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- W2897596898 abstract "Preclinical and prodromal Alzheimer's disease (AD) are at risk states of AD dementia with uncertain long-term prognosis. Thus, biomarker models for the prediction of future cognitive decline are urgently needed. Here, we used support vector machine (SVM) learning in “pure” autosomal dominant AD (ADAD) to train prediction models based on Amyloid-PET, FDG-PET, MRI and CSF biomarkers. The best SVM model was subsequently validated as a predictor of longitudinal 4-year memory decline in a large independent sample of subjects at risk of sporadic late-onset AD. We included 121 mutation-carriers with ADAD from the Dominantly Inherited Alzheimer Network (DIAN) plus 361 amyloid-positive (Aß+) and 319 Aß- subjects (211 cognitively normal controls (CN), 390 MCI, 79 AD dementia) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). In both studies, amyloid-PET, FDG-PET, T1-volumetric MRI and CSF-levels of Aß, total-tau, and p-tau181 were obtained. Freesurfer-based anatomical ROIs values (N = 80) of each imaging modality were computed. Using nested 10-fold cross-validation, support vector machine (SVM) regression models including ROI and CSF values were trained to predict estimated years from dementia symptom onset (EYO) in the mutation carriers (DIAN). Those SVM-based predictor parameters were subsequently applied to the scaled ROI and CSF data in the ADNI cohort to predict longitudinal changes in the composite memory score (ADNI-MEM) over a maximum of 4 years of follow-up. In the mutation-carriers, the 10-fold cross-validated SVM model was highly predictive of EYO (r=0.73, p<0.001, Figure 1), where CSF values of Aß, p-tau and tau had the largest predictive weights. When that SVM model trained in DIAN was tested for the prediction of the rate of memory decline in 361 sporadic Aß+ subjects from ADNI, higher SVM scores predicted stronger memory decline consistently across 1-year follow-up (ß=-0.31, p<0.001), 2-year follow-up (ß=-0.32, p<0.001), 3-year follow-up (ß=-0.31, p<0.001), and 4-year follow-up (ß=-0.41, p<0.001). Scatterplots for these associations including R2 values are shown in Figure 2. The SVM model did not predict cognitive changes in Aß- subjects." @default.
- W2897596898 created "2018-10-26" @default.
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- W2897596898 date "2018-07-01" @default.
- W2897596898 modified "2023-10-16" @default.
- W2897596898 title "P3‐426: CROSS‐VALIDATED BIOMARKER‐BASED PREDICTION OF 4‐YEAR RATE OF COGNITIVE DECLINE IN NON‐DEMENTED SUBJECTS AT RISK OF AD" @default.
- W2897596898 doi "https://doi.org/10.1016/j.jalz.2018.06.1789" @default.
- W2897596898 hasPublicationYear "2018" @default.
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