  Background: The increasing availability of databases containing both magnetic
resonance imaging (MRI) and genetic data allows researchers to utilize
multimodal data to better understand the characteristics of dementia of
Alzheimer's type (DAT). Objective: The goal of this study was to develop and
analyze novel biomarkers that can help predict the development and progression
of DAT. Methods: We used feature selection and ensemble learning classifier to
develop an image/genotype-based DAT score that represents a subject's
likelihood of developing DAT in the future. Three feature types were used: MRI
only, genetic only, and combined multimodal data. We used a novel data
stratification method to better represent different stages of DAT. Using a
pre-defined 0.5 threshold on DAT scores, we predicted whether or not a subject
would develop DAT in the future. Results: Our results on Alzheimer's Disease
Neuroimaging Initiative (ADNI) database showed that dementia scores using
genetic data could better predict future DAT progression for currently normal
control subjects (Accuracy=0.857) compared to MRI (Accuracy=0.143), while MRI
can better characterize subjects with stable mild cognitive impairment
(Accuracy=0.614) compared to genetics (Accuracy=0.356). Combining MRI and
genetic data showed improved classification performance in the remaining
stratified groups. Conclusion: MRI and genetic data can contribute to DAT
prediction in different ways. MRI data reflects anatomical changes in the
brain, while genetic data can detect the risk of DAT progression prior to the
symptomatic onset. Combining information from multimodal data in the right way
can improve prediction performance.
