Literature DB >> 25837600

An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data.

Michael Schirner1, Simon Rothmeier1, Viktor K Jirsa2, Anthony Randal McIntosh3, Petra Ritter4.   

Abstract

Large amounts of multimodal neuroimaging data are acquired every year worldwide. In order to extract high-dimensional information for computational neuroscience applications standardized data fusion and efficient reduction into integrative data structures are required. Such self-consistent multimodal data sets can be used for computational brain modeling to constrain models with individual measurable features of the brain, such as done with The Virtual Brain (TVB). TVB is a simulation platform that uses empirical structural and functional data to build full brain models of individual humans. For convenient model construction, we developed a processing pipeline for structural, functional and diffusion-weighted magnetic resonance imaging (MRI) and optionally electroencephalography (EEG) data. The pipeline combines several state-of-the-art neuroinformatics tools to generate subject-specific cortical and subcortical parcellations, surface-tessellations, structural and functional connectomes, lead field matrices, electrical source activity estimates and region-wise aggregated blood oxygen level dependent (BOLD) functional MRI (fMRI) time-series. The output files of the pipeline can be directly uploaded to TVB to create and simulate individualized large-scale network models that incorporate intra- and intercortical interaction on the basis of cortical surface triangulations and white matter tractograpy. We detail the pitfalls of the individual processing streams and discuss ways of validation. With the pipeline we also introduce novel ways of estimating the transmission strengths of fiber tracts in whole-brain structural connectivity (SC) networks and compare the outcomes of different tractography or parcellation approaches. We tested the functionality of the pipeline on 50 multimodal data sets. In order to quantify the robustness of the connectome extraction part of the pipeline we computed several metrics that quantify its rescan reliability and compared them to other tractography approaches. Together with the pipeline we present several principles to guide future efforts to standardize brain model construction. The code of the pipeline and the fully processed data sets are made available to the public via The Virtual Brain website (thevirtualbrain.org) and via github (https://github.com/BrainModes/TVB-empirical-data-pipeline). Furthermore, the pipeline can be directly used with High Performance Computing (HPC) resources on the Neuroscience Gateway Portal (http://www.nsgportal.org) through a convenient web-interface.
Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Computational modeling; Connectome; Diffusion MRI; Multimodal imaging; The Virtual Brain; Tractography

Mesh:

Year:  2015        PMID: 25837600     DOI: 10.1016/j.neuroimage.2015.03.055

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  26 in total

1.  Mapping complementary features of cross-species structural connectivity to construct realistic "Virtual Brains".

Authors:  Gleb Bezgin; Ana Solodkin; Rembrandt Bakker; Petra Ritter; Anthony R McIntosh
Journal:  Hum Brain Mapp       Date:  2017-01-05       Impact factor: 5.038

2.  Metastable neural dynamics in Alzheimer's disease are disrupted by lesions to the structural connectome.

Authors:  Thomas H Alderson; Arun L W Bokde; J A Scott Kelso; Liam Maguire; Damien Coyle
Journal:  Neuroimage       Date:  2018-08-18       Impact factor: 6.556

3.  Structural architecture supports functional organization in the human aging brain at a regionwise and network level.

Authors:  Joelle Zimmermann; Petra Ritter; Kelly Shen; Simon Rothmeier; Michael Schirner; Anthony R McIntosh
Journal:  Hum Brain Mapp       Date:  2016-04-04       Impact factor: 5.038

4.  Inferring multi-scale neural mechanisms with brain network modelling.

Authors:  Michael Schirner; Anthony Randal McIntosh; Viktor Jirsa; Gustavo Deco; Petra Ritter
Journal:  Elife       Date:  2018-01-08       Impact factor: 8.140

Review 5.  From Maps to Multi-dimensional Network Mechanisms of Mental Disorders.

Authors:  Urs Braun; Axel Schaefer; Richard F Betzel; Heike Tost; Andreas Meyer-Lindenberg; Danielle S Bassett
Journal:  Neuron       Date:  2018-01-03       Impact factor: 17.173

6.  Analytical Operations Relate Structural and Functional Connectivity in the Brain.

Authors:  Maria Luisa Saggio; Petra Ritter; Viktor K Jirsa
Journal:  PLoS One       Date:  2016-08-18       Impact factor: 3.240

7.  Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox.

Authors:  Andre Santos Ribeiro; Luis Miguel Lacerda; Hugo Alexandre Ferreira
Journal:  PeerJ       Date:  2015-07-14       Impact factor: 2.984

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

Authors:  Lucas Arbabyazd; Kelly Shen; Zheng Wang; Martin Hofmann-Apitius; Petra Ritter; Anthony R McIntosh; Demian Battaglia; Viktor Jirsa
Journal:  eNeuro       Date:  2021-07-06

Review 9.  Resting-State Functional MRI: Everything That Nonexperts Have Always Wanted to Know.

Authors:  H Lv; Z Wang; E Tong; L M Williams; G Zaharchuk; M Zeineh; A N Goldstein-Piekarski; T M Ball; C Liao; M Wintermark
Journal:  AJNR Am J Neuroradiol       Date:  2018-01-18       Impact factor: 3.825

10.  Reconfiguration of Directed Functional Connectivity Among Neurocognitive Networks with Aging: Considering the Role of Thalamo-Cortical Interactions.

Authors:  Moumita Das; Vanshika Singh; Lucina Q Uddin; Arpan Banerjee; Dipanjan Roy
Journal:  Cereb Cortex       Date:  2021-03-05       Impact factor: 5.357

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