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Prediction of Progressive Mild Cognitive Impairment by Multi-Modal Neuroimaging Biomarkers
Xu, Lele1; Wu, Xia1,2,3; Li, Rui4; Chen, Kewei5,6; Long, Zhiying2,3; Zhang, Jiacai1; Guo, Xiaojuan1; Yao, Li1,2,3; Alzheimer Dis Neuroimaging
AbstractFor patients with mild cognitive impairment (MCI), the likelihood of progression to probable Alzheimer's disease (AD) is important not only for individual patient care, but also for the identification of participants in clinical trial, so as to provide early interventions. Biomarkers based on various neuroimaging modalities could offer complementary information regarding different aspects of disease progression. The current study adopted a weighted multi-modality sparse representation-based classification method to combine data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, from three imaging modalities: Volumetric magnetic resonance imaging (MRI), fluorodeoxyglucose (FDG) positron emission tomography (PET), and florbetapir PET. We included 117 normal controls (NC) and 110 MCI patients, 27 of whom progressed to AD within 36 months (pMCI), while the remaining 83 remained stable (sMCI) over the same time period. Modality-specific biomarkers were identified to distinguish MCI from NC and to predict pMCI among MCI. These included the hippocampus, amygdala, middle temporal and inferior temporal regions for MRI, the posterior cingulum, precentral, and postcentral regions for FDG-PET, and the hippocampus, amygdala, and putamen for florbetapir PET. Results indicated that FDG-PET may be a more effective modality in discriminating MCI from NC and in predicting pMCI than florbetapir PET and MRI. Combining modality-specific sensitive biomarkers from the three modalities boosted the discrimination accuracy of MCI from NC (76.7%) and the prediction accuracy of pMCI (82.5%) when compared with the best single-modality results (73.6% for MCI and 75.6% for pMCI with FDG-PET).
KeywordFlorbetapir Positron Emission Tomography Fluorodeoxyglucose Positron Emission Tomography Magnetic Resonance Imaging Mild Cognitive Impairment Multi-modality Prediction Progressive Mild Cognitive Impairment
Indexed BySCI
Funding Organization863 Program(2015AA020912) ; Funds for International Cooperation and Exchange of the National Natural Science Foundation of China(61210001) ; General Program of National Natural Science Foundation of China(61571047) ; Fundamental Research Funds for the Central Universities ; Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health)(U01AG024904) ; DOD ADNI (Department of Defense)(W81XWH-12-2-0012) ; National Institute on Aging ; National Institute of Biomedical Imaging and Bioengineering ; Canadian Institutes of Health Research
WOS Research AreaNeurosciences & Neurology
WOS SubjectNeurosciences
WOS IDWOS:000374239300011
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
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Document Type期刊论文
Affiliation1.Beijing Normal Univ, Coll Informat Sci & Technol, 19 Xin Jie Kou Wai Da Jie, Beijing 100875, Peoples R China
2.Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China
3.Beijing Normal Univ, IDG McGovern Inst Brain Res, Beijing 100875, Peoples R China
4.Chinese Acad Sci, Inst Psychol, Key Lab Mental Hlth, Ctr Aging Psychol, Beijing 100101, Peoples R China
5.Banner Alzheimers Inst, Phoenix, AZ USA
6.Banner Good Samaritan PET Ctr, Phoenix, AZ USA
Recommended Citation
GB/T 7714
Xu, Lele,Wu, Xia,Li, Rui,et al. Prediction of Progressive Mild Cognitive Impairment by Multi-Modal Neuroimaging Biomarkers[J]. JOURNAL OF ALZHEIMERS DISEASE,2016,51(4):1045-1056.
APA Xu, Lele.,Wu, Xia.,Li, Rui.,Chen, Kewei.,Long, Zhiying.,...&Alzheimer Dis Neuroimaging.(2016).Prediction of Progressive Mild Cognitive Impairment by Multi-Modal Neuroimaging Biomarkers.JOURNAL OF ALZHEIMERS DISEASE,51(4),1045-1056.
MLA Xu, Lele,et al."Prediction of Progressive Mild Cognitive Impairment by Multi-Modal Neuroimaging Biomarkers".JOURNAL OF ALZHEIMERS DISEASE 51.4(2016):1045-1056.
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