Machine Learning Based Multimodal Neuroimaging Genomics Dementia Score for Predicting Future Conversion to Alzheimer's Disease.

ABSTRACT:

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 D

SUBMITTER: Mirabnahrazam G 

PROVIDER: S-EPMC9195128 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature