Identification of the Early Stage of Alzheimer's Disease Using Structural MRI and Resting-State fMRI
Accurate prediction of the early stage of Alzheimer's disease (AD) is important but very challenging. The goal of this study was to utilize predictors for diagnosis conversion to AD based on integrating resting-state functional MRI (rs-fMRI) connectivity analysis and structural MRI (sMRI). We i...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-08-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fneur.2019.00904/full |
| _version_ | 1811261738593550336 |
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| author | Seyed Hani Hojjati Seyed Hani Hojjati Seyed Hani Hojjati Ata Ebrahimzadeh Abbas Babajani-Feremi Abbas Babajani-Feremi Abbas Babajani-Feremi |
| author_facet | Seyed Hani Hojjati Seyed Hani Hojjati Seyed Hani Hojjati Ata Ebrahimzadeh Abbas Babajani-Feremi Abbas Babajani-Feremi Abbas Babajani-Feremi |
| author_sort | Seyed Hani Hojjati |
| collection | DOAJ |
| description | Accurate prediction of the early stage of Alzheimer's disease (AD) is important but very challenging. The goal of this study was to utilize predictors for diagnosis conversion to AD based on integrating resting-state functional MRI (rs-fMRI) connectivity analysis and structural MRI (sMRI). We included 177 subjects in this study and aimed at identifying patients with mild cognitive impairment (MCI) who progress to AD, MCI converter (MCI-C), patients with MCI who do not progress to AD, MCI non-converter (MCI-NC), patients with AD, and healthy controls (HC). The graph theory was used to characterize different aspects of the rs-fMRI brain network by calculating measures of integration and segregation. The cortical and subcortical measurements, e.g., cortical thickness, were extracted from sMRI data. The rs-fMRI graph measures were combined with the sMRI measures to construct input features of a support vector machine (SVM) and classify different groups of subjects. Two feature selection algorithms [i.e., the discriminant correlation analysis (DCA) and sequential feature collection (SFC)] were used for feature reduction and selecting a subset of optimal features. Maximum accuracy of 67 and 56% for three-group (“AD, MCI-C, and MCI-NC” or “MCI-C, MCI-NC, and HC”) and four-group (“AD, MCI-C, MCI-NC, and HC”) classification, respectively, were obtained with the SFC feature selection algorithm. We also identified hub nodes in the rs-fMRI brain network which were associated with the early stage of AD. Our results demonstrated the potential of the proposed method based on integration of the functional and structural MRI for identification of the early stage of AD. |
| first_indexed | 2024-04-12T19:11:14Z |
| format | Article |
| id | doaj.art-70de58c191354e798aa113ab0f1e8919 |
| institution | Directory Open Access Journal |
| issn | 1664-2295 |
| language | English |
| last_indexed | 2024-04-12T19:11:14Z |
| publishDate | 2019-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj.art-70de58c191354e798aa113ab0f1e89192022-12-22T03:19:52ZengFrontiers Media S.A.Frontiers in Neurology1664-22952019-08-011010.3389/fneur.2019.00904464237Identification of the Early Stage of Alzheimer's Disease Using Structural MRI and Resting-State fMRISeyed Hani Hojjati0Seyed Hani Hojjati1Seyed Hani Hojjati2Ata Ebrahimzadeh3Abbas Babajani-Feremi4Abbas Babajani-Feremi5Abbas Babajani-Feremi6Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, United StatesDepartment of Electrical Engineering, Babol University of Technology, Babol, IranNeuroscience Institute and Children's Foundation Research Institute, Le Bonheur Children's Hospital, Memphis, TN, United StatesDepartment of Electrical Engineering, Babol University of Technology, Babol, IranDepartment of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, United StatesNeuroscience Institute and Children's Foundation Research Institute, Le Bonheur Children's Hospital, Memphis, TN, United StatesDepartment of Anatomy and Neurobiology, University of Tennessee Health Science Center, Memphis, TN, United StatesAccurate prediction of the early stage of Alzheimer's disease (AD) is important but very challenging. The goal of this study was to utilize predictors for diagnosis conversion to AD based on integrating resting-state functional MRI (rs-fMRI) connectivity analysis and structural MRI (sMRI). We included 177 subjects in this study and aimed at identifying patients with mild cognitive impairment (MCI) who progress to AD, MCI converter (MCI-C), patients with MCI who do not progress to AD, MCI non-converter (MCI-NC), patients with AD, and healthy controls (HC). The graph theory was used to characterize different aspects of the rs-fMRI brain network by calculating measures of integration and segregation. The cortical and subcortical measurements, e.g., cortical thickness, were extracted from sMRI data. The rs-fMRI graph measures were combined with the sMRI measures to construct input features of a support vector machine (SVM) and classify different groups of subjects. Two feature selection algorithms [i.e., the discriminant correlation analysis (DCA) and sequential feature collection (SFC)] were used for feature reduction and selecting a subset of optimal features. Maximum accuracy of 67 and 56% for three-group (“AD, MCI-C, and MCI-NC” or “MCI-C, MCI-NC, and HC”) and four-group (“AD, MCI-C, MCI-NC, and HC”) classification, respectively, were obtained with the SFC feature selection algorithm. We also identified hub nodes in the rs-fMRI brain network which were associated with the early stage of AD. Our results demonstrated the potential of the proposed method based on integration of the functional and structural MRI for identification of the early stage of AD.https://www.frontiersin.org/article/10.3389/fneur.2019.00904/fullAlzheimer's disease (AD)mild cognitive impairment (MCI)resting-state fMRIgraph theorymachine learninghub nodes |
| spellingShingle | Seyed Hani Hojjati Seyed Hani Hojjati Seyed Hani Hojjati Ata Ebrahimzadeh Abbas Babajani-Feremi Abbas Babajani-Feremi Abbas Babajani-Feremi Identification of the Early Stage of Alzheimer's Disease Using Structural MRI and Resting-State fMRI Frontiers in Neurology Alzheimer's disease (AD) mild cognitive impairment (MCI) resting-state fMRI graph theory machine learning hub nodes |
| title | Identification of the Early Stage of Alzheimer's Disease Using Structural MRI and Resting-State fMRI |
| title_full | Identification of the Early Stage of Alzheimer's Disease Using Structural MRI and Resting-State fMRI |
| title_fullStr | Identification of the Early Stage of Alzheimer's Disease Using Structural MRI and Resting-State fMRI |
| title_full_unstemmed | Identification of the Early Stage of Alzheimer's Disease Using Structural MRI and Resting-State fMRI |
| title_short | Identification of the Early Stage of Alzheimer's Disease Using Structural MRI and Resting-State fMRI |
| title_sort | identification of the early stage of alzheimer s disease using structural mri and resting state fmri |
| topic | Alzheimer's disease (AD) mild cognitive impairment (MCI) resting-state fMRI graph theory machine learning hub nodes |
| url | https://www.frontiersin.org/article/10.3389/fneur.2019.00904/full |
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