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Network efficiency predicts resilience to cognitive decline in elderly at risk for Alzheimer’s

Florian U. Fischer, Dominik Wolf, Andreas Fellgiebel, for the Alzheimer’s Disease Neuroimaging Initiative
doi: https://doi.org/10.1101/2020.02.14.949826
Florian U. Fischer
aUniversity Medical Center Mainz, Mainz, Germany, Department of Psychiatry and Psychotherapy, Untere Zahlbacher Str. 8, 55131 Mainz
bCenter for Mental Health in Old Age, Landeskrankenhaus (AöR), Mainz, Germany, Hartmühlenweg 2-4, 55122 Mainz
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  • For correspondence: florian.fischer@unimedizin-mainz.de
Dominik Wolf
aUniversity Medical Center Mainz, Mainz, Germany, Department of Psychiatry and Psychotherapy, Untere Zahlbacher Str. 8, 55131 Mainz
bCenter for Mental Health in Old Age, Landeskrankenhaus (AöR), Mainz, Germany, Hartmühlenweg 2-4, 55122 Mainz
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Andreas Fellgiebel
aUniversity Medical Center Mainz, Mainz, Germany, Department of Psychiatry and Psychotherapy, Untere Zahlbacher Str. 8, 55131 Mainz
bCenter for Mental Health in Old Age, Landeskrankenhaus (AöR), Mainz, Germany, Hartmühlenweg 2-4, 55122 Mainz
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Abstract

To determine whether white matter network efficiency (WMNE) may be a surrogate marker of the physiological basis of resilience to cognitive decline in elderly persons without dementia and age and AD-related cerebral pathology, we quantified WMNE from baseline MRI scans and investigated its association with longitudinal neuropsychological assessments independent of baseline amyloid, tau and white matter hyperintensity volume. 85 cognitively normal elderly subjects and patients with mild cognitive impairment (MCI) with baseline diffusion imaging, CSF specimens, AV45-PET and longitudinal cognitive assessments were included. WMNE was calculated from reconstructed cerebral white matter networks for each individual. Mixed linear effects models were estimated to investigate the association of higher resilience to cognitive decline with higher WMNE and the modulation of this association by increased cerebral amyloid, CSF tau or WMHV. For the majority of cognitive outcome measures, higher WMNE was associated with higher resilience to cognitive decline independently of pathology measures (beta: .074 – .098; p: .011 – .039). Additionally, WMNE was consistently associated with higher resilience to cognitive decline in subjects with higher cerebral amyloid burden (beta: .024 – .276; p: .000 – .036) and with lower CSF tau (beta: −.030 – −.074; p: .015 – .002) across all cognitive outcome measures. The results of this study indicate that WMNE in particular and possibly white matter organization in general may be worthy targets of investigation to provide measures quantifying a patient’s resilience to cognitive decline and thus provide an individual prognosis.

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 analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

  • Conflicts of interest Author Florian U. Fischer, Author Dominik Wolf and Author Andreas Fellgiebel declare that they have no conflict of interest.

  • http://adni.loni.usc.edu/

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 14, 2020.
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Network efficiency predicts resilience to cognitive decline in elderly at risk for Alzheimer’s
Florian U. Fischer, Dominik Wolf, Andreas Fellgiebel, for the Alzheimer’s Disease Neuroimaging Initiative
bioRxiv 2020.02.14.949826; doi: https://doi.org/10.1101/2020.02.14.949826
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Network efficiency predicts resilience to cognitive decline in elderly at risk for Alzheimer’s
Florian U. Fischer, Dominik Wolf, Andreas Fellgiebel, for the Alzheimer’s Disease Neuroimaging Initiative
bioRxiv 2020.02.14.949826; doi: https://doi.org/10.1101/2020.02.14.949826

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