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Author Notes:

Corresponding author: Michaël E. Belloy, Email:michael.belloy@hotmail.be.

M.E.B. wrote manuscript, designed research, performed research, performed analysis, and designed analysis tools.

D.S. contributed valuable comments to research design and data analysis.

A.A., A.K., and S.R. contributed material and analysis tools.

A.V.d.l., S.D.K., G.A.K. and M.V. designed research and provided input on data analysis.

The authors declare no competing interests.

The authors thank Behanz Yousefi for her valuable comments and helpful discussions.

We also thank Frank de Vos for providing feedback on how to implement the presented elastic net regression models.

Subjects:

Research Funding:

This work was supported by the interdisciplinary PhD grant (ID) BOF DOCPRO 2014 (granted to M.V.) and further partially supported by funding received from: the European Union’s Seventh Framework Programme (FP7/2007–2013; INMiND) (grant agreement 278850, granted to AV.d.L.), the molecular Imaging of Brain Pathohysiology (BRAINPATH) and the Marie Curie Actions-Industry-Academia Partnerships and Pathways (IAPP) program (grant agreement 612360, granted to A.V.d.L.), Flagship ERA-NET (FLAG-ERA) FUSIMICE (grant agreement G. 0D7615N), stichting Alzheimer onderzoek (SAO-FRA) (grant agreement 13026, granted to A.V.d.L.), the Flemish Impulse funding for heavy scientific equipment (granted to A.V.d.L.), the Fund for Scientific Research Flanders (FWO) (grant agreements G.057615N and G.067515N), the National Institutes of Health (NIH) (grant agreements R01MH111416-01 and R01NS078095), the National Science Foundation (NSF) (grant agreement BCS INSPIRE 1533260), and the ISMRM Research Exchange Program (granted to M.E.B.).

Keywords:

  • Science & Technology
  • Multidisciplinary Sciences
  • Science & Technology - Other Topics
  • RESTING-STATE FMRI
  • FUNCTIONAL CONNECTIVITY NETWORKS
  • FREQUENCY BOLD FLUCTUATIONS
  • AMYLOID CASCADE HYPOTHESIS
  • MILD COGNITIVE IMPAIRMENT
  • TRANSGENIC MOUSE MODEL
  • DEFAULT-MODE
  • GLOBAL SIGNAL
  • INDIVIDUAL CLASSIFICATION
  • BRAIN CONNECTIVITY

Quasi-Periodic Patterns of Neural Activity improve Classification of Alzheimer's Disease in Mice

Tools:

Journal Title:

Scientific Reports

Volume:

Volume 8, Number 1

Publisher:

, Pages 10024-10024

Type of Work:

Article | Final Publisher PDF

Abstract:

Resting state (rs)fMRI allows measurement of brain functional connectivity and has identified default mode (DMN) and task positive (TPN) network disruptions as promising biomarkers for Alzheimer's disease (AD). Quasi-periodic patterns (QPPs) of neural activity describe recurring spatiotemporal patterns that display DMN with TPN anti-correlation. We reasoned that QPPs could provide new insights into AD network dysfunction and improve disease diagnosis. We therefore used rsfMRI to investigate QPPs in old TG2576 mice, a model of amyloidosis, and age-matched controls. Multiple QPPs were determined and compared across groups. Using linear regression, we removed their contribution from the functional scans and assessed how they reflected functional connectivity. Lastly, we used elastic net regression to determine if QPPs improved disease classification. We present three prominent findings: (1) Compared to controls, TG2576 mice were marked by opposing neural dynamics in which DMN areas were anti-correlated and displayed diminished anti-correlation with the TPN. (2) QPPs reflected lowered DMN functional connectivity in TG2576 mice and revealed significantly decreased DMN-TPN anti-correlations. (3) QPP-derived measures significantly improved classification compared to conventional functional connectivity measures. Altogether, our findings provide insight into the neural dynamics of aberrant network connectivity in AD and indicate that QPPs might serve as a translational diagnostic tool.

Copyright information:

© 2018 The Author(s).

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
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