Keywords: Alzheimer's Disease, Alzheimer's Disease, Multimodal fusion
Motivation: Combining multimodal MRI data may help to improve the accuracy of calcification in Alzheimer's disease(AD).
Goal(s): By integrating structural MRI (sMRI), DTI, and functional MRI (fMRI) data, we aimed to discover biomarkers for AD calcification.
Approach: Extended pml-jICA approach was applied for multimodal fusion (sMRI, DTI, and fMRI) to identify biomarkers for AD classification.
Results: Loading parameters revealed significant differences among AD, mild cognitive impairment (MCI), subjective cognitive decline (SCD), and normal cognition (NC) in certain components. The accuracy of the four-class classification using SVM was 55%, and the accuracy for the binary classification of SCD and NC was 80%.
Impact: The observed differences of loading parameters among AD, MCI, SCD, and NC suggested that the combination of multimodal data may provide useful biomarkers for improving the accuracy AD diagnosis.
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