title: Sparse SPM: Group Sparse-dictionary learning in SPM framework for resting-state functional connectivity MRI analysis.
abstract: Recent studies of functional connectivity MR imaging have revealed that the default-mode network activity is disrupted in diseases such as Alzheimer's disease (AD). However, there is not yet a consensus on the preferred method for resting-state analysis. Because the brain is reported to have complex interconnected networks according to graph theoretical analysis, the independency assumption, as in the popular independent component analysis (ICA) approach, often does not hold. Here, rather than using the independency assumption, we present a new statistical parameter mapping (SPM)-type analysis method based on a sparse graph model where temporal dynamics at each voxel position are described as a sparse combination of global brain dynamics. In particular, a new concept of a spatially adaptive design matrix has been proposed to represent local connectivity that shares the same temporal dynamics. If we further assume that local network structures within a group are similar, the estimation problem of global and local dynamics can be solved using sparse dictionary learning for the concatenated temporal data across subjects. Moreover, under the homoscedasticity variance assumption across subjects and groups that is often used in SPM analysis, the aforementioned individual and group analyses using sparse dictionary learning can be accurately modeled by a mixed-effect model, which also facilitates a standard SPM-type group-level inference using summary statistics. Using an extensive resting fMRI data set obtained from normal, mild cognitive impairment (MCI), and Alzheimer's disease patient groups, we demonstrated that the changes in the default mode network extracted by the proposed method are more closely correlated with the progression of Alzheimer's disease.
authors.affiliation: Laboratory for Cognitive Neuroscience and NeuroImaging, Dept. of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 373-1 Guseong-dong Yuseong-gu, Daejeon 305-701, South Korea.
authors.forename: Young-Beom
authors.initials: YB
authors.lastname: Lee
authors.affiliation: Bio Imaging and Signal Processing Lab., Dept. of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 373-1 Guseong-dong Yuseong-gu, Daejeon 305-701, South Korea; Currently at Korea Hydro and Nuclear Power Co., Ltd, Gyeongju, South Korea.
authors.forename: Jeonghyeon
authors.initials: J
authors.lastname: Lee
authors.affiliation: Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London WC1N 3BG, UK.
authors.forename: Sungho
authors.initials: S
authors.lastname: Tak
authors.affiliation: Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada.
authors.forename: Kangjoo
authors.initials: K
authors.lastname: Lee
authors.affiliation: Department of Neurology, and Neuroscience Center Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
authors.forename: Duk L
authors.initials: DL
authors.lastname: Na
authors.affiliation: Department of Neurology, and Neuroscience Center Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
authors.forename: Sang Won
authors.initials: SW
authors.lastname: Seo
authors.affiliation: Laboratory for Cognitive Neuroscience and NeuroImaging, Dept. of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 373-1 Guseong-dong Yuseong-gu, Daejeon 305-701, South Korea. Electronic address: yong@kaist.ac.kr.
authors.forename: Yong
authors.initials: Y
authors.lastname: Jeong
authors.affiliation: Bio Imaging and Signal Processing Lab., Dept. of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 373-1 Guseong-dong Yuseong-gu, Daejeon 305-701, South Korea. Electronic address: jong.ye@kaist.ac.kr.
authors.forename: Jong Chul
authors.initials: JC
authors.lastname: Ye
mesh_terms: D000368:Aged; D000465:Algorithms; D000544:Alzheimer Disease / Q000503:physiopathology; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D060825:Cognitive Dysfunction / Q000503:physiopathology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008962:Models, Theoretical; D009434:Neural Pathways / Q000502:physiology*; D012146:Rest
keywords: Alzheimer's disease; Functional connectivity; K-SVD; Resting-state fMRI analysis; Sparse dictionary learning; Sparse graph