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.

Predicting Cognitive Decline in Subjects at Risk for Alzheimer Disease by Using Combined Cerebrospinal Fluid, MR Imaging, and PET Biomarkers

“…Shaffer et al biomarker utility have been published from ADNI (11,(18)(19)(20)(21)(22)(23)(24), but relatively few have examined the additive utility of such biomarkers above and beyond routine neuropsychological tests. This is critical because none of the biomarkers are likely to be used clinically before cognition is assessed.…”

“…Our study is different from these in a few important ways, however. First, we included FDG PET in our analysis, which is not true for most studies to date including those published by Gomar et al (19), Cui et al (18), Ewers et al (20), Davatzikos et al (11), and McEvoy et al (23). Second, we used whole-brain data rather than regions-of-interest data.…”

Cognitive function is associated with impaired heart rate variability in ageing adults: the Irish longitudinal study on ageing wave one results

“…The conversion rate from MCI to dementia is 14 % annually [30]. Predictors of conversion from MCI to dementia currently include cerebrospinal fluid biomarkers, neuropsychological tests and structural magnetic resonance imaging (MRI) morphometry [8,11,42].…”

New perspectives in the search for reliable biomarkers in Alzheimer disease

“…The combination of CSF markers and cognitive tests, such as Mini-Mental State Examination (MMSE) and the clock drawing, has been also suggested, as it showed to be significantly better than these methods alone for prediction of conversion from MCI to AD 39 . Several studies assessed the predictive accuracy for the diagnosis and conversion to AD when analyzing CSF markers together with MRI and/or neuropsychological and functional measures (NMs) [40][41][42][43] . The results determined that combination of selected MRI, CSF and/or NM features outperformed a single modality of these features.…”