Jong just sent me the following:
Hi Igor,I would like to bring your attention to our recent paper showing that a sparse dictionary learning can address a long-standing problem in brain connectivity analysis.Young-Beom Lee, Jeonghyeon Lee, Sungho Tak, Kangjoo Lee, Duk L. Na, Sangwon Seo, Yong Jeong, and Jong Chul Ye, "Sparse SPM: Sparse-Dictionary Learning for Resting-state Functional Connectivity MRI Analysis", NeuroImage (in press), 2015.In this paper, we developed a unified mixed-model called sparse SPM for group sparse dictionary learning and inference for resting-state fMRI analysis.Unlike ICA methods, the new algorithm exploits the fact that temporal dynamics at each voxel can be represented as a sparse combination of global dynamics because of the property of small-worldness of brain networks. Under the reasonable assumption that the individual network structures in the same group are similar, we demonstrated that a group sparse dictionary learning algorithm can extract the subnetwork structures as group biomarkers, and that mixed-effect analysis with ReML covariance estimation can provide a unified individual- and group- level statistical analysis and inference. This unified mixed effect framework also enabled ANOVA at a group level, which provided systematic tools to investigates the disease progression. We compared and validated our tools with the existing seed-based and ICA approaches for normal, MCI and Alzheimer's disease patients. The results indicate that the DMN network extracted with our method shows a clear correlation with the progression of disease.Cheers,-Jong
Thank you Jong for the context, here is the paper:
Sparse SPM: Sparse-Dictionary Learning for Resting-state Functional Connectivity MRI Analysis by Young-Beom Lee, Jeonghyeon Lee, Sungho Tak, Kangjoo Lee, Duk L. Na, Sangwon Seo, Yong Jeong, Jong Chul Ye , and The Alzheimer's Disease Neuroimaging Initiative
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-e ect 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.
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