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Virtual connectomic datasets in Alzheimer’s Disease and aging using whole-brain network dynamics modelling

Lucas Arbabyazd, Kelly Shen, Zheng Wang, Martin Hofmann-Apitius, Petra Ritter, The Alzheimer’s Disease Neuroimaging Initiative, Anthony R. McIntosh, Demian Battaglia, Viktor Jirsa
doi: https://doi.org/10.1101/2020.01.18.911248
Lucas Arbabyazd
1Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005 Marseille, France
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Kelly Shen
2Rotman Research Institute, Baycrest Centre, Toronto, Ontario, M6A 2E1, Canada
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Zheng Wang
2Rotman Research Institute, Baycrest Centre, Toronto, Ontario, M6A 2E1, Canada
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Martin Hofmann-Apitius
3Fraunhofer Institute for Algorithms and Scientific Computing, 53754 Sankt Augustin, Germany
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Petra Ritter
4Brain Simulation Section, Department of Neurology, Charité University Medicine Berlin & Berlin Institute of Health, Germany
5Bernstein Center for Computational Neuroscience Berlin, Germany
6Einstein Center for Neuroscience Berlin, Charitéplatz 1, 10117 Berlin
7Einstein Center Digital Future, Wilhelmstraße 67, 10117 Berlin
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Anthony R. McIntosh
2Rotman Research Institute, Baycrest Centre, Toronto, Ontario, M6A 2E1, Canada
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Demian Battaglia
1Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005 Marseille, France
5Bernstein Center for Computational Neuroscience Berlin, Germany
8University of Strasbourg Institute for Advanced Studies (USIAS), 67000 Strasbourg, France
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  • For correspondence: demian.battaglia@univ-amu.fr
Viktor Jirsa
1Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005 Marseille, France
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  • For correspondence: demian.battaglia@univ-amu.fr
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Abstract

Large neuroimaging datasets, including information about structural (SC) and functional connectivity (FC), play an increasingly important role in clinical research, where they guide the design of algorithms for automated stratification, diagnosis or prediction. A major obstacle is, however, the problem of missing features (e.g., lack of concurrent DTI SC and resting-state fMRI FC measurements for many of the subjects).

We propose here to address the missing connectivity features problem by introducing strategies based on computational whole-brain network modeling. Using two datasets, the ADNI dataset and a healthy aging dataset, for proof-of-concept, we demonstrate the feasibility of virtual data completion (i.e., inferring “virtual FC” from empirical SC or “virtual SC” from empirical FC), by using self-consistent simulations of linear and nonlinear brain network models. Furthermore, by performing machine learning classification (to separate age classes or control from patient subjects) we show that algorithms trained on virtual connectomes achieve discrimination performance comparable to when trained on actual empirical data; similarly, algorithms trained on virtual connectomes can be used to successfully classify novel empirical connectomes. Completion algorithms can be combined and reiterated to generate realistic surrogate connectivity matrices in arbitrarily large number, opening the way to the generation of virtual connectomic datasets with network connectivity information comparable to the one of the original data.

Significance statement Personalized information on anatomical connectivity (“structural connectivity”, SC) or coordinated resting state activation patterns (“functional connectivity’, FC) is a source of powerful neuromarkers to detect and track the development of neurodegenerative diseases. However, there are often “gaps” in the available information, with only SC (or FC) being known but not FC (or SC). Exploiting whole-brain modelling, we show that gap in databases can be filled by inferring the other connectome through computational simulations. The generated virtual connectomic data carry information analogous to the one of empirical connectomes, so that machine learning algorithms can be trained on them. This opens the way to the release in the future of cohorts of “virtual patients”, complementing traditional datasets in data-driven predictive medicine.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵+ First author

  • ↵§ shared last authors

  • ↵* 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

  • New version after manuscript revision. Added a complementary healthy aging dataset provided by Petra Ritter (added coauthor) for confirming robustness of analyses.

  • https://github.com/FunDyn/VirtualCohorts

Copyright 
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-ND 4.0 International license.
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Posted March 08, 2021.
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Virtual connectomic datasets in Alzheimer’s Disease and aging using whole-brain network dynamics modelling
Lucas Arbabyazd, Kelly Shen, Zheng Wang, Martin Hofmann-Apitius, Petra Ritter, The Alzheimer’s Disease Neuroimaging Initiative, Anthony R. McIntosh, Demian Battaglia, Viktor Jirsa
bioRxiv 2020.01.18.911248; doi: https://doi.org/10.1101/2020.01.18.911248
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Virtual connectomic datasets in Alzheimer’s Disease and aging using whole-brain network dynamics modelling
Lucas Arbabyazd, Kelly Shen, Zheng Wang, Martin Hofmann-Apitius, Petra Ritter, The Alzheimer’s Disease Neuroimaging Initiative, Anthony R. McIntosh, Demian Battaglia, Viktor Jirsa
bioRxiv 2020.01.18.911248; doi: https://doi.org/10.1101/2020.01.18.911248

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