This page lists all of my favourite layer-fMRI conference abstracts from 2020.
OHBM, ISMRM, SFN abstracts are added as they are published. ISMRM will follow on July 24th.
Author: renzohuber
Referral to description of layerification algorithm in LN2_LAYERS
How can one assign layers to discrete voxels? Is it possible to perform topographical fMRI analyses across layers and columns directly in the original voxel space that raw data from the scanner come in?
Continue reading “Referral to description of layerification algorithm in LN2_LAYERS”
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.
Continue reading “Meeting minutes of the virtual layer-fMRI event #1”
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.
Continue reading “Equi-voluming: The Anakin Skywalker of layering algorithms”
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.
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.
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”
