Task fMRI data analysis based on supervised stochastic coordinate coding

• Published in: Medical image analysis Vol. 38; pp. 1 - 16
• Main Authors: Lv, Jinglei, Lin, Binbin, Li, Qingyang, Zhang, Wei, Zhao, Yu, Jiang, Xi, Guo, Lei, Han, Junwei, Hu, Xintao, Guo, Christine, Ye, Jieping, Liu, Tianming
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.05.2017; Elsevier BV
• Subjects: Algorithms; Brain; Brain - anatomy & histology; Brain - diagnostic imaging; Brain mapping; Brain Mapping - methods; Brain networks; Coding; Complexity; Connectome; Data analysis; Data processing; Functional magnetic resonance imaging; Humans; Information processing; Linear Models; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Motor Skills; Nervous system; Network analysis; Networks; Neural coding; Neuroimaging; NMR; Nuclear magnetic resonance; Pattern Recognition, Automated - methods; Regression analysis; Stochastic Processes; Supervised sparse coding; Task fMRI; Task fMRI; Brain networks; Supervised sparse coding
• ISSN: 1361-8415; 1361-8431; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2016.12.003

•Novel approach of sparse representation on the fMRI data.•Temporal features and spatial patterns could be supervised in dictionary learning on fMRI data.•Modeling brain networks with correspondence of prior knowledge makes group-wise analysis feasible.•Automatic learning makes it flexible to detect meaningful concurrent brain networks hidden in the data. Task functional magnetic resonance imaging (fMRI) has been widely employed for brain activation detection and brain network analysis. Modeling rich information from spatially-organized collection of fMRI time series is challenging because of the intrinsic complexity. Hypothesis-driven methods, such as the general linear model (GLM), which regress exterior stimulus from voxel-wise functional brain activity, are limited due to overlooking the complexity of brain activities and the diversity of concurrent brain networks. Recently, sparse representation and dictionary learning methods have attracted increasing interests in task fMRI data analysis. The major advantage of this methodology is its promise in reconstructing concurrent brain networks systematically. However, this data-driven strategy is, to some extent, arbitrary and does not sufficiently utilize the prior information of task design and neuroscience knowledge. To bridge this gap, we here propose a novel supervised sparse representation and dictionary learning framework based on stochastic coordinate coding (SCC) algorithm for task fMRI data analysis, in which certain brain networks are learned with known information such as pre-defined temporal patterns and spatial network patterns, and at the same time other networks are learned automatically from data. Our proposed method has been applied to two independent task fMRI datasets, and qualitative and quantitative evaluations have shown that our method provides a new and effective framework for task fMRI data analysis. [Display omitted] In this paper, we propose a novel supervised sparse representation and dictionary learning framework, named supervised stochastic coordinate coding (SCC), for task fMRI data analysis, in which certain brain networks are learned with known information such as pre-defined temporal features and spatial network patterns, and at the same time other concurrent networks are learned automatically from data. The proposed method takes advantages of both hypothesis-driven methodology and data-driven methodology for fMRI analysis.