Robust Identification of Alzheimer's Disease subtypes based on cortical atrophy patterns

Jong Yun Park; Han Kyu Na; Sungsoo Kim; Hyunwook Kim; Hee Jin Kim; Sang Won Seo; Duk L. Na; Cheol E. Han; Joon Kyung Seong

doi:10.1038/srep43270

Robust Identification of Alzheimer's Disease subtypes based on cortical atrophy patterns

Jong Yun Park, Han Kyu Na, Sungsoo Kim, Hyunwook Kim, Hee Jin Kim, Sang Won Seo, Duk L. Na, Cheol E. Han, Joon Kyung Seong

Department of Artificial Intelligence

Research output: Contribution to journal › Article › peer-review

62 Citations (Scopus)

Abstract

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.

Original language	English
Article number	43270
Journal	Scientific reports
Volume	7
DOIs	https://doi.org/10.1038/srep43270
Publication status	Published - 2017 Mar 9

Bibliographical note

Funding 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).

Publisher Copyright:
© The Author(s) 2017.

ASJC Scopus subject areas

General

Access to Document

10.1038/srep43270

Cite this

@article{3779e2c5615c40d29a46d8ec97758d06,

title = "Robust Identification of Alzheimer's Disease subtypes based on cortical atrophy patterns",

abstract = "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.",

author = "Park, {Jong Yun} and Na, {Han Kyu} and Sungsoo Kim and Hyunwook Kim and Kim, {Hee Jin} and Seo, {Sang Won} and Na, {Duk L.} and Han, {Cheol E.} and Seong, {Joon Kyung}",

note = "Funding 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). Publisher Copyright: {\textcopyright} The Author(s) 2017.",

year = "2017",

month = mar,

day = "9",

doi = "10.1038/srep43270",

language = "English",

volume = "7",

journal = "Scientific reports",

issn = "2045-2322",

publisher = "Nature Publishing Group",

}

TY - JOUR

T1 - Robust Identification of Alzheimer's Disease subtypes based on cortical atrophy patterns

AU - Park, Jong Yun

AU - Na, Han Kyu

AU - Kim, Sungsoo

AU - Kim, Hyunwook

AU - Kim, Hee Jin

AU - Seo, Sang Won

AU - Na, Duk L.

AU - Han, Cheol E.

AU - Seong, Joon Kyung

N1 - Funding 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). Publisher Copyright: © The Author(s) 2017.

PY - 2017/3/9

Y1 - 2017/3/9

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85014924701&partnerID=8YFLogxK

U2 - 10.1038/srep43270

DO - 10.1038/srep43270

M3 - Article

C2 - 28276464

AN - SCOPUS:85014924701

SN - 2045-2322

VL - 7

JO - Scientific reports

JF - Scientific reports

M1 - 43270

ER -

Robust Identification of Alzheimer's Disease subtypes based on cortical atrophy patterns

Abstract

Bibliographical note

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this