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Research ArticleResearch Article: Methods/New Tools, Novel Tools and Methods

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, Anthony R. McIntosh, Demian Battaglia and Viktor Jirsa
eNeuro 27 May 2021, 8 (4) ENEURO.0475-20.2021; DOI: https://doi.org/10.1523/ENEURO.0475-20.2021
Lucas Arbabyazd
1Institut de Neurosciences des Systèmes, Université Aix-Marseille, Institut ational de la Santé et de la Recherche Médicale Unité Mixte de Recherche 1106, Marseille F-13005, 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, Sankt Augustin 53754, Germany
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Petra Ritter
4Brain Simulation Section, Department of Neurology, Charité University Medicine Berlin and Berlin Institute of Health, Berlin 10117, Germany
5Bernstein center for Computational Neuroscience Berlin, Berlin 10117, Germany
6Einstein center for Neuroscience Berlin, Berlin 10117, Germany
7Einstein center Digital Future, Berlin 10117, Germany
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Anthony R. McIntosh
2Rotman Research Institute, Baycrest Centre, Toronto, Ontario M6A 2E1, Canada
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  • ORCID record for Anthony R. McIntosh
Demian Battaglia
1Institut de Neurosciences des Systèmes, Université Aix-Marseille, Institut ational de la Santé et de la Recherche Médicale Unité Mixte de Recherche 1106, Marseille F-13005, France
8University of Strasbourg Institute for Advanced Studies (USIAS), Strasbourg 67000, France
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Viktor Jirsa
1Institut de Neurosciences des Systèmes, Université Aix-Marseille, Institut ational de la Santé et de la Recherche Médicale Unité Mixte de Recherche 1106, Marseille F-13005, France
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Abstract

Large neuroimaging datasets, including information about structural connectivity (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 functional magnetic resonance imaging (rsfMRI) 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 Alzheimer’s Disease Neuroimaging Initiative (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.

  • aging
  • Alzheimer’s diseases
  • connectome
  • dataset completion
  • fMRI
  • whole-brain modelling

Footnotes

  • The authors declare no competing financial interests.

  • 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). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health. The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. D.B. was supported by the European Union Innovative Training Network “i-CONN” (H2020 ITN 859937); V.J. was supported by the European Union’s Horizon 2020 Framework Program for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2) and H2020 Research and Innovation Action grants VirtualBrainCloud; and A.R.M. was supported by the Brightfocus Foundation ADR Grant program, Grant Reference Number A2017286S.

  • ↵* L.A. is first author.

  • ↵# D.B. and V.J. shared last authorship.

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.

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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, Anthony R. McIntosh, Demian Battaglia, Viktor Jirsa
eNeuro 27 May 2021, 8 (4) ENEURO.0475-20.2021; DOI: 10.1523/ENEURO.0475-20.2021

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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, Anthony R. McIntosh, Demian Battaglia, Viktor Jirsa
eNeuro 27 May 2021, 8 (4) ENEURO.0475-20.2021; DOI: 10.1523/ENEURO.0475-20.2021
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Keywords

  • aging
  • Alzheimer’s diseases
  • connectome
  • dataset completion
  • fMRI
  • whole-brain modelling

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