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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Main Authors: Peng Peng, Yongfeng Ju, Yipu Zhang, Kaiming Wang, Suying Jiang, Yuping Wang
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9107250/
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spelling 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&#x2019;an University, Xi&#x2019;an, ChinaSchool of Electronics and Control Engineering, Chang&#x2019;an University, Xi&#x2019;an, ChinaSchool of Electronics and Control Engineering, Chang&#x2019;an University, Xi&#x2019;an, ChinaSchool of Science, Chang&#x2019;an University, Xi&#x2019;an, ChinaSchool of Information Engineering, Chang&#x2019;an University, Xi&#x2019;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/
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