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Sparse SPM: Group Sparse-dictionary learning in SPM framework for resting-state functional connectivity MRI analysis
 Lee et al, NeuroImage ,   2015 Oct 31. pii: S1053-8119(15)01012-5. doi: 10.1016/j.neuroimage.2015.10.081.

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-eect 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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Sparse SPM download site

    Release note:  version 1.0,  Dec. 14, 2015:    SSPM_manual.pdf

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ABOUT US
We are interested in novel bio-imaging and signal processing techniques that can greatly enhance our ability to monitor biological structures and functions. The mathematical tool of BISPL is a new signal processing theory called "compressed sensing". According to compressed sensing theory, perfect reconstruction is possible even from sampling rates dramatically smaller than the Nyquist sampling limit, as long as the non-zero spectral signal is sparse and the samples are obtained with an incoherent basis. From the bio imaging perspective, compressed sensing is a disruptive technology that challenges our current imaging principles. Therefore, our current research activities have been focused on demonstration of new proof-of-concept bioimaging tools for the aforementioned real bio-imaging applications .
CONTACT US
Bio Imaging & Signal Processing Lab. 
Dept. of Bio & Brain Engineering 
KAIST
291 Daehak-ro, Yuseong-gu
Daejeon 305-701, Korea

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