TY - JOUR
T1 - Four Distinct Subtypes of Alzheimer's Disease Based on Resting-State Connectivity Biomarkers
AU - Chen, Pindong
AU - Yao, Hongxiang
AU - Alzheimer's Disease Neuroimaging Initiative
AU - Tijms, Betty M.
AU - Wang, Pan
AU - Wang, Dawei
AU - Song, Chengyuan
AU - Yang, Hongwei
AU - Zhang, Zengqiang
AU - Zhao, Kun
AU - Qu, Yida
AU - Kang, Xiaopeng
AU - du, Kai
AU - Fan, Lingzhong
AU - Han, Tong
AU - Yu, Chunshui
AU - Zhang, Xi
AU - Jiang, Tianzi
AU - Zhou, Yuying
AU - Lu, Jie
AU - Han, Ying
AU - Liu, Bing
AU - Zhou, Bo
AU - Liu, Yong
N1 - Funding Information: This work was partially supported by the Fundamental Research Funds for the Central Universities (Grant No. 2021XD-A03-1 [to YL]), the Beijing Natural Science Funds for Distinguished Young Scholars (Grant No. JQ20036 [to YL]), the National Natural Science Foundation of China (Grant Nos. 81871438 [to YL], 82172018 [to YL], and 81901101 [to PW]), and the Special Fund for Military Health committee (Grant No. 21BJZ21 [to HYao]). Data collection and sharing for this project were funded by the National Natural Science Foundation of China (Grant Nos. 61633018 [to YH], 81571062 [to YL], 81400890 [to DW], and 81471120 [to XZ]). Data collection and sharing for this project were funded by the ADNI (National Institutes of Health Grant No. U01 AG024904) and DOD ADNI (Department of Defense Award Number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Co. Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EUROIMMUN; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development, LLC.; Lumosity; Lundbeck; Merck & Co. Inc.; Meso Scale Diagnostics, LLC.; NeuroRx; Neurotrack Technologies, Inc; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research provides funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (http://www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. We thank Drs. Rhoda E. and Edmund F. Perozzi for their very extensive English language and editing assistance. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report. A complete listing of the ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf, The authors report no biomedical financial interests or potential conflicts of interest. Funding Information: This work was partially supported by the Fundamental Research Funds for the Central Universities (Grant No. 2021XD-A03-1 [to YL]), the Beijing Natural Science Funds for Distinguished Young Scholars (Grant No. JQ20036 [to YL]), the National Natural Science Foundation of China (Grant Nos. 81871438 [to YL] , 82172018 [to YL] , and 81901101 [to PW] ), and the Special Fund for Military Health committee (Grant No. 21BJZ21 [to HYao]). Data collection and sharing for this project were funded by the National Natural Science Foundation of China (Grant Nos. 61633018 [to YH] , 81571062 [to YL] , 81400890 [to DW] , and 81471120 [to XZ] ). Publisher Copyright: © 2022 Society of Biological Psychiatry
PY - 2022
Y1 - 2022
N2 - Background: Alzheimer's disease (AD) is a neurodegenerative disorder with significant heterogeneity. Different AD phenotypes may be associated with specific brain network changes. Uncovering disease heterogeneity by using functional networks could provide insights into precise diagnoses. Methods: We investigated the subtypes of AD using nonnegative matrix factorization clustering on the previously identified 216 resting-state functional connectivities that differed between AD and normal control subjects. We conducted the analysis using a discovery dataset (n = 809) and a validated dataset (n = 291). Next, we grouped individuals with mild cognitive impairment according to the model obtained in the AD groups. Finally, the clinical measures and brain structural characteristics were compared among the subtypes to assess their relationship with differences in the functional network. Results: Individuals with AD were clustered into 4 subtypes reproducibly, which included those with 1) diffuse and mild functional connectivity disruption (subtype 1), 2) predominantly decreased connectivity in the default mode network accompanied by an increase in the prefrontal circuit (subtype 2), 3) predominantly decreased connectivity in the anterior cingulate cortex accompanied by an increase in prefrontal cortex connectivity (subtype 3), and 4) predominantly decreased connectivity in the basal ganglia accompanied by an increase in prefrontal cortex connectivity (subtype 4). In addition to these differences in functional connectivity, differences between the AD subtypes were found in cognition, structural measures, and cognitive decline patterns. Conclusions: These comprehensive results offer new insights that may advance precision medicine for AD and facilitate strategies for future clinical trials.
AB - Background: Alzheimer's disease (AD) is a neurodegenerative disorder with significant heterogeneity. Different AD phenotypes may be associated with specific brain network changes. Uncovering disease heterogeneity by using functional networks could provide insights into precise diagnoses. Methods: We investigated the subtypes of AD using nonnegative matrix factorization clustering on the previously identified 216 resting-state functional connectivities that differed between AD and normal control subjects. We conducted the analysis using a discovery dataset (n = 809) and a validated dataset (n = 291). Next, we grouped individuals with mild cognitive impairment according to the model obtained in the AD groups. Finally, the clinical measures and brain structural characteristics were compared among the subtypes to assess their relationship with differences in the functional network. Results: Individuals with AD were clustered into 4 subtypes reproducibly, which included those with 1) diffuse and mild functional connectivity disruption (subtype 1), 2) predominantly decreased connectivity in the default mode network accompanied by an increase in the prefrontal circuit (subtype 2), 3) predominantly decreased connectivity in the anterior cingulate cortex accompanied by an increase in prefrontal cortex connectivity (subtype 3), and 4) predominantly decreased connectivity in the basal ganglia accompanied by an increase in prefrontal cortex connectivity (subtype 4). In addition to these differences in functional connectivity, differences between the AD subtypes were found in cognition, structural measures, and cognitive decline patterns. Conclusions: These comprehensive results offer new insights that may advance precision medicine for AD and facilitate strategies for future clinical trials.
KW - Alzheimer's disease
KW - Functional connectivity
KW - Heterogeneity
KW - Mild cognitive impairment
KW - Nonnegative matrix factorization
KW - Subtype
UR - http://www.scopus.com/inward/record.url?scp=85136137062&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.biopsych.2022.06.019
DO - https://doi.org/10.1016/j.biopsych.2022.06.019
M3 - Article
C2 - 36137824
SN - 0006-3223
JO - Biological Psychiatry
JF - Biological Psychiatry
ER -