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- W2000972871 abstract "Dubois and coworkers have suggested a revision of the NINCDS-ADRDA criterion for the diagnosis of Alzheimer's disease (AD). This new criterion is still centered on a clinical core of early and significant episodic memory impairment, but also includes at least one abnormal biomarker among magnetic resonance imaging (MRI), positron emission tomography (PET) and cerebrospinal fluid (CSF). The aim of this project is to investigate the potential of combining MRI and CSF using multivariate data analysis for classifying AD subjects and subjects with mild cognitive impairment (MCI) compared to healthy controls (CTL). A total of 373 subjects from the Alzheimer's disease Neuroimaging Initiative (ADNI) are included in the current study (AD = 96, MCI = 166 and CTL = 111). Freesurfer was used to generate regional volume and regional cortical thickness measures. A total of 60 variables were used for multivariate analysis (34 cortical thickness measures, 23 volumetric measures and 3 CSF measures: Aß42, t-tau and p-tau). Orthogonal partial least square to latent structures (OPLS) models were created for CSF, MRI and CSF+MRI measures to distinguish between AD vs. CTL and MCI vs. CTL. Combining CSF and MRI gave the best predictive accuracy for distinguishing between AD and CTL. We received an accuracy of 91.8% for the combined model compared to 81.6% for CSF measures and 87.0% for MRI measures. The combined model also gave the best accuracy when comparing MCI and CTL (75.8%). Within the MCI cohorts 36 subjects converted at 18 month follow-up, 88.9% of which were correctly classified. Combining MRI and CSF measures in a multivariate model gave better predictive accuracy, than using them separately. This shows that combining different measures are advantageous for the early diagnosis of AD. The method also shows potential for selecting populations for clinical trials." @default.
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- W2000972871 date "2011-07-01" @default.
- W2000972871 modified "2023-10-02" @default.
- W2000972871 title "P4-298: Combining MRI and CSF measures for classification of Alzheimer's disease and prediction of mild cognitive impairment conversion" @default.
- W2000972871 doi "https://doi.org/10.1016/j.jalz.2011.09.043" @default.
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