Information Assisted Dictionary Learning for fMRI Data Analysis
In this paper, the task-related fMRI problem is treated in its matrix factorization form, focusing on the Dictionary Learning (DL) approach. The proposed method allows the incorporation of a priori knowledge that is associated with both the experimental design and available brain atlases. Moreover,...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9091875/ |
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| author | Manuel Morante Yannis Kopsinis Sergios Theodoridis Athanassios Protopapas |
| author_facet | Manuel Morante Yannis Kopsinis Sergios Theodoridis Athanassios Protopapas |
| author_sort | Manuel Morante |
| collection | DOAJ |
| description | In this paper, the task-related fMRI problem is treated in its matrix factorization form, focusing on the Dictionary Learning (DL) approach. The proposed method allows the incorporation of a priori knowledge that is associated with both the experimental design and available brain atlases. Moreover, it can cope efficiently with uncertainties in the modeling of the hemodynamic response function. In addition, the method bypasses one of the major drawbacks of the DL methods; namely, the selection of the sparsity-related regularization parameters. Under the proposed formulation, the associated regularization parameters bear a direct relation to the number of the activated voxels for each one of the sources’ spatial maps. This natural interpretation facilitates fine-tuning of the related parameters and allows for exploiting external information from brain atlases. The proposed method is evaluated against several other popular techniques, including the classical General Linear Model (GLM). The obtained performance gains are quantitatively demonstrated via a novel realistic synthetic fMRI dataset as well as real data from a challenging experimental design. |
| format | Article |
| id | doaj-art-4b232556660f4465a5244c343bfe7835 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4b232556660f4465a5244c343bfe78352025-01-16T00:01:04ZengIEEEIEEE Access2169-35362020-01-018900529006810.1109/ACCESS.2020.29942769091875Information Assisted Dictionary Learning for fMRI Data AnalysisManuel Morante0https://orcid.org/0000-0001-8935-0186Yannis Kopsinis1Sergios Theodoridis2Athanassios Protopapas3Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, GreeceLibra MLI Ltd., Edinburgh, U.KDepartment of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, GreeceDepartment of Special Needs Education, University of Oslo, Oslo, NorwayIn this paper, the task-related fMRI problem is treated in its matrix factorization form, focusing on the Dictionary Learning (DL) approach. The proposed method allows the incorporation of a priori knowledge that is associated with both the experimental design and available brain atlases. Moreover, it can cope efficiently with uncertainties in the modeling of the hemodynamic response function. In addition, the method bypasses one of the major drawbacks of the DL methods; namely, the selection of the sparsity-related regularization parameters. Under the proposed formulation, the associated regularization parameters bear a direct relation to the number of the activated voxels for each one of the sources’ spatial maps. This natural interpretation facilitates fine-tuning of the related parameters and allows for exploiting external information from brain atlases. The proposed method is evaluated against several other popular techniques, including the classical General Linear Model (GLM). The obtained performance gains are quantitatively demonstrated via a novel realistic synthetic fMRI dataset as well as real data from a challenging experimental design.https://ieeexplore.ieee.org/document/9091875/Dictionary learningfMRIsemi-blindsparsityweighted norms |
| spellingShingle | Manuel Morante Yannis Kopsinis Sergios Theodoridis Athanassios Protopapas Information Assisted Dictionary Learning for fMRI Data Analysis IEEE Access Dictionary learning fMRI semi-blind sparsity weighted norms |
| title | Information Assisted Dictionary Learning for fMRI Data Analysis |
| title_full | Information Assisted Dictionary Learning for fMRI Data Analysis |
| title_fullStr | Information Assisted Dictionary Learning for fMRI Data Analysis |
| title_full_unstemmed | Information Assisted Dictionary Learning for fMRI Data Analysis |
| title_short | Information Assisted Dictionary Learning for fMRI Data Analysis |
| title_sort | information assisted dictionary learning for fmri data analysis |
| topic | Dictionary learning fMRI semi-blind sparsity weighted norms |
| url | https://ieeexplore.ieee.org/document/9091875/ |
| work_keys_str_mv | AT manuelmorante informationassisteddictionarylearningforfmridataanalysis AT yanniskopsinis informationassisteddictionarylearningforfmridataanalysis AT sergiostheodoridis informationassisteddictionarylearningforfmridataanalysis AT athanassiosprotopapas informationassisteddictionarylearningforfmridataanalysis |