Tutorial on how to use the image processing pipeline with the HBP collab
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Tutorial on how to use the image processing pipeline with the HBP collab
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An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data, published in NeuroImage, August 2015, by Michael Schirner, Simon Rothmeier, Viktor K. Jirsa, Anthony Randal McIntosh, PetraRitter
doi: 10.1016/j.neuroimage.2015.03.055
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.
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This tutorial on importing and creating brain models in The Virtual Brain is part of the TVB Node 10 series, a 4 day workshop dedicated to learning about The Virtual Brain, brain imaging, brain simulation, personalised brain models, TVB use cases, etc... TVB is a full brain simulation platform.
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This lecture on generating TVB ready imaging data is part of the TVB Node 10 series, a 4 day workshop dedicated to learning about The Virtual Brain, brain imaging, brain simulation, personalised brain models, TVB use cases, etc. TVB is a full brain simulation platform.
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Walk through of the Image Processing Pipeline, an integral part of the TVB on EBRAINS integrated workflows
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An overview of the process of constructing models for TVB automatically on the EBRAINS infrastructure
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Brain network reconstruction from empirical data is of key importance to generate personalized virtual brain models. This lecture will introduce the basic concepts of preprocessing structural, functional and diffusion weighted neuroimages. It highlights the latest methods and pipelines to extract structural as well as functional connectomes according to a multimodal parcellation.