Sparse Representation and Dictionary Learning Model Incorporating Group Sparsity and Incoherence to Extract Abnormal Brain Regions Associated With Schizophrenia
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 di...
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doaj-9f28b5d7e7ce4359a8e476ba5907a6572021-03-30T02:19:09ZengIEEEIEEE Access2169-35362020-01-01810439610440610.1109/ACCESS.2020.29995139107250Sparse Representation and Dictionary Learning Model Incorporating Group Sparsity and Incoherence to Extract Abnormal Brain Regions Associated With SchizophreniaPeng Peng0https://orcid.org/0000-0001-9637-424XYongfeng Ju1Yipu Zhang2Kaiming Wang3Suying Jiang4Yuping Wang5School of Electronics and Control Engineering, Chang’an University, Xi’an, ChinaSchool of Electronics and Control Engineering, Chang’an University, Xi’an, ChinaSchool of Electronics and Control Engineering, Chang’an University, Xi’an, ChinaSchool of Science, Chang’an University, Xi’an, ChinaSchool of Information Engineering, Chang’an University, Xi’an, ChinaDepartment of Biomedical Engineering, Tulane University, New Orleans, LA, USASchizophrenia 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<sub>1</sub> - 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.https://ieeexplore.ieee.org/document/9107250/Group sparsityincoherencesparse representation and dictionary learningabnormal brain regionsschizophrenia |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Peng Peng Yongfeng Ju Yipu Zhang Kaiming Wang Suying Jiang Yuping Wang |
spellingShingle |
Peng Peng Yongfeng Ju Yipu Zhang Kaiming Wang Suying Jiang Yuping Wang Sparse Representation and Dictionary Learning Model Incorporating Group Sparsity and Incoherence to Extract Abnormal Brain Regions Associated With Schizophrenia IEEE Access Group sparsity incoherence sparse representation and dictionary learning abnormal brain regions schizophrenia |
author_facet |
Peng Peng Yongfeng Ju Yipu Zhang Kaiming Wang Suying Jiang Yuping Wang |
author_sort |
Peng Peng |
title |
Sparse Representation and Dictionary Learning Model Incorporating Group Sparsity and Incoherence to Extract Abnormal Brain Regions Associated With Schizophrenia |
title_short |
Sparse Representation and Dictionary Learning Model Incorporating Group Sparsity and Incoherence to Extract Abnormal Brain Regions Associated With Schizophrenia |
title_full |
Sparse Representation and Dictionary Learning Model Incorporating Group Sparsity and Incoherence to Extract Abnormal Brain Regions Associated With Schizophrenia |
title_fullStr |
Sparse Representation and Dictionary Learning Model Incorporating Group Sparsity and Incoherence to Extract Abnormal Brain Regions Associated With Schizophrenia |
title_full_unstemmed |
Sparse Representation and Dictionary Learning Model Incorporating Group Sparsity and Incoherence to Extract Abnormal Brain Regions Associated With Schizophrenia |
title_sort |
sparse representation and dictionary learning model incorporating group sparsity and incoherence to extract abnormal brain regions associated with schizophrenia |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
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<sub>1</sub> - 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. |
topic |
Group sparsity incoherence sparse representation and dictionary learning abnormal brain regions schizophrenia |
url |
https://ieeexplore.ieee.org/document/9107250/ |
work_keys_str_mv |
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