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Virtual Connectomic Datasets in Alzheimer’s Disease and Aging Using Whole-Brain Network Dynamics Modelling

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, howe...

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Autores principales: Arbabyazd, Lucas, Shen, Kelly, Wang, Zheng, Hofmann-Apitius, Martin, Ritter, Petra, McIntosh, Anthony R., Battaglia, Demian, Jirsa, Viktor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society for Neuroscience 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8260273/
https://www.ncbi.nlm.nih.gov/pubmed/34045210
http://dx.doi.org/10.1523/ENEURO.0475-20.2021
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author Arbabyazd, Lucas
Shen, Kelly
Wang, Zheng
Hofmann-Apitius, Martin
Ritter, Petra
McIntosh, Anthony R.
Battaglia, Demian
Jirsa, Viktor
author_facet Arbabyazd, Lucas
Shen, Kelly
Wang, Zheng
Hofmann-Apitius, Martin
Ritter, Petra
McIntosh, Anthony R.
Battaglia, Demian
Jirsa, Viktor
author_sort Arbabyazd, Lucas
collection PubMed
description 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.
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spelling pubmed-82602732021-07-08 Virtual Connectomic Datasets in Alzheimer’s Disease and Aging Using Whole-Brain Network Dynamics Modelling Arbabyazd, Lucas Shen, Kelly Wang, Zheng Hofmann-Apitius, Martin Ritter, Petra McIntosh, Anthony R. Battaglia, Demian Jirsa, Viktor eNeuro Research Article: Methods/New Tools 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. Society for Neuroscience 2021-07-03 /pmc/articles/PMC8260273/ /pubmed/34045210 http://dx.doi.org/10.1523/ENEURO.0475-20.2021 Text en Copyright © 2021 Arbabyazd et al. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.
spellingShingle Research Article: Methods/New Tools
Arbabyazd, Lucas
Shen, Kelly
Wang, Zheng
Hofmann-Apitius, Martin
Ritter, Petra
McIntosh, Anthony R.
Battaglia, Demian
Jirsa, Viktor
Virtual Connectomic Datasets in Alzheimer’s Disease and Aging Using Whole-Brain Network Dynamics Modelling
title Virtual Connectomic Datasets in Alzheimer’s Disease and Aging Using Whole-Brain Network Dynamics Modelling
title_full Virtual Connectomic Datasets in Alzheimer’s Disease and Aging Using Whole-Brain Network Dynamics Modelling
title_fullStr Virtual Connectomic Datasets in Alzheimer’s Disease and Aging Using Whole-Brain Network Dynamics Modelling
title_full_unstemmed Virtual Connectomic Datasets in Alzheimer’s Disease and Aging Using Whole-Brain Network Dynamics Modelling
title_short Virtual Connectomic Datasets in Alzheimer’s Disease and Aging Using Whole-Brain Network Dynamics Modelling
title_sort virtual connectomic datasets in alzheimer’s disease and aging using whole-brain network dynamics modelling
topic Research Article: Methods/New Tools
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8260273/
https://www.ncbi.nlm.nih.gov/pubmed/34045210
http://dx.doi.org/10.1523/ENEURO.0475-20.2021
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