Identifying Alzheimer’s disease and mild cognitive impairment with atlas-based multi-modal metrics
IntroductionMulti-modal neuroimaging metrics in combination with advanced machine learning techniques have attracted more and more attention for an effective multi-class identification of Alzheimer’s disease (AD), mild cognitive impairment (MCI) and health controls (HC) recently.MethodsIn this paper...
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| Format: | Article |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Aging Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2023.1212275/full |
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| author | Zhuqing Long Zhuqing Long Jie Li Jianghua Fan Bo Li Yukeng Du Shuang Qiu Jichang Miao Jian Chen Jian Chen Juanwu Yin Bin Jing Bin Jing |
| author_facet | Zhuqing Long Zhuqing Long Jie Li Jianghua Fan Bo Li Yukeng Du Shuang Qiu Jichang Miao Jian Chen Jian Chen Juanwu Yin Bin Jing Bin Jing |
| author_sort | Zhuqing Long |
| collection | DOAJ |
| description | IntroductionMulti-modal neuroimaging metrics in combination with advanced machine learning techniques have attracted more and more attention for an effective multi-class identification of Alzheimer’s disease (AD), mild cognitive impairment (MCI) and health controls (HC) recently.MethodsIn this paper, a total of 180 subjects consisting of 44 AD, 66 MCI and 58 HC subjects were enrolled, and the multi-modalities of the resting-state functional magnetic resonance imaging (rs-fMRI) and the structural MRI (sMRI) for all participants were obtained. Then, four kinds of metrics including the Hurst exponent (HE) metric and bilateral hippocampus seed independently based connectivity metrics generated from fMRI data, and the gray matter volume (GMV) metric obtained from sMRI data, were calculated and extracted in each region of interest (ROI) based on a newly proposed automated anatomical Labeling (AAL3) atlas after data pre-processing. Next, these metrics were selected with a minimal redundancy maximal relevance (MRMR) method and a sequential feature collection (SFC) algorithm, and only a subset of optimal features were retained after this step. Finally, the support vector machine (SVM) based classification methods and artificial neural network (ANN) algorithm were utilized to identify the multi-class of AD, MCI and HC subjects in single modal and multi-modal metrics respectively, and a nested ten-fold cross-validation was utilized to estimate the final classification performance.ResultsThe results of the SVM and ANN based methods indicated the best accuracies of 80.36 and 74.40%, respectively, by utilizing all the multi-modal metrics, and the optimal accuracies for AD, MCI and HC were 79.55, 78.79 and 82.76%, respectively, in the SVM based method. In contrast, when using single modal metric, the SVM based method obtained a best accuracy of 72.62% with the HE metric, and the accuracies for AD, MCI and HC subjects were just 56.82, 80.30 and 75.86%, respectively. Moreover, the overlapping abnormal brain regions detected by multi-modal metrics were mainly located at posterior cingulate gyrus, superior frontal gyrus and cuneus.ConclusionTaken together, the SVM based method with multi-modal metrics could provide effective diagnostic information for identifying AD, MCI and HC subjects. |
| first_indexed | 2024-03-12T11:48:49Z |
| format | Article |
| id | doaj.art-92a4f8e232594933b2451b373cc0bf89 |
| institution | Directory Open Access Journal |
| issn | 1663-4365 |
| language | English |
| last_indexed | 2024-03-12T11:48:49Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Aging Neuroscience |
| spelling | doaj.art-92a4f8e232594933b2451b373cc0bf892023-08-31T12:09:47ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652023-08-011510.3389/fnagi.2023.12122751212275Identifying Alzheimer’s disease and mild cognitive impairment with atlas-based multi-modal metricsZhuqing Long0Zhuqing Long1Jie Li2Jianghua Fan3Bo Li4Yukeng Du5Shuang Qiu6Jichang Miao7Jian Chen8Jian Chen9Juanwu Yin10Bin Jing11Bin Jing12Medical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha, Hunan Province, ChinaSchool of Biomedical Engineering, Capital Medical University, Beijing, ChinaMedical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha, Hunan Province, ChinaDepartment of Pediatric Emergency Center, Hunan Children’s Hospital, Changsha, Hunan Province, ChinaDepartment of Traditional Chinese Medicine, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, ChinaMedical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha, Hunan Province, ChinaMedical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha, Hunan Province, ChinaDepartment of Medical Devices, Nanfang Hospital, Guangzhou, ChinaSchool of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou, Fujian, ChinaBeijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Beijing, ChinaMedical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha, Hunan Province, ChinaSchool of Biomedical Engineering, Capital Medical University, Beijing, ChinaBeijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Beijing, ChinaIntroductionMulti-modal neuroimaging metrics in combination with advanced machine learning techniques have attracted more and more attention for an effective multi-class identification of Alzheimer’s disease (AD), mild cognitive impairment (MCI) and health controls (HC) recently.MethodsIn this paper, a total of 180 subjects consisting of 44 AD, 66 MCI and 58 HC subjects were enrolled, and the multi-modalities of the resting-state functional magnetic resonance imaging (rs-fMRI) and the structural MRI (sMRI) for all participants were obtained. Then, four kinds of metrics including the Hurst exponent (HE) metric and bilateral hippocampus seed independently based connectivity metrics generated from fMRI data, and the gray matter volume (GMV) metric obtained from sMRI data, were calculated and extracted in each region of interest (ROI) based on a newly proposed automated anatomical Labeling (AAL3) atlas after data pre-processing. Next, these metrics were selected with a minimal redundancy maximal relevance (MRMR) method and a sequential feature collection (SFC) algorithm, and only a subset of optimal features were retained after this step. Finally, the support vector machine (SVM) based classification methods and artificial neural network (ANN) algorithm were utilized to identify the multi-class of AD, MCI and HC subjects in single modal and multi-modal metrics respectively, and a nested ten-fold cross-validation was utilized to estimate the final classification performance.ResultsThe results of the SVM and ANN based methods indicated the best accuracies of 80.36 and 74.40%, respectively, by utilizing all the multi-modal metrics, and the optimal accuracies for AD, MCI and HC were 79.55, 78.79 and 82.76%, respectively, in the SVM based method. In contrast, when using single modal metric, the SVM based method obtained a best accuracy of 72.62% with the HE metric, and the accuracies for AD, MCI and HC subjects were just 56.82, 80.30 and 75.86%, respectively. Moreover, the overlapping abnormal brain regions detected by multi-modal metrics were mainly located at posterior cingulate gyrus, superior frontal gyrus and cuneus.ConclusionTaken together, the SVM based method with multi-modal metrics could provide effective diagnostic information for identifying AD, MCI and HC subjects.https://www.frontiersin.org/articles/10.3389/fnagi.2023.1212275/fullmulti-modal imagingbrain atlasHurst exponentsupport vector machineartificial neural network |
| spellingShingle | Zhuqing Long Zhuqing Long Jie Li Jianghua Fan Bo Li Yukeng Du Shuang Qiu Jichang Miao Jian Chen Jian Chen Juanwu Yin Bin Jing Bin Jing Identifying Alzheimer’s disease and mild cognitive impairment with atlas-based multi-modal metrics Frontiers in Aging Neuroscience multi-modal imaging brain atlas Hurst exponent support vector machine artificial neural network |
| title | Identifying Alzheimer’s disease and mild cognitive impairment with atlas-based multi-modal metrics |
| title_full | Identifying Alzheimer’s disease and mild cognitive impairment with atlas-based multi-modal metrics |
| title_fullStr | Identifying Alzheimer’s disease and mild cognitive impairment with atlas-based multi-modal metrics |
| title_full_unstemmed | Identifying Alzheimer’s disease and mild cognitive impairment with atlas-based multi-modal metrics |
| title_short | Identifying Alzheimer’s disease and mild cognitive impairment with atlas-based multi-modal metrics |
| title_sort | identifying alzheimer s disease and mild cognitive impairment with atlas based multi modal metrics |
| topic | multi-modal imaging brain atlas Hurst exponent support vector machine artificial neural network |
| url | https://www.frontiersin.org/articles/10.3389/fnagi.2023.1212275/full |
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