Huber, L. (R.) et al. (2021) LayNii: a software suite for layer-fMRI. NeuroImage, 237, 118091. (doi: 10.1016/j.neuroimage.2021.118091) (PMID:33991698)
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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.
| Item Type: | Articles |
|---|---|
| Additional 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) |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Nothnagel, Dr Nils |
| Authors: | Huber, L. (R.), Poser, B. A., Bandettini, P. A., Arora, K., Wagstyl, K., Cho, S., Goense, J., Nothnagel, N., Morgan, A. T., van den Hurk, J., Müller, A. K., Reynolds, R. C., Glen, D. R., Goebel, R., and Gulban, O. F. |
| College/School: | College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience |
| Journal Name: | NeuroImage |
| Publisher: | Elsevier |
| ISSN: | 1053-8119 |
| ISSN (Online): | 1095-9572 |
| Published Online: | 12 May 2021 |
| Copyright Holders: | Copyright © 2021 The Authors |
| First Published: | First published in NeuroImage 237: 118091 |
| Publisher Policy: | Reproduced under a Creative Commons License |
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