Abstract
Schizophrenia is a complex mental illness, the mechanism of which is currently unclear. Using sparse representation and dictionary learning (SDL) model to analyze functional magnetic resonance imaging (fMRI) dataset of schizophrenia is currently a popular method for exploring the mechanism of the disease. The SDL method decomposed the fMRI data into a sparse coding matrix X and a dictionary matrix D. However, these traditional methods overlooked group structure information in X and the coherence between the atoms in D. To address this problem, we propose a new SDL model incorporating group sparsity and incoherence, namely GS2ISDL to detect abnormal brain regions. Specifically, GS2ISDL uses the group structure information that defined by AAL anatomical template from fMRI dataset as priori to achieve inter-group sparsity in X. At the same time, L 1 - norm is enforced on X to achieve intra-group sparsity. In addition, our algorithm also imposes incoherent constraint on the dictionary matrix D to reduce the coherence between the atoms in D, which can ensure the uniqueness of X and the discriminability of the atoms. To validate our proposed model GS2ISDL, we compared it with both IK-SVD and SDL algorithm for analyzing fMRI dataset collected by Mind Clinical Imaging Consortium (MCIC). The results show that the accuracy, sensitivity, recall and MCC values of GS2ISDL are 93.75%, 95.23%, 80.50% and 88.19%, respectively, which outperforms both IK-SVD and SDL. The ROIs extracted by GS2ISDL model (such as Precentral gyrus, Hippocampus and Caudate nucleus, etc.) are further verified by the literature review on schizophrenia studies, which have significant biological significance.
Highlights
The associate editor coordinating the review of this manuscript and approving it for publication was Mohamad Forouzanfar
Compared with the results obtained by IK-SVD algorithm, the accuracy, sensitivity, recall and Mathews correlation coefficient (MCC) values obtained by GS2ISDL algorithm are improved by 5.5%, 9.51%, 5.28%, and 9.06%, respectively
Compared with the results obtained by sparse representation and dictionary learning (SDL) algorithm, the accuracy, sensitivity, recall and MCC value obtained by GS2ISDL algorithm are improved by 6.24%, 14.52%, 7.65%, and 10.73%, respectively
Summary
The SDL has been shown to be efficient in learning adaptive, over-complete and diverse features, which decomposes the observed signals into sparse bases representations [12] This method outforms the traditional methods including principal component analysis (PCA) and ICA in the extraction of activity patterns [12], [15]. Jiang et al [25] applied SDL to ‘grayordinate’, a special organization of fMRI data to reconstruct functional networks. These SDL methods have a basic assumption that the signal matrix Y can be decomposed into the product of a dictionary matrix D and a sparse coding matrix X. [28]–[30] all improve the performance of the model by reducing the coherence of the dictionary matrix
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