Abstract
Accurate classification of either patients with Alzheimer's disease (AD) or patients with mild cognitive impairment (MCI), the prodromal stage of AD, from cognitively unimpaired (CU) individuals is important for clinical diagnosis and adequate intervention. The current study focused on distinguishing AD or MCI from CU based on the multi-feature kernel supervised within-Class-similar discriminative dictionary learning algorithm (MKSCDDL), which we introduced in a previous study, demonstrating that MKSCDDL had superior performance in face recognition. Structural magnetic resonance imaging (sMRI), fluorodeoxyglucose (FDG) positron emission tomography (PET), and florbetapir-PET data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were all included for classification of AD vs. CU, MCI vs. CU, as well as AD vs. MCI (113 AD patients, 110 MCI patients, and 117 CU subjects). By adopting MKSCDDL, we achieved a classification accuracy of 98.18% for AD vs. CU, 78.50% for MCI vs. CU, and 74.47% for AD vs. MCI, which in each instance was superior to results obtained using several other state-of-the-art approaches (MKL, JRC, mSRC, and mSCDDL). In addition, testing time results outperformed other high quality methods. Therefore, the results suggested that the MKSCDDL procedure is a promising tool for assisting early diagnosis of diseases using neuroimaging data.
Highlights
Alzheimer’s disease (AD) is a complex multifactorial neurodegenerative disorder and is the most common type of dementia, defined by extensive neuronal and synapses loss (Tan et al, 2013; Gao et al, 2016)
For discriminating AD from cognitively unimpaired (CU), MKSCDDL achieved an accuracy of 98.18% that was much better than the best accuracy of 91.18% with single-modality method
For classifying mild cognitive impairment (MCI) from CU, MKSCDDL achieved an accuracy of 78.50%, which was greater than all three single-modality methods
Summary
Alzheimer’s disease (AD) is a complex multifactorial neurodegenerative disorder and is the most common type of dementia, defined by extensive neuronal and synapses loss (Tan et al, 2013; Gao et al, 2016). MKSCDDL was examined for its robustness and efficiency of classification accuracy for AD or MCI with CU, based on three modalities data i.e., sMRI, FDGPET and florbetapir-PET. The feature of sMRI, FDG-PET, and florbetapir-PET were got by averaging the corresponding value of mean volume of GM, SUVr values of TABLE 1 | Demographic information of the subjects, p-value was obtained using one-way ANOVA to the AD, MCI, and CU groups. AD, Alzheimer’s disease; MCI, mild cognitive impairment; CU, cognitively unimpaired; M, male; F, female; MMSE, Mini-Mental State Examination; CDR, Clinical Dementia Rating; EDU, years of education; APOE4, percentage of APOE4 alleles. FDG-PET and florbetapir-PET from each ROI that all the voxels within the ROI of each subject
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