LayNii: A software suite for layer-fMRI
•A new software toolbox is introduced for layer-specific functional MRI: LayNii.•LayNii is a suite of command-line executable C++ programs for Linux, Windows, and macOS.•LayNii is designed for layer-fMRI data that suffer from SNR and coverage constraints.•LayNii performs layerification in the native...
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| Vydané v: | NeuroImage (Orlando, Fla.) Ročník 237; s. 118091 |
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| Hlavní autori: | , , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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United States
Elsevier Inc
15.08.2021
Elsevier Limited Elsevier |
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| ISSN: | 1053-8119, 1095-9572, 1095-9572 |
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| Abstract | •A new software toolbox is introduced for layer-specific functional MRI: LayNii.•LayNii is a suite of command-line executable C++ programs for Linux, Windows, and macOS.•LayNii is designed for layer-fMRI data that suffer from SNR and coverage constraints.•LayNii performs layerification in the native voxel space of functional data.•LayNii performs layer-smoothing, GE-BOLD deveining, QA, and VASO analysis.
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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| AbstractList | 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. •A new software toolbox is introduced for layer-specific functional MRI: LayNii.•LayNii is a suite of command-line executable C++ programs for Linux, Windows, and macOS.•LayNii is designed for layer-fMRI data that suffer from SNR and coverage constraints.•LayNii performs layerification in the native voxel space of functional data.•LayNii performs layer-smoothing, GE-BOLD deveining, QA, and VASO analysis. 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. [Display omitted] 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.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. |
| ArticleNumber | 118091 |
| Author | Huber, Laurentius (Renzo) Reynolds, Richard C. Arora, Kabir Poser, Benedikt A. Bandettini, Peter A. Wagstyl, Konrad Müller, Anna K Glen, Daniel R. Goense, Jozien van den Hurk, Job Nothnagel, Nils Cho, Shinho Morgan, Andrew Tyler Goebel, Rainer Gulban, Omer Faruk |
| AuthorAffiliation | f Scannexus, Maastricht, the Netherlands g Goethe University Frankfurt, Frankfurt, Germany c Wellcome Centre for Human Neuroimaging, University College London, London, UK b NIMH, NIH, Bethesda, MD, USA e University of Glasgow, UK a MBIC, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands h Brain Innovation, Maastricht, the Netherlands d CMRR, University of Minneapolis, MN, USA |
| AuthorAffiliation_xml | – name: c Wellcome Centre for Human Neuroimaging, University College London, London, UK – name: b NIMH, NIH, Bethesda, MD, USA – name: a MBIC, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands – name: h Brain Innovation, Maastricht, the Netherlands – name: g Goethe University Frankfurt, Frankfurt, Germany – name: d CMRR, University of Minneapolis, MN, USA – name: f Scannexus, Maastricht, the Netherlands – name: e University of Glasgow, UK |
| Author_xml | – sequence: 1 givenname: Laurentius (Renzo) orcidid: 0000-0002-3291-2202 surname: Huber fullname: Huber, Laurentius (Renzo) email: renzohuber@gmail.com organization: MBIC, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands – sequence: 2 givenname: Benedikt A. surname: Poser fullname: Poser, Benedikt A. organization: MBIC, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands – sequence: 3 givenname: Peter A. surname: Bandettini fullname: Bandettini, Peter A. organization: NIMH, NIH, Bethesda, MD, USA – sequence: 4 givenname: Kabir orcidid: 0000-0003-3978-9443 surname: Arora fullname: Arora, Kabir organization: MBIC, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands – sequence: 5 givenname: Konrad orcidid: 0000-0003-3439-5808 surname: Wagstyl fullname: Wagstyl, Konrad organization: Wellcome Centre for Human Neuroimaging, University College London, London, UK – sequence: 6 givenname: Shinho orcidid: 0000-0002-3886-3106 surname: Cho fullname: Cho, Shinho organization: CMRR, University of Minneapolis, MN, USA – sequence: 7 givenname: Jozien surname: Goense fullname: Goense, Jozien organization: University of Glasgow, UK – sequence: 8 givenname: Nils surname: Nothnagel fullname: Nothnagel, Nils organization: University of Glasgow, UK – sequence: 9 givenname: Andrew Tyler orcidid: 0000-0001-6017-2179 surname: Morgan fullname: Morgan, Andrew Tyler organization: University of Glasgow, UK – sequence: 10 givenname: Job surname: van den Hurk fullname: van den Hurk, Job organization: Scannexus, Maastricht, the Netherlands – sequence: 11 givenname: Anna K orcidid: 0000-0003-2176-0861 surname: Müller fullname: Müller, Anna K organization: Goethe University Frankfurt, Frankfurt, Germany – sequence: 12 givenname: Richard C. orcidid: 0000-0002-7267-5563 surname: Reynolds fullname: Reynolds, Richard C. organization: NIMH, NIH, Bethesda, MD, USA – sequence: 13 givenname: Daniel R. surname: Glen fullname: Glen, Daniel R. organization: NIMH, NIH, Bethesda, MD, USA – sequence: 14 givenname: Rainer orcidid: 0000-0003-1780-2467 surname: Goebel fullname: Goebel, Rainer organization: MBIC, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands – sequence: 15 givenname: Omer Faruk orcidid: 0000-0001-7761-3727 surname: Gulban fullname: Gulban, Omer Faruk organization: MBIC, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33991698$$D View this record in MEDLINE/PubMed |
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| Copyright | 2021 The Author(s) Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved. 2021. The Author(s) |
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| DOI | 10.1016/j.neuroimage.2021.118091 |
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