@InProceedings{8902991,
  author = {A. Bhanot and C. Meillier and F. Heitz and L. Harsan},
  booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
  title = {Online dictionary learning for single-subject fMRI data unmixing},
  year = {2019},
  pages = {1-5},
  abstract = {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.},
  keywords = {biomedical MRI;brain;image segmentation;independent component analysis;medical image processing;neurophysiology;functional magnetic resonance imaging;multisubject data;related temporal signatures;single-subject rs-fMRI dataset;noisy maps;blurry spatial maps;high-resolution spatial information;HR spatial segmentation map;low-resolution rs-fMRI;spatial abundance maps;online dictionary learning;single-subject fMRI data unmixing;independent component analysis;Functional magnetic resonance imaging;Spatial resolution;Signal processing algorithms;Estimation;Mice;Sparse matrices;Dictionary Learning;resting state fMRI;single-subject rs-fMRI unmixing;high-resolution anatomical atlas},
  doi = {10.23919/EUSIPCO.2019.8902991},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570532678.pdf},
}

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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. 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Bhanot and C. Meillier and F. Heitz and L. Harsan},\n booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},\n title = {Online dictionary learning for single-subject fMRI data unmixing},\n year = {2019},\n pages = {1-5},\n abstract = {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. 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