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Information Assisted Dictionary Learning for fMRI Data Analysis
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DOI：10.1109/ACCESS.2020.2994276.png)
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Abstract
En 中文
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 & x2019; 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.
Keywords:
Functional magnetic resonance imaging
Task analysis
Machine learning
Brain modeling
Data analysis
Hemodynamics
Standards
Dictionary learning
fMRI
semi-blind
sparsity
weighted norms
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