Online Dictionary Learning For Single-Subject Fmri Data Unmixing
2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO)(2019)
摘要
Independent component analysis (ICA) and dictionary learning (DL) methods are widely used to analyse resting state functional Magnetic Resonance Imaging (rs-fMRI) in multi-subject studies. These methods aim at decomposing the multi-subject data into common spatial abundance maps and their related temporal signatures.We are interested here in such a decomposition for a single-subject rs-fMRI dataset. The above-mentioned methods often fail in this case because the problem becomes too ill-posed, requiring the use of additional prior information and the design of novel regularising constraints. The poor resolution of rs-fMRI data is an additional source of difficulty, yielding noisy and blurry spatial maps.In this paper, we propose a new DL formulation adapted to the unique subject by integrating high-resolution (HR) spatial information to constrain single-subject data unmixing. HR information is provided by the registration of an anatomical atlas on the data set. We show on a quasi-real dataset from mice, the benefit of using an HR spatial segmentation map in the decomposition of low-resolution rs-fMRI.
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关键词
Dictionary Learning, resting state fMRI, single-subject rs-fMRI unmixing, high-resolution anatomical atlas
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