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

• Published in: Neurobiology of aging 2018-08, Vol.68, p.151-158
• Main Authors: Ning, Kaida, Chen, Bo, Sun, Fengzhu, Hobel, Zachary, Zhao, Lu, Matloff, Will, Toga, Arthur W.
• Format: Article
• Language: English
• Subjects: Aged; Aged, 80 and over; Alzheimer Disease - classification; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - genetics; Alzheimer's disease; Apolipoproteins E - genetics; Brain - diagnostic imaging; Brain - pathology; Brain imaging; Cognitive Dysfunction; Cohort Studies; Databases, Genetic; Disease Progression; Female; Genetics; Humans; Magnetic Resonance Imaging; Male; Mild cognitive impairment; Nerve Net - diagnostic imaging; Nerve Net - pathology; Neural network; Neuroimaging; Organ Size; Risk; ROC Curve; Understanding neural network
• ISSN: 0197-4580; 1558-1497; 1558-1497
• DOI: 10.1016/j.neurobiolaging.2018.04.009

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 and single nucleotide polymorphism (SNP) data for 138 AD patients, 225 CN subjects, and 358 MCI patients. We found that NN models with both brain and SNP features as predictors perform significantly better than models with either alone in classifying AD and CN subjects, with an area under the receiver operating characteristic curve (AUC) of 0.992, and in predicting the progression from MCI to AD (AUC=0.835). The most important predictors in the NN model were the left middle temporal gyrus volume, the left hippocampus volume, the right entorhinal cortex volume, and the APOE (a gene that encodes apolipoprotein E) ɛ4 risk allele. Furthermore, we identified interactions between the right parahippocampal gyrus and the right lateral occipital gyrus, the right banks of the superior temporal sulcus and the left posterior cingulate, and SNP rs10838725 and the left lateral occipital gyrus. Our work shows the ability of NN models to not only classify and predict AD occurrence but also to identify important AD risk factors and interactions among them.