LayNii: a software suite for layer-fMRI

  • High-resolution fMRI in the sub-millimeter regime allows researchers to resolve brain activity across cortical layers and columns non-invasively. While these high-resolution data make it possible to address novel questions of directional information flow within and across brain circuits, the corresponding data analyses are challenged by MRI artifacts, including image blurring, image distortions, low SNR, and restricted coverage. These challenges often result in insufficient spatial accuracy of conventional analysis pipelines. Here we introduce a new software suite that is specifically designed for layer-specific functional MRI: LayNii. This toolbox is a collection of command-line executable programs written in C/C++ and is distributed opensource and as pre-compiled binaries for Linux, Windows, and macOS. LayNii is designed for layer-fMRI data that suffer from SNR and coverage constraints and thus cannot be straightforwardly analyzed in alternative software packages. Some of the most popular programs of LayNii contain ‘layerification’ and columnarization in the native voxel space of functional data as well as many other layer-fMRI specific analysis tasks: layer-specific smoothing, model-based vein mitigation of GE-BOLD data, quality assessment of artifact dominated sub-millimeter fMRI, as well as analyses of VASO data.

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Author:Laurentius Huber, Benedikt A. Poser, Peter Bandettini, Kabir Arora, Konrad Wagstyl, Shinho Cho, Jozien Goense, Nils Nothnagel, Andrew Tyler Morgan, Job van den Hurk, Anna K. Müller, Richard C. Reynolds, Daniel R. Glen, Rainer Goebel, Omer Faruk Gulban
URN:urn:nbn:de:hebis:30:3-630755
DOI:https://doi.org/10.1016/j.neuroimage.2021.118091
ISSN:1053-8119
Parent Title (English):NeuroImage
Publisher:Academic Press
Place of publication:Orlando, Fla.
Document Type:Article
Language:English
Date of Publication (online):2021/05/12
Date of first Publication:2021/05/12
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2022/03/10
Volume:237
Issue:art. 118091
Page Number:19
First Page:1
Last Page:19
Note:
Parts of this research was supported by the NIMH Intramural Research Program (ZIA-MH002783). Konrad Wagstyl is supported by the Wellcome Trust, Grant: 215901/Z/19/Z. Laurentius Huber was funded form the NWO VENI project 016.Veni.198.032 for part of the study. Benedikt Poser is partially funded by the NWO VIDI grant 16.Vidi.178.052 and by the National Institute for Health grant (R01MH/111444) (PI David Feinberg). Portions of this study used the high performance computational capabilities of the Biowulf Linux cluster at the National Institutes of Health, Bethesda, MD (biowulf.nih.gov). Rainer Goebel is partly funded by the European Research Council Grant ERC-2010-AdG 269853 and Human Brain Project Grant FP7-ICT-2013-FET-F/604102. Nils Nothnagel and Jozien Goense are funded by the Medical Research Council (MR/R005745/1). Andrew Tyler Morgan is funded by the Medical Research Council (MR/N008537/1) and the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 and 945539 (Human Brain Project SGA2 and SGA3)
HeBIS-PPN:494737085
Institutes:Medizin
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung 4.0