Accumulating evidence suggests that Alzheimer's disease (AD) is heterogenous and can be classified into several subtypes. Here, we propose a robust subtyping method for AD based on cortical atrophy patterns and graph theory. We calculated similarities between subjects in their atrophy patterns throughout the whole brain, and clustered subjects with similar atrophy patterns using the Louvain method for modular organization extraction. We applied our method to AD patients recruited at Samsung Medical Center and externally validated our method by using the AD Neuroimaging Initiative (ADNI) dataset. Our method categorized very mild AD into three clinically distinct subtypes with high reproducibility (>90%); the parietal-predominant (P), medial temporal-predominant (MT), and diffuse (D) atrophy subtype. The P subtype showed the worst clinical presentation throughout the cognitive domains, while the MT and D subtypes exhibited relatively mild presentation. The MT subtype revealed more impaired language and executive function compared to the D subtype.
Bibliographical noteFunding Information:
This research was supported by the Basic Science Research Program (MSIP-2014-R1A1A1008173), Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) (No. B0101-15-247, Development of Open ICT Healing Platform using Personal Health Data), and the Original Technology Research Program for Brain Science through the National Research Foundation of Korea( NRF) funded by the Ministry of Science ICT and Future Planning (2015M3C7A1029034).
© The Author(s) 2017.
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