LayNii: A software suite for layer-fMRI

Laurentius Renzo 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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-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: layerspecific 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
Article number118091
Pages (from-to)1-29
Number of pages19
JournalNeuroimage
Volume237
Early online date12 May 2021
DOIs
Publication statusPublished - 15 Aug 2021

Keywords

  • ANALYSIS STRATEGIES
  • FUNCTIONAL MRI
  • GRADIENT-ECHO
  • HIGH-RESOLUTION FMRI
  • HUMAN BRAIN
  • HUMAN VISUAL-CORTEX
  • NEGATIVE BOLD
  • SEGMENTATION
  • SPECIFICITY
  • SPIN-ECHO EPI

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