AUTHOR=Liu Ke , Li Qing , Yao Li , Guo Xiaojuan 

TITLE=The Coupled Representation of Hierarchical Features for Mild Cognitive Impairment and Alzheimer's Disease Classification

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 16 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.902528

DOI=10.3389/fnins.2022.902528

ISSN=1662-453X

ABSTRACT=Structural magnetic resonance imaging (MRI) features have played an increasingly crucial role in discriminating Alzheimer’s disease (AD) patients and mild cognitive impairment (MCI), from normal controls (NC). However, the large number of structural MRI studies only extracted low-level neuroimaging features, or simply concatenated multitudinous features while ignoring the interregional covariate information. The appropriate representation and integration of multi-level features will be preferable for the precise discrimination in the progression of AD. In this study, we proposed a novel inter-coupled feature representation method and built an integration model considering the two-level (regions of interest (ROI)-level and network-level) coupled features based on structural MRI data. For the intra-coupled interactions about the network-level features, we performed the ROI-level (intra- and inter-) coupled interaction within each network by features expansion and coupling learning. For the inter-coupled interaction of the network-level features, we measured the coupled relationships among different networks via Canonical Correlation Analysis. We evaluated the classification performance using coupled feature representations on Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Results showed that the coupled integration model with hierarchical features achieved the optimal classification performance with an accuracy of 90.44% for AD and NC groups, with an accuracy of 87.72% for the MCI converters (MCI-c) and MCI non-converters (MCI-nc) groups. These findings suggested that our two-level coupled interaction representation of hierarchical features has been the effective means for the precise discrimination of MCI-c from MCI-nc groups and therefore be helpful in the characterization of different AD courses.