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. 2024 Oct;37(5):845-857.
doi: 10.1007/s10334-024-01178-3. Epub 2024 Jun 13.

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

Zhiwei Song #  1 , Honglun Li #  2 , Yiyu Zhang  1 , Chuanzhen Zhu  1 , Minbo Jiang  1 , Limei Song  3 , Yi Wang  1   4 , Minhui Ouyang  5   6 , Fang Hu  4 , Qiang Zheng  7
Affiliations

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

Zhiwei Song et al. MAGMA. 2024 Oct.

Abstract

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 s2MRI-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 s2MRI-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 < 0.05). Significant associations (p < 0.05) between attention scores and brain abnormality, between classification scores and clinical measure of cognitive ability, CSF biomarker, metabolism, and genetic risk score also provided solid neurobiological interpretation.

Conclusion: The s2MRI-ADNet solely on sMRI could leverage the complementary multi-dimensional representations of AD in Euclidean and graph spaces, and achieved superior performance in the early diagnosis of AD, facilitating its potential in both clinical translation and popularization.

Keywords: Alzheimer’s disease; Deep learning; Interpretability; Multi-dimensional representations; Structural MRI.

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