Online dictionary learning for single-subject fMRI data unmixing. Bhanot, A., Meillier, C., Heitz, F., & Harsan, L. In 2019 27th European Signal Processing Conference (EUSIPCO), pages 1-5, Sep., 2019.
Online dictionary learning for single-subject fMRI data unmixing [pdf]Paper  doi  abstract   bibtex   
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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