Identification of mild cognitive impairment subtypes predicting conversion to Alzheimer's disease using multimodal data.

ABSTRACT: Mild cognitive impairment (MCI) is a high-risk condition for conversion to Alzheimer's disease (AD) dementia. However, individuals with MCI show heterogeneous patterns of pathology and conversion to AD dementia. Thus, detailed subtyping of MCI subjects and accurate prediction of the patients in whom MCI will convert to AD dementia is critical for identifying at-risk populations and the underlying biological features. To this end, we developed a model that simultaneously subtypes MCI subjects and predicts conversion to AD and performed an analysis of the underlying biological characteristics of each subtype. In particular, a heterogeneous mixture learning (HML) method was used to build a decision tree-based model based on multimodal data, including cerebrospinal fluid (CSF) biomarker data,

SUBMITTER: Kikuchi M 

PROVIDER: S-EPMC9513733 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature