Classifying Alzheimer's disease with brain imaging and genetic data using a neural network framework.

ABSTRACT: A long-standing question is how to best use brain morphometric and genetic data to distinguish Alzheimer's disease (AD) patients from cognitively normal (CN) subjects and to predict those who will progress from mild cognitive impairment (MCI) to AD. Here, we use a neural network (NN) framework on both magnetic resonance imaging-derived quantitative structural brain measures and genetic data to address this question. We tested the effectiveness of NN models in classifying and predicting AD. We further performed a novel analysis of the NN model to gain insight into the most predictive imaging and genetics features and to identify possible interactions between features that affect AD risk. Data were obtained from the AD Neuroimaging Initiative cohort and included baseline structural MRI data

SUBMITTER: Ning K 

PROVIDER: S-EPMC5993633 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature