Identification of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Multivariate Predictors
Year: 2011
Type: article
Abstract: Prediction of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is of major interest in AD research. A large number of potential predictors have been proposed, with most investigations tending to examine one or a set of related predictors. In this study, we simultaneously examined multiple features from different modalities of data, including structural magnetic resonance imaging (MRI) morphometry, cerebrospinal fluid (CSF) biomarkers and neuropsychological and functional measures (NMs), to explore an optimal set of predictors of conversion from MCI to AD in an Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. After FreeSurfer-derived MRI feature extraction, CSF and NM feature collection, feature selection was employed to choose optimal subsets of features from each modality. Support vector machine (SVM) classifiers were then trained on normal control (NC) and AD participants. Testing was conducted on MCIc (MCI individuals who have converted to AD within 24 months) and MCInc (MCI individuals who have not converted to AD within 24 months) groups. Classification results demonstrated that NMs outperformed CSF and MRI features. The combination of selected NM, MRI and CSF features attained an accuracy of 67.13%, a sensitivity of 96.43%, a specificity of 48.28%, and an AUC (area under curve) of 0.796. Analysis of the predictive values of MCIc who converted at different follow-up evaluations showed that the predictive values were significantly different between individuals who converted within 12 months and after 12 months. This study establishes meaningful multivariate predictors composed of selected NM, MRI and CSF measures which may be useful and practical for clinical diagnosis.
Source: PLoS ONE
Authors: Yue Cui, Bing Liu, Suhuai Luo, Xiantong Zhen, Ming Fan, Tao Liu, Wanlin Zhu, Mira Park, Tianzi Jiang, Jesse S. Jin, the Alzheimer's Disease Neuroimaging Initiative
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,382
Funders: Foundation for the National Institutes of Health, Alzheimer's Association, Dana Foundation, Bristol-Myers Squibb, Eli Lilly and Company, Pfizer, AstraZeneca, GlaxoSmithKline, Novartis, F. Hoffmann-La Roche, Medpace, Synarc, Alzheimer's Disease Neuroimaging Initiative, Northern California Institute for Research and Education, National Natural Science Foundation of China, Elan, Eisai, China Scholarship Council, National Institutes of Health, U.S. Food and Drug Administration, Genentech, University of California, Los Angeles, University of California, San Diego, Beijing Nova Program, National High-tech Research and Development Program, National Institute on Aging, National Institute of Biomedical Imaging and Bioengineering
Awards: G104586243, G1103752518, UC Davis Alzheimer's Disease Core Center, Alzheimer's Disease Neuroimaging Initiative, G2152643862, G2283900753, G2409989180, G2835601961, G2844381683, G2877684854, G29951154, G3131374986, G3146424788, G3218891203, G3553156159, G3705772492, G3710456697, G3870774570, G3966393351, "MR Morphometrics and Cognitive Decline Rate in Large-Scale Aging Studies", G4243412236, G43180419, G4401988282, G4660785924, G470063782, 轻度认知损害及阿尔茨海默病的多模态脑影像研究和早期诊断系统, G5207404921, G5209990037, G5366509656, G5389965057, G5732003897, G598830915, G6069485058, G6259281481, G6412110892, G6899163935, G6948409098, G7315666996, 精神分裂症脑网络异常的影像遗传学研究, G7414321945, G7799614282, G781725486, Alzheimer's Disease Cooperative Study, G793122433, G8636843673, G9000039018, G9000074849, G9000122335, G920654007, G991187644
OpenAlex ID: W2129030910