title: Sparse representation of higher-order functional interaction patterns in task-based FMRI data.
abstract: Traditional task-based fMRI activation detection methods, e.g., the widely used general linear model (GLM), assume that the brain's hemodynamic responses follow the block-based or event-related stimulus paradigm. Typically, these activation detections are performed voxel-wise independently, and then are usually followed by statistical corrections. Despite remarkable successes and wide adoption of these methods, it remains largely unknown how functional brain regions interact with each other within specific networks during task performance blocks and in the baseline. In this paper, we present a novel algorithmic pipeline to statistically infer and sparsely represent higher-order functional interaction patterns within the working memory network during task performance and in the baseline. Specifically, a collection of higher-order interactions are inferred via the greedy equivalence search (GES) algorithm for both task and baseline blocks. In the next stage, an effective online dictionary learning algorithm is utilized for sparse representation of the inferred higher-order interaction patterns. Application of this framework on a working memory task-based fMRI data reveals interesting and meaningful distributions of the learned sparse dictionary atoms in task and baseline blocks. In comparison with traditional voxel-wise activation detection and recent pair-wise functional connectivity analysis, our framework offers a new methodology for representation and exploration of higher-order functional activities in the brain.
authors: Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
authors: Shu
authors: S
authors: Zhang
authors: Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
authors: Xiang
authors: X
authors: Li
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: Xi
authors: X
authors: Jiang
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: Hanbo
authors: H
authors: Chen
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*; D008570:Memory, Short-Term / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013647:Task Performance and Analysis