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A computational model of neurodegeneration in Alzheimer’s disease

Author

Listed:
  • D. Jones

    (Mayo Clinic
    Mayo Clinic)

  • V. Lowe

    (Mayo Clinic)

  • J. Graff-Radford

    (Mayo Clinic)

  • H. Botha

    (Mayo Clinic)

  • L. Barnard

    (Mayo Clinic)

  • D. Wiepert

    (Mayo Clinic)

  • M. C. Murphy

    (Mayo Clinic)

  • M. Murray

    (Mayo Clinic)

  • M. Senjem

    (Mayo Clinic)

  • J. Gunter

    (Mayo Clinic)

  • H. Wiste

    (Mayo Clinic)

  • B. Boeve

    (Mayo Clinic)

  • D. Knopman

    (Mayo Clinic)

  • R. Petersen

    (Mayo Clinic)

  • C. Jack

    (Mayo Clinic)

Abstract

Disruption of mental functions in Alzheimer’s disease (AD) and related disorders is accompanied by selective degeneration of brain regions. These regions comprise large-scale ensembles of cells organized into systems for mental functioning, however the relationship between clinical symptoms of dementia, patterns of neurodegeneration, and functional systems is not clear. Here we present a model of the association between dementia symptoms and degenerative brain anatomy using F18-fluorodeoxyglucose PET and dimensionality reduction techniques in two cohorts of patients with AD. This reflected a simple information processing-based functional description of macroscale brain anatomy which we link to AD physiology, functional networks, and mental abilities. We further apply the model to normal aging and seven degenerative diseases of mental functions. We propose a global information processing model for mental functions that links neuroanatomy, cognitive neuroscience and clinical neurology.

Suggested Citation

  • D. Jones & V. Lowe & J. Graff-Radford & H. Botha & L. Barnard & D. Wiepert & M. C. Murphy & M. Murray & M. Senjem & J. Gunter & H. Wiste & B. Boeve & D. Knopman & R. Petersen & C. Jack, 2022. "A computational model of neurodegeneration in Alzheimer’s disease," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29047-4
    DOI: 10.1038/s41467-022-29047-4
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    References listed on IDEAS

    as
    1. Timothy N Rubin & Oluwasanmi Koyejo & Krzysztof J Gorgolewski & Michael N Jones & Russell A Poldrack & Tal Yarkoni, 2017. "Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition," PLOS Computational Biology, Public Library of Science, vol. 13(10), pages 1-24, October.
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    3. Andrew C. Murphy & Maxwell A. Bertolero & Lia Papadopoulos & David M. Lydon-Staley & Danielle S. Bassett, 2020. "Multimodal network dynamics underpinning working memory," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    4. Karolina Finc & Kamil Bonna & Xiaosong He & David M. Lydon-Staley & Simone Kühn & Włodzisław Duch & Danielle S. Bassett, 2020. "Author Correction: Dynamic reconfiguration of functional brain networks during working memory training," Nature Communications, Nature, vol. 11(1), pages 1-1, December.
    5. Karolina Finc & Kamil Bonna & Xiaosong He & David M. Lydon-Staley & Simone Kühn & Włodzisław Duch & Danielle S. Bassett, 2020. "Dynamic reconfiguration of functional brain networks during working memory training," Nature Communications, Nature, vol. 11(1), pages 1-15, December.
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    Cited by:

    1. David J. Whiteside & Negin Holland & Kamen A. Tsvetanov & Elijah Mak & Maura Malpetti & George Savulich & P. Simon Jones & Michelle Naessens & Matthew A. Rouse & Tim D. Fryer & Young T. Hong & Frankli, 2023. "Synaptic density affects clinical severity via network dysfunction in syndromes associated with frontotemporal lobar degeneration," Nature Communications, Nature, vol. 14(1), pages 1-13, December.

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