Abstract
Self-Weighting Grading Biomarker Based on Graph-Guided Information Propagation for the Prediction of Mild Cognitive Impairment Conversion
Highlights
Alzheimer’s disease (AD) is a common progressive neurodegenerative disease and is likely to affect 1 in 85 people by 2050 [1]
We propose a new biomarker of Mild cognitive impairment (MCI) subject which can make the best of the information derived from AD and normal control (NC) subjects, aiming to enhance the Magnetic resonance imaging (MRI)-based prediction accuracy of MCI-to-AD conversion
CLASSIFICATION PERFORMANCE OF COEFFICIENT FEATURES BASED ON GRAPH-GUIDED INFORMATION PROPAGATION In this subsection, the performance of the proposed coefficient features was firstly compared with the morphological features, features based on ridge regression and features based on elastic net according to cortical thickness (CT) and VOL data, respectively
Summary
Alzheimer’s disease (AD) is a common progressive neurodegenerative disease and is likely to affect 1 in 85 people by 2050 [1]. The symptoms associated with AD are memory loss, cognitive decline, language deterioration and so forth, which bring difficulties to patients’ daily life [2], [3]. Mild cognitive impairment (MCI), commonly accompanied by a measurable memory impairment but largely intact cognitive functioning, is referred to as the transition stage between normal aging and AD [4], [5]. The associate editor coordinating the review of this article and approving it for publication was Muhammad E.
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