A Novel Metabolic Connectome Method to Predict Progression to Mild Cognitive Impairment
Objective. Glucose-based positron emission tomography (PET) imaging has been widely used to predict the progression of mild cognitive impairment (MCI) into Alzheimer’s disease (AD) clinically. However, existing discriminant methods are unsubtle to reveal pathophysiological changes. Therefore, we pre...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Behavioural Neurology |
| Online Access: | http://dx.doi.org/10.1155/2020/2825037 |
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| _version_ | 1832547773484892160 |
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| author | Min Wang Zhuangzhi Yan Shu-yun Xiao Chuantao Zuo Jiehui Jiang |
| author_facet | Min Wang Zhuangzhi Yan Shu-yun Xiao Chuantao Zuo Jiehui Jiang |
| author_sort | Min Wang |
| collection | DOAJ |
| description | Objective. Glucose-based positron emission tomography (PET) imaging has been widely used to predict the progression of mild cognitive impairment (MCI) into Alzheimer’s disease (AD) clinically. However, existing discriminant methods are unsubtle to reveal pathophysiological changes. Therefore, we present a novel metabolic connectome-based predictive modeling to predict progression from MCI to AD accurately. Methods. In this study, we acquired fluorodeoxyglucose PET images and clinical assessments from 420 MCI patients with 36 months follow-up. Individual metabolic network based on connectome analysis was constructed, and the metabolic connectivity in this network was extracted as predictive features. Three different classification strategies were implemented to interrogate the predictive performance. To verify the effectivity of selected features, specific brain regions associated with MCI conversion were identified based on these features and compared with prior knowledge. Results. As a result, 4005 connectome features were obtained, and 153 in which were selected as efficient features. Our proposed feature extraction method had achieved 85.2% accuracy for MCI conversion prediction (sensitivity: 88.1%; specificity: 81.2%; and AUC: 0.933). The discriminative brain regions associated with MCI conversion were mainly located in the precentral gyrus, precuneus, lingual, and inferior frontal gyrus. Conclusion. Overall, the results suggest that our proposed individual metabolic connectome method has great potential to predict whether MCI patients will progress to AD. The metabolic connectome may help to identify brain metabolic dysfunction and build a clinically applicable biomarker to predict the MCI progression. |
| format | Article |
| id | doaj-art-45f937c88cc547f5bac54ccaf00e29ca |
| institution | Kabale University |
| issn | 0953-4180 1875-8584 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Behavioural Neurology |
| spelling | doaj-art-45f937c88cc547f5bac54ccaf00e29ca2025-02-03T06:43:26ZengWileyBehavioural Neurology0953-41801875-85842020-01-01202010.1155/2020/28250372825037A Novel Metabolic Connectome Method to Predict Progression to Mild Cognitive ImpairmentMin Wang0Zhuangzhi Yan1Shu-yun Xiao2Chuantao Zuo3Jiehui Jiang4Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, ChinaInstitute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, ChinaDepartment of Brain and Mental Disease, Shanghai Hospital of Traditional Chinese Medicine, Shanghai, ChinaPET Center, Huashan Hospital, Fudan University, Shanghai, ChinaInstitute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, ChinaObjective. Glucose-based positron emission tomography (PET) imaging has been widely used to predict the progression of mild cognitive impairment (MCI) into Alzheimer’s disease (AD) clinically. However, existing discriminant methods are unsubtle to reveal pathophysiological changes. Therefore, we present a novel metabolic connectome-based predictive modeling to predict progression from MCI to AD accurately. Methods. In this study, we acquired fluorodeoxyglucose PET images and clinical assessments from 420 MCI patients with 36 months follow-up. Individual metabolic network based on connectome analysis was constructed, and the metabolic connectivity in this network was extracted as predictive features. Three different classification strategies were implemented to interrogate the predictive performance. To verify the effectivity of selected features, specific brain regions associated with MCI conversion were identified based on these features and compared with prior knowledge. Results. As a result, 4005 connectome features were obtained, and 153 in which were selected as efficient features. Our proposed feature extraction method had achieved 85.2% accuracy for MCI conversion prediction (sensitivity: 88.1%; specificity: 81.2%; and AUC: 0.933). The discriminative brain regions associated with MCI conversion were mainly located in the precentral gyrus, precuneus, lingual, and inferior frontal gyrus. Conclusion. Overall, the results suggest that our proposed individual metabolic connectome method has great potential to predict whether MCI patients will progress to AD. The metabolic connectome may help to identify brain metabolic dysfunction and build a clinically applicable biomarker to predict the MCI progression.http://dx.doi.org/10.1155/2020/2825037 |
| spellingShingle | Min Wang Zhuangzhi Yan Shu-yun Xiao Chuantao Zuo Jiehui Jiang A Novel Metabolic Connectome Method to Predict Progression to Mild Cognitive Impairment Behavioural Neurology |
| title | A Novel Metabolic Connectome Method to Predict Progression to Mild Cognitive Impairment |
| title_full | A Novel Metabolic Connectome Method to Predict Progression to Mild Cognitive Impairment |
| title_fullStr | A Novel Metabolic Connectome Method to Predict Progression to Mild Cognitive Impairment |
| title_full_unstemmed | A Novel Metabolic Connectome Method to Predict Progression to Mild Cognitive Impairment |
| title_short | A Novel Metabolic Connectome Method to Predict Progression to Mild Cognitive Impairment |
| title_sort | novel metabolic connectome method to predict progression to mild cognitive impairment |
| url | http://dx.doi.org/10.1155/2020/2825037 |
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