Imaging-based enrichment criteria using deep learning algorithms for efficient clinical trials in mild cognitive impairment.

ABSTRACT: The mild cognitive impairment (MCI) stage of Alzheimer's disease (AD) may be optimal for clinical trials to test potential treatments for preventing or delaying decline to dementia. However, MCI is heterogeneous in that not all cases progress to dementia within the time frame of a trial and some may not have underlying AD pathology. Identifying those MCIs who are most likely to decline during a trial and thus most likely to benefit from treatment will improve trial efficiency and power to detect treatment effects. To this end, using multimodal, imaging-derived, inclusion criteria may be especially beneficial. Here, we present a novel multimodal imaging marker that predicts future cognitive and neural decline from [F-18]fluorodeoxyglucose positron emission tomography (PET), amyloid florbeta

SUBMITTER: Ithapu VK 

PROVIDER: S-EPMC4684492 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature