title: Sparse representation of group-wise FMRI signals.
abstract: The human brain function involves complex processes with population codes of neuronal activities. Neuroscience research has demonstrated that when representing neuronal activities, sparsity is an important characterizing property. Inspired by this finding, significant amount of efforts from the scientific communities have been recently devoted to sparse representations of signals and patterns, and promising achievements have been made. However, sparse representation of fMRI signals, particularly at the population level of a group of different brains, has been rarely explored yet. In this paper, we present a novel group-wise sparse representation of task-based fMRI signals from multiple subjects via dictionary learning methods. Specifically, we extract and pool task-based fMRI signals for a set of cortical landmarks, each of which possesses intrinsic anatomical correspondence, from a group of subjects. Then an effective online dictionary learning algorithm is employed to learn an over-complete dictionary from the pooled population of fMRI signals based on optimally determined dictionary size. Our experiments have identified meaningful Atoms of Interests (AOI) in the learned dictionary, which correspond to consistent and meaningful functional responses of the brain to external stimulus. Our work demonstrated that sparse representation of group-wise fMRI signals is naturally suitable and effective in recovering population codes of neuronal signals conveyed in fMRI data.
authors: School of Automation, Northwestern Polytechnical University, Xi'an, China.
authors: Jinglei
authors: J
authors: Lv
authors: Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
authors: Xiang
authors: X
authors: Li
authors: Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
authors: Dajiang
authors: D
authors: Zhu
authors: Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
authors: Xi
authors: X
authors: Jiang
authors: School of Automation, Northwestern Polytechnical University, Xi'an, China.
authors: Xin
authors: X
authors: Zhang
authors: School of Automation, Northwestern Polytechnical University, Xi'an, China.
authors: Xintao
authors: X
authors: Hu
authors: School of Automation, Northwestern Polytechnical University, Xi'an, China.
authors: Tuo
authors: T
authors: Zhang
authors: School of Automation, Northwestern Polytechnical University, Xi'an, China.
authors: Lei
authors: L
authors: Guo
authors: Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
authors: Tianming
authors: T
authors: Liu
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008568:Memory / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity