Evaluating the capabilities and challenges of layer-fMRI VASO at 3T

Poster No:


Submission Type:

Abstract Submission 


Renzo Huber1, Lisa Kronbichler2, Rüdiger Stirnberg3, Benedikt Poser1, Sara Fernández-Cabello4, Tony Stöcker3, Martin Kronbichler5


1Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands, 2Department of Psychiatry, Psychotherapy and Psychosomatics, Christian‐Doppler Medical Centre, PMU, Salzburg, Austria, 3German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany, 4Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Uni Oslo, Oslo, Norway, 5Neuroscience Institute, Christian Doppler Medical Centre, PMU, Salzburg, Austria

First Author:

Renzo Huber  
Faculty of Psychology and Neuroscience, Maastricht University
Maastricht, The Netherlands


Lisa Kronbichler  
Department of Psychiatry, Psychotherapy and Psychosomatics, Christian‐Doppler Medical Centre, PMU
Salzburg, Austria
Rüdiger Stirnberg  
German Center for Neurodegenerative Diseases (DZNE)
Bonn, Germany
Benedikt Poser  
Faculty of Psychology and Neuroscience, Maastricht University
Maastricht, The Netherlands
Sara Fernández-Cabello  
Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Uni Oslo
Oslo, Norway
Tony Stöcker, Prof. Dr  
German Center for Neurodegenerative Diseases (DZNE)
Bonn, Germany
Martin Kronbichler  
Neuroscience Institute, Christian Doppler Medical Centre, PMU
Salzburg, Austria


Recent methodological advances in fMRI contrast and readout strategies allow approaching the mesoscopic regime of cortical layers. This enables mapping of cortical information processing within and across brain systems. However, most layer-fMRI studies have been confined to a few specialized ultra-high field (UHF) equipment (≥7Tesla) and venous-biased sequences, which is problematic (Bollmann 2020, Weldon 2020):

1.) The need for UHF scanners limits layer-fMRIs to <100 MRI labs globally and prohibits its widespread adoption as a neuroscientific imaging tool. Layer-fMRI at 3T (Markuerikiaga 2017, Sheeringa 2016) can increase the availability of layer-fMRI worldwide by two orders of magnitude.

2.) The conventional fMRI contrast of gradient-echo (GE) BOLD imposes unwanted signal bias in the superficial layers.

In this study, we aim to demonstrate that both constraints can be addressed with a new imaging methodology of a 3T-optimized VASO (Lu 2003) sequence that utilizes a segmented 3D-EPI readout. We seek to characterize and validate the imaging methodology of 3T CBV-weighted layer-fMRI by means of visual and motor benchmarking tasks. We furthermore aim to extend the imaging modality for whole-brain layer-fMRI connectivity measurement.


8 participants were scanned on a SIEMENS Prisma (64ch coil). We used a 3D-EPI sequence from Stirnberg & Stöcker (2020) for VASO imaging with whole-brain MAGEC capabilities (Huber 2020). Here we used the flexibility of this sequence to optimize layer-dependent CBV acquisitions at 3T:
⇒ the T2*-independence of the VASO contrast allows in-plane segmented approaches with increased VASO CNR and reduced BOLD contaminations (Fig 1A).
⇒ the shorter T1 at 3T compared to 7T, allows more aggressive (larger) flip-angles across kz-segments, increasing the readout SNR efficiency.

A slab protocol (26-28 slices) was used for motor/visual experiments. A whole-brain protocol (120 slices) was used for movie watching. Both protocols were acquired with 0.8mm iso. resolution and in-plane GRAPPA 3. The whole brain protocol further applied through-plane GRAPPA 2 and 2-fold in-plane segmentation (Stirnberg 2020) and divided the k-space segment acquisitions across 4 inversion cycles. Compared to whole brain layer-fMRI at 7T (Mueller 2021), the readout efficiency could be increased by a factor of two.
Layer analyses were done in LAYNII, including layer-specific smoothing by 0.8mm (Huber et al., 2020).
The visual/motor task consisted of 12 min alternating activation-rest blocks. A movie watching task (Finn 2020) was used for connectivity analyses (HCP clips).
All scan protocols, data, and analyses of this study are available for download on Openneuro and Github: https://github.com/layerfMRI/Sequence_Github -> 3T_layerVASO.


Fig. 1B depicts a single run for BOLD and VASO. The GE-BOLD activation map shows more significantly activated voxels than VASO. VASO's higher spatial specificity is also visible in profile plots as two separate peaks. The blurry nature of the GE-BOLD signal is expected due to the locally unspecific (intra-vascular) signal at 3T.
Fig. 2 illustrates representative results for the different tested protocols. Across all acquisition setups, we found significant activity changes. Due to the locally specificity of CBV-fMRI, the activation pattern closely follows the cortical ribbon.
Supporting Image: Fig1-01.png
   ·Fig. 1) Panel A depicts the sequence approach used for high resolution layer-specific VASO. Panel B depicts the layer-specific sensitivity and specificity of BOLD and VASO fMRI contrasts at 3T.
Supporting Image: Fig2-01.png
   ·Fig. 2) Blood volume weighted fMRI signal quality and activation maps of three layer-fMRI protocols tested.


In this work we find that high quality layer-specific CBV-fMRI data can be acquired on widely available clinical 3T scanners. While the functional detection threshold is limited, we show that layer-dependent activation can be visualized in reasonable scan times of 12min (primary brain areas) to 45 min (associative brain areas).
Due to the vast availability of 3T compared to UHF scanners, we believe that this demonstration of 3T optimized VASO development can boost the user base of laminar fMRI, and helps to pave the way for CBV layer-fMRI to become a routine tool to address both clinical and basic neuroscience research questions.

Novel Imaging Acquisition Methods:



Cortical Layers
Open Data
Open-Source Code

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Provide references using author date format

Bollmann & Barth (2020), Progr. NeuroBiol., https://doi.org/10.1016/j.pneurobio.2020.101936
Finn et al., (2019). Nat. Neuro., https://doi.org/10.1038/s41593-019-0487-z
Huber et al., (2017), Neuron., https://doi.org/10.1016/j.neuron.2017.11.005
Huber et al. (2020). BioRxiv, doi: https://doi.org/10.1101/2020.06.12.148080
Lu et al. (2003), MRM https://doi.org/10.1002/mrm.10519
Markuerkiaga et al., (2017), BioRxiv, https://doi.org/10.1101/2020.07.16.206383
Mueller et al., (2021). ISMRM, https://layerfmri.page.link/ISMR2021
Scheeringa et al. (2016), PNAS. https://doi.org/10.1016/j.cub.2015.12.038
Strinberg et al., (2020), MRM, https://doi.org/10.1002/mrm.28486
Weldon & Olman, (2020), Philos. Trans. R. Soc. B, https://doi.org/10.1098/rstb.2020.0040