Abstract
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.
Highlights
Alzheimer’s disease (AD) is an irreversible, progressive neurodegenerative disorder characterized by abnormal accumulation of amyloid plaques and neurofibrillary tangles in the brain, causing problems with memory, thinking, and behavior
The overall goal of Alzheimer’s Disease Neuroimaging Initiative cohort (ADNI) is to test whether serial magnetic resonance imaging (MRI), position emission tomography (PET), other biological markers, and clinical and neuropsychological assessment could be combined to measure the progression of mild cognitive impairment (MCI) and early AD
Demographic information, raw neuroimaging scan data, APOE genotype, Cerebrospinal fluid (CSF) measurements, neuropsychological test scores, and diagnostic information are publicly available from the ADNI data repository
Summary
Alzheimer’s disease (AD) is an irreversible, progressive neurodegenerative disorder characterized by abnormal accumulation of amyloid plaques and neurofibrillary tangles in the brain, causing problems with memory, thinking, and behavior. Various machine learning methods have been applied to identify biomarkers for MCI conversion prediction and improve their performances. A multi-task learning along with SVM was used to identify AD-relevant features, showing 73.9% accuracy, 68.6% sensitivity, and 73.6% specificity[7]. The integration of multi-modality data improves the performance for MCI conversion prediction by extracting complementary AD-related biomarkers from each modality. In order to predict MCI to AD conversion, we proposed a multimodal recurrent neural network method, a deep learning approach, based on the integration of demographic information, longitudinal CSF biomarkers, longitudinal cognitive performance, and cross-sectional neuroimaging biomarkers at baseline obtained from the Alzheimer’s Disease Neuroimaging Initiative cohort (ADNI). Non-overlapping samples as well as overlapping samples from each data can be used to build a prediction model
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