LayNii: A software suite for layer-fMRI.


Journal

NeuroImage
ISSN: 1095-9572
Titre abrégé: Neuroimage
Pays: United States
ID NLM: 9215515

Informations de publication

Date de publication:
15 08 2021
Historique:
received: 23 07 2020
revised: 19 02 2021
accepted: 16 04 2021
pubmed: 16 5 2021
medline: 29 10 2021
entrez: 15 5 2021
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 33991698
pii: S1053-8119(21)00368-2
doi: 10.1016/j.neuroimage.2021.118091
pii:
doi:

Types de publication

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

Langues

eng

Sous-ensembles de citation

IM

Pagination

118091

Subventions

Organisme : Wellcome Trust
ID : 215901/Z/19/Z
Pays : United Kingdom
Organisme : NIMH NIH HHS
ID : R01 MH111444
Pays : United States
Organisme : Medical Research Council
ID : MR/N003403/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/N008537/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/R005745/1
Pays : United Kingdom
Organisme : Intramural NIH HHS
ID : ZIA MH002783
Pays : United States

Informations de copyright

Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.

Auteurs

Laurentius Renzo Huber (LR)

MBIC, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands. Electronic address: renzohuber@gmail.com.

Benedikt A Poser (BA)

MBIC, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands.

Peter A Bandettini (PA)

NIMH, NIH, Bethesda, MD, USA.

Kabir Arora (K)

MBIC, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands.

Konrad Wagstyl (K)

Wellcome Centre for Human Neuroimaging, University College London, London, UK.

Shinho Cho (S)

CMRR, University of Minneapolis, MN, USA.

Jozien Goense (J)

University of Glasgow, UK.

Nils Nothnagel (N)

University of Glasgow, UK.

Andrew Tyler Morgan (AT)

University of Glasgow, UK.

Job van den Hurk (J)

Scannexus, Maastricht, the Netherlands.

Anna K Müller (AK)

Goethe University Frankfurt, Frankfurt, Germany.

Richard C Reynolds (RC)

NIMH, NIH, Bethesda, MD, USA.

Daniel R Glen (DR)

NIMH, NIH, Bethesda, MD, USA.

Rainer Goebel (R)

MBIC, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands; Brain Innovation, Maastricht, the Netherlands.

Omer Faruk Gulban (OF)

MBIC, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands; Brain Innovation, Maastricht, the Netherlands.

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Classifications MeSH