Cross-View Neuroimage Pattern Analysis for Alzheimer's Disease Staging
The research on staging of pre-symptomatic and prodromal phase of neurological disorders, e.g., Alzheimer's disease (AD), is essential for prevention of dementia. New strategies for AD staging with a focus on early detection, are demanded to optimize potential efficacy of disease-modifying ther...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2016-02-01
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| Series: | Frontiers in Aging Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnagi.2016.00023/full |
| _version_ | 1819273965922680832 |
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| author | Sidong eLiu Weidong Tom Cai Sonia ePujol Ron eKikinis David Dagan Feng David Dagan Feng |
| author_facet | Sidong eLiu Weidong Tom Cai Sonia ePujol Ron eKikinis David Dagan Feng David Dagan Feng |
| author_sort | Sidong eLiu |
| collection | DOAJ |
| description | The research on staging of pre-symptomatic and prodromal phase of neurological disorders, e.g., Alzheimer's disease (AD), is essential for prevention of dementia. New strategies for AD staging with a focus on early detection, are demanded to optimize potential efficacy of disease-modifying therapies that can halt or slow the disease progression. Recently, neuroimaging are increasingly used as additional research-based markers to detect AD onset and predict conversion of MCI and normal control (NC) to AD. Researchers have proposed a variety of neuroimaging biomarkers to characterize the patterns of the pathology of AD and MCI, and suggested that multi-view neuroimaging biomarkers could lead to better performance than single-view biomarkers in AD staging. However, it is still unclear what leads to such synergy and how to preserve or maximize. In an attempt to answer these questions, we proposed a cross-view pattern analysis framework for investigating the synergy between different neuroimaging biomarkers. We quantitatively analyzed 9 types of biomarkers derived from FDG-PET and T1-MRI, and evaluated their performance in a task of classifying AD, MCI and NC subjects obtained from the ADNI baseline cohort. The experiment results showed that these biomarkers could depict the pathology of AD from different perspectives, and output distinct patterns that are significantly associated with the disease progression. Most importantly, we found that these features could be separated into clusters, each depicting a particular aspect; and the inter-cluster features could always achieve better performance than the intra-cluster features in AD staging. |
| first_indexed | 2024-12-23T23:00:55Z |
| format | Article |
| id | doaj.art-1120951280f44f259298a15606d0c429 |
| institution | Directory Open Access Journal |
| issn | 1663-4365 |
| language | English |
| last_indexed | 2024-12-23T23:00:55Z |
| publishDate | 2016-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Aging Neuroscience |
| spelling | doaj.art-1120951280f44f259298a15606d0c4292022-12-21T17:26:55ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652016-02-01810.3389/fnagi.2016.00023166249Cross-View Neuroimage Pattern Analysis for Alzheimer's Disease StagingSidong eLiu0Weidong Tom Cai1Sonia ePujol2Ron eKikinis3David Dagan Feng4David Dagan Feng5The University of SydneyThe University of SydneyHarvard Medical SchoolHarvard Medical SchoolThe University of SydneyShanghai Jiao Tong UniversityThe research on staging of pre-symptomatic and prodromal phase of neurological disorders, e.g., Alzheimer's disease (AD), is essential for prevention of dementia. New strategies for AD staging with a focus on early detection, are demanded to optimize potential efficacy of disease-modifying therapies that can halt or slow the disease progression. Recently, neuroimaging are increasingly used as additional research-based markers to detect AD onset and predict conversion of MCI and normal control (NC) to AD. Researchers have proposed a variety of neuroimaging biomarkers to characterize the patterns of the pathology of AD and MCI, and suggested that multi-view neuroimaging biomarkers could lead to better performance than single-view biomarkers in AD staging. However, it is still unclear what leads to such synergy and how to preserve or maximize. In an attempt to answer these questions, we proposed a cross-view pattern analysis framework for investigating the synergy between different neuroimaging biomarkers. We quantitatively analyzed 9 types of biomarkers derived from FDG-PET and T1-MRI, and evaluated their performance in a task of classifying AD, MCI and NC subjects obtained from the ADNI baseline cohort. The experiment results showed that these biomarkers could depict the pathology of AD from different perspectives, and output distinct patterns that are significantly associated with the disease progression. Most importantly, we found that these features could be separated into clusters, each depicting a particular aspect; and the inter-cluster features could always achieve better performance than the intra-cluster features in AD staging.http://journal.frontiersin.org/Journal/10.3389/fnagi.2016.00023/fullNeuroimagingAlzheimer's diseasemild cognitive impairment (MCI)pattern recognitionmultimodal |
| spellingShingle | Sidong eLiu Weidong Tom Cai Sonia ePujol Ron eKikinis David Dagan Feng David Dagan Feng Cross-View Neuroimage Pattern Analysis for Alzheimer's Disease Staging Frontiers in Aging Neuroscience Neuroimaging Alzheimer's disease mild cognitive impairment (MCI) pattern recognition multimodal |
| title | Cross-View Neuroimage Pattern Analysis for Alzheimer's Disease Staging |
| title_full | Cross-View Neuroimage Pattern Analysis for Alzheimer's Disease Staging |
| title_fullStr | Cross-View Neuroimage Pattern Analysis for Alzheimer's Disease Staging |
| title_full_unstemmed | Cross-View Neuroimage Pattern Analysis for Alzheimer's Disease Staging |
| title_short | Cross-View Neuroimage Pattern Analysis for Alzheimer's Disease Staging |
| title_sort | cross view neuroimage pattern analysis for alzheimer 39 s disease staging |
| topic | Neuroimaging Alzheimer's disease mild cognitive impairment (MCI) pattern recognition multimodal |
| url | http://journal.frontiersin.org/Journal/10.3389/fnagi.2016.00023/full |
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