Prognostic model for predicting Alzheimer's disease conversion using functional connectome manifolds.

ABSTRACT:

Background

Early detection of Alzheimer's disease (AD) is essential for timely management and consideration of therapeutic options; therefore, detecting the risk of conversion from mild cognitive impairment (MCI) to AD is crucial during neurodegenerative progression. Existing neuroimaging studies have mostly focused on group differences between individuals with MCI (or AD) and cognitively normal (CN), discarding the temporal information of conversion time. Here, we aimed to develop a prognostic model for AD conversion using functional connectivity (FC) and Cox regression suitable for conversion event modeling.

Methods

We developed a prognostic model using a large-scale Alzheimer's Disease Neuroimaging Initiative dataset, and it was validated using external data obtained from

SUBMITTER: Kim S 

PROVIDER: S-EPMC11465528 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature