Stage-dependent differential influence of metabolic and structural networks on memory across Alzheimer's disease continuum
- National Neuroscience Institute, Singapore
- National University of Singapore, Singapore
- McGill University, Canada
Abstract
Background: Large-scale neuronal network breakdown underlies memory impairment in Alzheimer's disease (AD). However, the differential trajectories of the relationships between network organization and memory across pathology and cognitive stages in AD remain elusive. We determined whether and how the influences of individual-level structural and metabolic covariance network integrity on memory varied with amyloid pathology across clinical stages without assuming a constant relationship.
Methods: 708 participants from the Alzheimer's Disease Neuroimaging Initiative were studied. Individual-level structural and metabolic covariance scores in higher-level cognitive and hippocampal networks were derived from magnetic resonance imaging and [18F]fluorodeoxyglucose positron emission tomography using seed-based partial least square analyses. The non-linear associations between network scores and memory across cognitive stages in each pathology group were examined using sparse varying coefficient modelling.
Results: We showed that the associations of memory with structural and metabolic networks in the hippocampal and default mode regions exhibited pathology-dependent differential trajectories across cognitive stages using sparse varying coefficient modelling. In amyloid pathology group, there was an early influence of hippocampal structural network deterioration on memory impairment in the preclinical stage, and a biphasic influence of the angular gyrus-seeded default mode metabolic network on memory in both preclinical and dementia stages. In non- amyloid pathology groups, in contrast, the trajectory of the hippocampus-memory association was opposite and weaker overall, while no metabolism covariance networks were related to memory. Key findings were replicated in a larger cohort of 1280 participants.
Conclusions: Our findings highlight potential windows of early intervention targeting network breakdown at the preclinical AD stage.
Funding: Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). We also acknowledge the funding support from the Duke-NUS/Khoo Bridge Funding Award (KBrFA/2019-0020) and NMRC Open Fund Large Collaborative Grant (OFLCG09May0035).
Data availability
ADNI data used in this manuscript are publicly available at adni.loni.usc.edu, subject to adherence to the ADNI Data Use Agreement and publications' policies (https://ida.loni.usc.edu/collaboration/access/appLicense.jsp). Guidelines to apply for data access can be found in https://adni.loni.usc.edu/data-samples/access-data/#access_data. Codes used in this manuscript are available at https://github.com/hzlab/2021Qian_ADNI_FDG . The repository is currently private, but will be made public after manuscript acceptance for publication.
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Alzheimer's Disease Neuroimaging Initiative (ADNI)https://ida.loni.usc.edu/login.jsp.
Article and author information
Author details
Funding
Alzheimer's Disease Neuroimaging Initiative (National Institutes of Health Grant U01 AG024904)
- Kok Pin Ng
- Xing Qian
- Kwun Kei Ng
- Fang Ji
- Pedro Rosa-Neto
- Serge Gauthier
- Nagaendran Kandiah
- Juan Helen Zhou
Duke-NUS Medical School (Duke-NUS/Khoo Bridge Funding Award (KBrFA/2019-0020))
- Juan Helen Zhou
National Medical Research Council (NMRC Open Fund Large Collaborative Grant (OFLCG09May0035))
- Juan Helen Zhou
DoD Alzheimer's Disease Neuroimaging Initiative (Department of Defense award number W81XWH-12-2-0012)
- Kok Pin Ng
- Xing Qian
- Kwun Kei Ng
- Fang Ji
- Pedro Rosa-Neto
- Serge Gauthier
- Nagaendran Kandiah
- Juan Helen Zhou
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Jeannie Chin, Baylor College of Medicine, United States
Ethics
Human subjects: Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer's disease (AD).The ADNI study was approved by the Institutional Review Boards of all of the participating institutions and informed written consent was obtained from all participants at eachsite.
Version history
- Received: February 9, 2022
- Preprint posted: March 2, 2022 (view preprint)
- Accepted: September 2, 2022
- Accepted Manuscript published: September 2, 2022 (version 1)
- Version of Record published: September 15, 2022 (version 2)
- Version of Record updated: September 20, 2022 (version 3)
- Version of Record updated: April 3, 2023 (version 4)
Copyright
© 2022, Ng et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Stage-dependent differential influence of metabolic and structural networks on memory across Alzheimer's disease continuum
eLife 11:e77745.
https://doi.org/10.7554/eLife.77745
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