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- W4236060294 abstract "Alzheimer's disease (AD) has been proposed to be primarily a “disconnection syndrome”, whereby the progression of neurodegeneration propagates along cortical networks. Previously, we have introduced methods by which cognitive performance can be predicted from group-wise correlations in cortical thickness, derived from T1-weighted MRI. Here, we extend this basic approach using GLMNET, which allows all network edges to be analyzed in a single multivariate regression model, to yield a small set of edges which best predict the individual behavioural outcome. Cortical thickness estimates were obtained from T1-weighted images obtained from the ADNI-1 cohort. For each pair of regions (ROIs), a linear model was fitted for Normal Control (NC) subjects only. Residual error was computed from this model for all subjects. Using GLMNET, the resulting residuals were regressed against cognitive performance scores in a general linear model (GLM) including residuals from all pairs of ROIs i and j (Figure 1A). Cross-validation was performed by using different subsets of the NC group to fit and test the GLM. We demonstrate the approach using the Alzheimer's Disease Assessment Scale (ADAS-cog). GLMNET predicted ∼40% of the variance using residuals from ∼150 ROI pairs (Figure 1B). As shown in Figure 1C, the strongest coefficients were found for ROI pairs including entorhinal cortex (EC), middle temporal gyrus (MTG), superior parietal gyrus (SPG), precuneus (PCUN), postcentral lobule (PCL), and superior frontal gyrus (SFG)." @default.
- W4236060294 created "2022-05-12" @default.
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- W4236060294 date "2013-07-01" @default.
- W4236060294 modified "2023-09-27" @default.
- W4236060294 title "O1-10-01: Predicting Alzheimer's-related cognition with cortical thickness correlations: A GLMNET approach" @default.
- W4236060294 doi "https://doi.org/10.1016/j.jalz.2013.04.104" @default.
- W4236060294 hasPublicationYear "2013" @default.
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