Title: Deep learning of MRI brain images for early prediction of Alzheimer’s disease dementia and cognitive decline
Early prediction of Alzheimer’s disease (AD) and cognitive decline could promote timely interventions to slow or halt dementia progression. Although clinical criteria for mild cognitive impairment (MCI) and AD have been developed to formalize assessment of the gradual progression of the disease, it remains challenging to predict when and which individuals who meet criteria for MCI will ultimately progress to AD dementia. It is even more difficult to predict when a cognitively normal person will have cognitive decline. In this talk, we will present our recent neuroimaging studies for prediction of individual MCI subjects’ progression to AD dementia and cognitive decline of cognitively normal people based on their structural magnetic resonance imaging (MRI) data. Particularly, we have developed deep learning frameworks to extract informative features from MRI data and build prognostic models on the extracted features to predict AD dementia and cognitive decline in a time-to-event analysis setting. We have evaluated the proposed methods using baseline structural MRI data of subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Australian Imaging Biomarkers and Lifestyle Study of Aging (AIBL). We also compared the deep learning-based imaging features with conventionally hand-crafted imaging features. Extensive evaluation experiments have demonstrated that the deep learning prediction models could achieve promising performance for early predicting rapidity of dementia progression and cognitive decline of individual older adults based on their baseline MRI images and demographic and cognitive measures.