,Brain activation,Coding (social sciences),Ground truth,Artificial intelligence,Machine learning
Conference
Volume
ISSN
Citations 
9349
0302-9743
2
PageRank 
References 
Authors
0.38
7
9
Name
Order
Citations
PageRank
Jinglei Lv120526.70
Lin, Binbin2666.41
Wei Zhang39710.58
Xi Jiang431137.88
Xintao Hu511813.53
Junwei Han63501194.57
Lei Guo71661142.63
Jieping Ye86943351.37
Tianming Liu91033112.95