Multi-scale graph-based grading for Alzheimer’s disease prediction

• Published in: Medical image analysis Vol. 67; p. 101850
• Main Authors: Hett, Kilian, Ta, Vinh-Thong, Oguz, Ipek, Manjón, José V., Coupé, Pierrick
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2021; Elsevier BV; Elsevier
• Subjects: Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Alzheimer’s disease classification; Bioengineering; Biomarkers; Brain; Brain - diagnostic imaging; Cognitive ability; Cognitive Dysfunction - diagnostic imaging; Cognitive science; Computer science; Computer Vision and Pattern Recognition; Conversion; Datasets; Graph-based method; Hippocampal subfields; Hippocampus; Humans; Imaging; Inter-subject similarity; Intra-subject variability; Life Sciences; Magnetic Resonance Imaging; Mild cognitive impairment; Multiscale analysis; Neurodegenerative diseases; Neuroscience; Patch-based grading; State-of-the-art reviews; Whole brain analysis; Hippocampal subfields; Inter-subject similarity; Graph-based method; Intra-subject variability; Alzheimer’s disease classification; Mild cognitive impairment; Patch-based grading; Whole brain analysis
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2020.101850

•This work introduces a new approach combining patch-based grading and graph-based model.•Combination of inter-subject similarity and intra-subject variability helps to predict Alzheimer’s disease.•Analysis of whole brain structures and hippocampal subfields jointly enables improvement of prediction performances.•Image-based biomarkers and cognitive data are highly complementary for Alzheimer’s disease prediction. [Display omitted] The prediction of subjects with mild cognitive impairment (MCI) who will progress to Alzheimer’s disease (AD) is clinically relevant, and may above all have a significant impact on accelerating the development of new treatments. In this paper, we present a new MRI-based biomarker that enables us to accurately predict conversion of MCI subjects to AD. In order to better capture the AD signature, we introduce two main contributions. First, we present a new graph-based grading framework to combine inter-subject similarity features and intra-subject variability features. This framework involves patch-based grading of anatomical structures and graph-based modeling of structure alteration relationships. Second, we propose an innovative multiscale brain analysis to capture alterations caused by AD at different anatomical levels. Based on a cascade of classifiers, this multiscale approach enables the analysis of alterations of whole brain structures and hippocampus subfields at the same time. During our experiments using the ADNI-1 dataset, the proposed multiscale graph-based grading method obtained an area under the curve (AUC) of 81% to predict conversion of MCI subjects to AD within three years. Moreover, when combined with cognitive scores, the proposed method obtained 85% of AUC. These results are competitive in comparison to state-of-the-art methods evaluated on the same dataset.