Meeting minutes of the virtual layer-fMRI event #1

On May 7th 2020, there was a virtual layerfMRI event to discuss current issues in the field.

This meeting was held as a replacement of an originally planned layer-fMRI dinner at ISMRM and happened in succession of an earlier in-person layerfMRI workshop in November 2019 (meeting minutes here).

Below you find the important links of the the virtual event, videos of the talks and discussions, and a summary of the hot topics that were discussed.

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Equi-voluming: The Anakin Skywalker of layering algorithms

Authors: Renzo Huber and Faruk Gulban

When you want to analyze functional magnetic resonance imaging (fMRI) signals across cortical depths, you need to know which voxel overlaps with which cortical depth. The relative cortical depth of each voxel is calculated based on the geometry of the proximal cortical gray matter boundaries. One of these boundaries is the inner gray matter boundary which often faces the white matter and the other boundary is the outer gray matter boundary which often faces the cerebrospinal fluid. Once the cortical depth of each voxel is calculated based on the cortical gray matter geometry, corresponding layers can be assigned to cortical depths based on several principles.

One of the fundamental principles used for “assigning layers to cortical depths” (aka layering, layerification) is the equi-volume principle. This layering principle was proposed by Bok in 1929, where he tries to subdivide the cortex across little layer-chunks that have the same volume. I.e. gyri and sulci will exhibit any given layer at a different cortical depth, dependent on the cortical folding and volume sizes (see figure below).

With respect to applying equi-volume principle in layer-fMRI, the equi-volume layering has gone through quite a story. A plot with many parallels to Anakin Skywalker.

In this blog, the equi-volume layering approach is evaluated. Furthermore, it is demonstrated how to use it in LAYNII software.

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Quality assurance measures for layer-fMRI time series: How to obtain them in LAYNII

Doing layer-fMRI sometimes feels like doing nothing more than noise management. One must have a full grown masochistic personality trait to enjoy working with such messy data. Namely, layer-fMRI time series data suffer from each and every one of the artifacts in conventional fMRI; they are just much worse and there are also a few extra artifacts that we need to worry about. As such, layer-fMRI time series usually suffer from amplified ghosting, time-variable intermittent ghosting, non-gaussian noise, noise-coupling, motion artifacts, and signal blurring.

Thus, we need to have a set of metrics that tell us whether or not we can trust our specific data sets. We would like to have quality assessment (QA) tools that tell us when we need to stop wasting our time on artifact-infested data and throw them away. It would be extremely helpful to have tools that extract a basic set of QA metrics that are  specifically optimized and suited for sub-millimeter resolution fMRI artifacts.

This blog post discusses a number of these layer-fMRI specific QA metrics and describes how to generate them in LAYNII.

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Removing unwanted venous signal from GE-BOLD maps: Overview of vein removal models and implementations in LAYNII

Did you acquire a layer-fMRI study without VASO? Did you even acquire your data with GE-BOLD EPI? Don’t you know that this contrast is dominated by unwanted signals from locally unspecific large draining veins?

That’s ok. Don’t be down in the mouth. Nobody is perfect. It happens to the best of us 😉 Luckily, there are several models out there that should help you to tease out the tiny microvascular GE-BOLD signal that you care about and help you to remove the dominating macro-vascular venous signal. However, note that some of these vein-removal models work better than others. None of the models is perfect! But some of them are useful. The most relevant approaches are implemented in the LAYNII software suit on a voxel-wise level.

In this blog post, I want to describe these de-veining models and how to use them to get rid of unwanted macrovascular venous signals in LAYNII.

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layer-fMRI summer course MBIC 2020

Title: High resolution fMRI: An introductory course for data acquisition and analysis challenges.

Support: This lecture series is finanzially supported by the FPN-MBIC-school. The session on sequences and sequence artifacts is supported (in kind) by the York-Maastricht-partnership grant. Faruk Omer Gulban works for Brain Innovation.

Coordinators: Laurentius (Renzo) Huber & Omer Faruk Gulban, Cognitive Neuroscience Department

Email: or

Dates: 7, 14, 21, 28 July 2020 (4 sessions in total), 3pm to 4:30pm.

Video Conference Zoom link (note that these sessions may be recorded):

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Layer-fMRI VASO worldwide

This blog post gives an overview of the scientific network of researchers that are using the VASO (vascular space occupancy) for applications in layer-fMRI. I tried to give an overview of all layer-fMRI VASO papers published so far and provide a map of all layer-fMRI VASO labs around the globe. Continue reading “Layer-fMRI VASO worldwide”

Layer-fMRI Jobs

Tweet intro
Don’t we all feel like Daniel in his Tweet? Below you find more temporary trainee positions. If you are good and among the best in your field you might even be lucky enough to get one of those.

This page lists all open layer-fMRI job opportunities.

Suggestions are welcome to

This page is created with respect to efforts of the layer-fMRI network meeting in 2019 in Minnesota.


layer-fMRI abstracts

The following is a personal selection of my must-see layer-fMRI abstracts presented at SFN/OHBM/ISMRM. If I missed something, please let us know (


SFN 2019 in Chicago


  • 4:00 pm – 5:00 pm, 092.12/ BB63. Poster, O. STANLEY, Phase-based macrovascular filtering from gradient echo bold fMRI reduces orientation dependence.


  • 8:00 am – 8:15 am, 111.01/Room S402. Nanosymposium talk, J. TOWNSEND: Non-invasive mapping of acoustic-phonetic speech features in human superior temporal gyrus using ultra-high field 7T fMRI.
  • 11:00 am – 12:00 am, 141.04/K28Poster, T. LIU: Affective processing of face stimuli in human primary visual cortex.

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MP2RAGE: challenges and artifacts of its use for layer-fMRI

The MP2RAGE sequence is very popular for 7T anatomical imaging and is very commonly used to acquire 0.7-1 mm resolution whole brain anatomical reference data. Aside of this common application, it can also be very helpful for layer-fMRI studies to obtain even higher resolution T1 maps in the range of 0.5mm iso. However, when optimizing MP2RAGE sequence parameters for layer-fMRI studies, there are a few things that might be helpful to keep in mind.

In this post, I would like to discuss the challenges of using the popular MP2RAGE sequence in layer-fMRI studies. Specifically I will discuss challenges/features regarding:

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Example analysis pipeline of layer-VASO

In this blog post I want to go through the analysis pipeline of layer-dependent VASO.

I will go through the all the analysis steps that need to be done to go from raw data from the scanner to final layer profiles. The entire thing will take about 30 min (10 min analysis and 20 min explaining and browsing through data).

During the entire analysis pipeline I am using the following software packages: SPM, AFNI, and LAYNII, and gnuplot (if you want fancier plotting tools)

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How many layers should I extract?

In this blog post, I want to share my thoughts on the number of layers that should be extracted from any given dataset. I will try to give an overview of how many layers are usually extracted in the field, I’ll describe my personal choices of layer numbers, and I will try to discuss the challenges of layer signal extraction along the way.

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‘layer fMRI’, ‘sub-millimeter fMRI’, ‘mesoscopic fMRI’, or ‘cortical depth dependent fMRI’…. Which term should I use?

What’s the best name of our field and what’s the best attributing term for our data? There are many competing options: “Layer fMRI”, “mesoscopic fMRI”, “sub-millimeter fMRI”, “ultra-high resolution fMRI”, “laminar fMRI”, “cortical depth-dependent fMRI”. They differ with respect to how flashy they are, how scientifically appropriate they are, and how popular they are.

In this blog post, I want to review which ones are the most popular ones in the field and also share some thoughts on my favourite candidates.

Edits on March 3rd 2019 with contributions and clarifications taken from Kamil Uludağ, Sri Kashyap and  Faruk Gulban.

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Registration of high-resolution data

In this blog post Sri Kashyap and I describe how to deal with the registration of high-resolution datasets across days, across different resolutions, and across different sequences.

I am particularly fond of the following two tools: Firstly, ITK-SNAP for visually-guided manual alignment and secondly, using ANTs programs: antsRegistration and antsApplyTransforms.

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TR-FOCI pulse optimisations for SS-SI VASO

CBV-fMRI with VASO is highly dependent on a good inversion contrast. It gives it its CBV sensitivity and is also responsible for most of the VASO specific pitfalls (e.g. inflow, CSF etc. ). And thus, it should be optimized as much as possible.

In this blog post, I want to describe the most important features of a reliable inversion pulse for the application of VASO at 7T with a head transmit coil.

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