Predicting Alzheimer's disease progression using multi-modal deep learning approach.

TitlePredicting Alzheimer's disease progression using multi-modal deep learning approach.
Publication TypeJournal Article
Year of Publication2019
AuthorsLee G, Nho K, Kang B, Sohn K-A, Kim D
Corporate Authorsfor Alzheimer’s Disease Neuroimaging Initiative
JournalSci Rep
Date Published2019 02 13

Alzheimer's disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI). The proposed framework integrated longitudinal multi-domain data. Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC = 0.86) when incorporating longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials.

Alternate JournalSci Rep
PubMed ID30760848
PubMed Central IDPMC6374429
Grant List / / CIHR / Canada
R01 LM012535 / LM / NLM NIH HHS / United States
P30 AG010129 / AG / NIA NIH HHS / United States
U24 AG021886 / AG / NIA NIH HHS / United States
R03 AG054936 / AG / NIA NIH HHS / United States
U01 AG024904 / AG / NIA NIH HHS / United States
UL1 TR002369 / TR / NCATS NIH HHS / United States

Weill Cornell Medicine Neurology 525 E. 68th St.
PO Box 117
New York, NY 10065 Phone: (212) 746-6575