title: An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data.
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.
authors.affiliation: Dept. Neurology, Charité - University Medicine, Berlin, Germany; Bernstein Focus State Dependencies of Learning, Bernstein Center for Computational Neuroscience, Berlin, Germany.
authors.forename: Michael
authors.initials: M
authors.lastname: Schirner
authors.affiliation: Dept. Neurology, Charité - University Medicine, Berlin, Germany; Bernstein Focus State Dependencies of Learning, Bernstein Center for Computational Neuroscience, Berlin, Germany.
authors.forename: Simon
authors.initials: S
authors.lastname: Rothmeier
authors.affiliation: Institut de Neurosciences des Systèmes UMR INSERM 1106, Aix-Marseille Université Faculté de Médecine, Marseille, France.
authors.forename: Viktor K
authors.initials: VK
authors.lastname: Jirsa
authors.affiliation: Rotman Research Institute of Baycrest Centre, University of Toronto, Toronto, Canada.
authors.forename: Anthony Randal
authors.initials: AR
authors.lastname: McIntosh
authors.affiliation: Dept. Neurology, Charité - University Medicine, Berlin, Germany; Bernstein Focus State Dependencies of Learning, Bernstein Center for Computational Neuroscience, Berlin, Germany; Minerva Research Group BrainModes, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Berlin School of Mind and Brain, Mind and Brain Institute, Humboldt University, Berlin, Germany. Electronic address: petra.ritter@charite.de.
authors.forename: Petra
authors.initials: P
authors.lastname: Ritter
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology*; D063132:Connectome / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D064847:Multimodal Imaging; D055815:Young Adult
keywords: Computational modeling; Connectome; Diffusion MRI; Multimodal imaging; The Virtual Brain; Tractography