s2MRI-ADNet: an interpretable deep learning framework integrating Euclidean-graph representations of Alzheimer’s disease solely from structural MRI

Objective To establish a multi-dimensional representation solely on structural MRI (sMRI) for early diagnosis of AD. Methods A total of 3377 participants’ sMRI from four independent databases were retrospectively identified to construct an interpretable deep learning model that integrated multi-dime...

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Bibliographic Details
Published in:Magma (New York, N.Y.) N.Y.), 2024-10, Vol.37 (5), p.845-857
Main Authors: Song, Zhiwei, Li, Honglun, Zhang, Yiyu, Zhu, Chuanzhen, Jiang, Minbo, Song, Limei, Wang, Yi, Ouyang, Minhui, Hu, Fang, Zheng, Qiang
Format: Article
Language:English
Subjects:
Basic Science - Reconstruction algorithms and artificial intelligence
Biomedical Engineering and Bioengineering
Computer Appl. in Life Sciences
Health Informatics
Imaging
Medicine
Medicine & Public Health
Radiology
Research Article
Solid State Physics
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Summary:Objective To establish a multi-dimensional representation solely on structural MRI (sMRI) for early diagnosis of AD. Methods A total of 3377 participants’ sMRI from four independent databases were retrospectively identified to construct an interpretable deep learning model that integrated multi-dimensional representations of AD solely on sMRI (called s 2 MRI-ADNet) by a dual-channel learning strategy of gray matter volume (GMV) from Euclidean space and the regional radiomics similarity network (R2SN) from graph space. Specifically, the GMV feature map learning channel (called GMV-Channel) was to take into consideration spatial information of both long-range spatial relations and detailed localization information, while the node feature and connectivity strength learning channel (called NFCS-Channel) was to characterize the graph-structured R2SN network by a separable learning strategy. Results The s 2 MRI-ADNet achieved a superior classification accuracy of 92.1% and 91.4% under intra-database and inter-database cross-validation. The GMV-Channel and NFCS-Channel captured complementary group-discriminative brain regions, revealing a complementary interpretation of the multi-dimensional representation of brain structure in Euclidean and graph spaces respectively. Besides, the generalizable and reproducible interpretation of the multi-dimensional representation in capturing complementary group-discriminative brain regions revealed a significant correlation between the four independent databases ( p  
ISSN:1352-8661
1352-8661
DOI:10.1007/s10334-024-01178-3