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Four distinct subtypes of Alzheimer's disease based on resting-state connectivity biomarkers

  • Author Footnotes
    † These authors contributed equally to this work.
    Pindong Chen
    Footnotes
    † These authors contributed equally to this work.
    Affiliations
    Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;

    School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China;
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  • Author Footnotes
    † These authors contributed equally to this work.
    Hongxiang Yao
    Footnotes
    † These authors contributed equally to this work.
    Affiliations
    Department of Radiology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China;
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  • Betty M. Tijms
    Affiliations
    Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC - Location VUmc, The Netherlands
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  • Pan Wang
    Affiliations
    Department of Neurology, Tianjin Huanhu Hospital Tianjin University, Tianjin, China;
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  • Dawei Wang
    Affiliations
    Department of Radiology, Qilu Hospital of Shandong University, Ji'nan, China;
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  • Chengyuan Song
    Affiliations
    Department of Neurology, Qilu Hospital of Shandong University, Ji'nan, China;
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  • Hongwei Yang
    Affiliations
    Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China;
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  • Zengqiang Zhang
    Affiliations
    Branch of Chinese PLA General Hospital, Sanya, China;
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  • Kun Zhao
    Affiliations
    Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing, China
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  • Yida Qu
    Affiliations
    Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;

    School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China;
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  • Xiaopeng Kang
    Affiliations
    Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;

    School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China;
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  • Kai Du
    Affiliations
    Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;

    School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China;
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  • Lingzhong Fan
    Affiliations
    Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;
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  • Tong Han
    Affiliations
    Department of Radiology, Tianjin Huanhu Hospital, Tianjin, China;
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  • Chunshui Yu
    Affiliations
    Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China;
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  • Xi Zhang
    Affiliations
    Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China;
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  • Tianzi Jiang
    Affiliations
    Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;

    School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China;
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  • Yuying Zhou
    Affiliations
    Department of Radiology, Qilu Hospital of Shandong University, Ji'nan, China;
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  • Jie Lu
    Affiliations
    Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China;
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  • Ying Han
    Affiliations
    Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China;

    Beijing Institute of Geriatrics, Beijing, China;

    National Clinical Research Center for Geriatric Disorders, Beijing, China;
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  • Bing Liu
    Affiliations
    State Key Laboratory of Cognition Neuroscience & Learning, Beijing Normal University, Beijing, China;
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  • Bo Zhou
    Correspondence
    Correspondence to: Bo Zhou, MD, , Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China
    Affiliations
    Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China;
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  • Yong Liu
    Correspondence
    Correspondence to: Yong Liu, PhD, , School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China
    Affiliations
    Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;

    School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China;

    School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
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  • for theAlzheimer's Disease Neuroimaging Initiative
    Author Footnotes
    $ Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (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
  • Author Footnotes
    † These authors contributed equally to this work.
    $ Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (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

      Abstract

      Background

      Alzheimer's disease (AD) is a neurodegenerative disorder with significant heterogeneity. Different AD phenotypes may be associated with specific changed brain networks. Uncovering disease heterogeneity by using functional network could provide insights into precise diagnoses.

      Methods

      We investigated the subtypes of AD using non-negative matrix factorization clustering on the previously identified 216 resting-state functional connectivities that differed between AD and normal controls. 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 four subtypes reproducibly, which included one with diffuse and mild functional connectivity disruption (subtype 1), another with predominantly decreased connectivity in the default mode network accompanied by an increase in the prefrontal circuit (subtype 2), a third with predominantly decreased connectivity in the anterior cingulate cortex accompanied by an increase in the prefrontal cortex (subtype 3), and a final one with predominantly decreased connectivity in the basal ganglia accompanied by an increase in the prefrontal cortex (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.

      Keywords

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