title: Identification of conversion from mild cognitive impairment to Alzheimer's disease using multivariate predictors.
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.
authors.affiliation: School of Design, Communication and Information Technology, University of Newcastle, Newcastle, New South Wales, Australia.
authors.forename: Yue
authors.initials: Y
authors.lastname: Cui
authors.forename: Bing
authors.initials: B
authors.lastname: Liu
authors.forename: Suhuai
authors.initials: S
authors.lastname: Luo
authors.forename: Xiantong
authors.initials: X
authors.lastname: Zhen
authors.forename: Ming
authors.initials: M
authors.lastname: Fan
authors.forename: Tao
authors.initials: T
authors.lastname: Liu
authors.forename: Wanlin
authors.initials: W
authors.lastname: Zhu
authors.forename: Mira
authors.initials: M
authors.lastname: Park
authors.forename: Tianzi
authors.initials: T
authors.lastname: Jiang
authors.forename: Jesse S
authors.initials: JS
authors.lastname: Jin
mesh_terms: D000368:Aged; D000544:Alzheimer Disease / Q000134:cerebrospinal fluid / Q000175:diagnosis* / Q000209:etiology*; D015415:Biomarkers / Q000134:cerebrospinal fluid*; D060825:Cognitive Dysfunction / Q000134:cerebrospinal fluid / Q000150:complications* / Q000175:diagnosis*; D018450:Disease Progression; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D059906:Neuroimaging; D009483:Neuropsychological Tests; D011237:Predictive Value of Tests; D060388:Support Vector Machine
chemical_list: D015415:Biomarkers