LayNii: A software suite for layer-fMRI

Laurentius (Renzo) R. Huber, Benedikt A. Poser, Peter A. 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

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number118091
JournalNeuroImage
Volume237
DOIs
StatePublished - Aug 15 2021

Bibliographical note

Funding Information:
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)

Funding Information:
We thank Pilou Bazin for contributing comments, discussions and ideas regarding the algorithms and implementation of the equi-volume layerification and layer-specific smoothing programs of LayNii. We thank Kamil Uludag and Martin Havlicek for comments on the manuscript regarding the model-based vein mitigation. 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), We thank Sriranga Kashyap for many informative questions of previous versions of LayNii. We thank Kamil Uludag for helpful discussions and contributions on model-based deveining (Fig. 10). We thank Elisha Merriam for discussions about the least noise amplifications in LN_BOCO with spline surround-division. We thank the many users of LayNii that submitted bug reports and feature requests to our repository. We thank Kenny Chung and Joe Stolinski for radiographic assistance with experiments conducted at NIH (Figs. 4C, 5E, 6, 9B). We thank Sean Marrett for co-acquiring data used in Figs. 6, and 9B. We thank FMRIF (especially Andy Derbyshire), NMRF (especially Joelle Sarlls and Lalith Talagala), and Scannexus (especially Chris Wiggins), where the example data were acquired. We thank R?diger Stirnberg and Tony St?ker for their segmented IR-3D-EPI sequence that was used to obtain data in Fig. 8. Author OFG and RG work for Brain Innovation and have financial interest tied to the company. We thank the many LayNii users who sent feedback via Github issues. We also thank Dimo Ivanov, Sri Kashyap and Deni Kurban for testing the binaries. Example data, source code, binary executables, installation instructions, installer packages can be found here: https://doi.org/10.5281/zenodo.3514298 via a BSD-3 license. A Dockercontainer with preinstalled LayNii and example data is avaliable here: https://hub.docker.com/r/layerfmri/laynii_v2.0.0. Also see common analysis pipelines in which LayNii is used here: https://github.com/ofgulban/LayNii_extras.

Publisher Copyright:
© 2021 The Authors

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, N.I.H., Intramural
  • Research Support, Non-U.S. Gov't

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