title: Supervised dictionary learning for inferring concurrent brain networks.
abstract: Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model (GLM) has been a dominant approach to detect task-evoked networks. However, GLM focuses on task-evoked or event-evoked brain responses and possibly ignores the intrinsic brain functions. In comparison, dictionary learning and sparse coding methods have attracted much attention recently, and these methods have shown the promise of automatically and systematically decomposing fMRI signals into meaningful task-evoked and intrinsic concurrent networks. Nevertheless, two notable limitations of current data-driven dictionary learning method are that the prior knowledge of task paradigm is not sufficiently utilized and that the establishment of correspondences among dictionary atoms in different brains have been challenging. In this paper, we propose a novel supervised dictionary learning and sparse coding method for inferring functional networks from tfMRI data, which takes both of the advantages of model-driven method and data-driven method. The basic idea is to fix the task stimulus curves as predefined model-driven dictionary atoms and only optimize the other portion of data-driven dictionary atoms. Application of this novel methodology on the publicly available human connectome project (HCP) tfMRI datasets has achieved promising results.
authors.forename: Shijie
authors.initials: S
authors.lastname: Zhao
authors.forename: Junwei
authors.initials: J
authors.lastname: Han
authors.forename: Jinglei
authors.initials: J
authors.lastname: Lv
authors.forename: Xi
authors.initials: X
authors.lastname: Jiang
authors.forename: Xintao
authors.initials: X
authors.lastname: Hu
authors.forename: Yu
authors.initials: Y
authors.lastname: Zhao
authors.forename: Bao
authors.initials: B
authors.lastname: Ge
authors.forename: Lei
authors.initials: L
authors.lastname: Guo
authors.forename: Tianming
authors.initials: T
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D063132:Connectome; D006801:Humans; D016014:Linear Models; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009415:Nerve Net / Q000502:physiology*