Multimodal Imaging for Enhanced Diagnosis and for Assessing Progression of Alzheimer's Disease

Chunfei Li, Florida International University


A neuroimaging feature extraction model is designed to extract region-based image features whose values are predicted by base learners trained on raw neuroimaging morphological variables. The main objectives are to identify Alzheimer’s disease (AD) in its earliest manifestations, and be able to predict and gauge progression of the disease through the stages of mild cognitive impairment (EMCI), late MCI (LMCI) and AD. The model was evaluated on the ADNI database and showed 75.26% accuracy for the challenging EMCI diagnosis based on the 10-fold cross-validation. Our approach also performed well for the other binary classifications: EMCI vs. LMCI (72.3%), EMCI vs. AD (95%), LMCI vs. AD (84.3%), CN vs. LMCI (77.5%), and CN vs. AD (96.5%). By applying the model to the Genome-wide Association Study, along with the sparse Partial Least Squares regression method, we successfully detected risk genes such as the APOE, TOMM40, RVRL2 and APOC1 along with the new finding of rs917100. Moreover, the research aimed to investigate the relationship of different biomarkers; especially the imaging biomarkers to better understand the precise biologic changes that characterize Alzheimer’s disease. The unique and independent contribution of APOE4 allele status (E4+\E4-), amyloid (Aβ) load status (Amy+\Amy-) and combined APOE4 and Aβ status on regional cortical thickness (CTh) and cognition were evaluated via a series of two-way ANCOVAs with post-hoc Tukey HSD tests. Results showed that decreased CTh is independently associated with Amy+ status in many brain regions, but with E4+ status in very restricted number of brain regions. Among CN and EMCI participants, E4+ status is associated with increased CTh, in medial and inferior temporal regions. Diverging association patterns of global and regional Aβ load with cortical volume were found in the entorhinal, temporal pole and parahippocampal regions, which were positively associated with regional Aβ load, but with a negative correlation for global Aβ load in MCI stages. In addition, strong positive correlations were shown between baseline regional CTh and the difference of CTh in each region between the CN and AD, even after adjusting for the regional Aβ and APOE genotype (E4+: r = 0.521 and E4-: r = 0.694).

Subject Area

Computer Engineering|Biomedical engineering|Electrical engineering

Recommended Citation

Li, Chunfei, "Multimodal Imaging for Enhanced Diagnosis and for Assessing Progression of Alzheimer's Disease" (2018). ProQuest ETD Collection for FIU. AAI10976737.