Vascular Space Occupancy is an fMRI method that is popular for high-resolution layer-fMRI. Currently, the most popular sequence is the one by Rüdiger Stirnberg from the DZNE in Bonn, which is actively being employed at more than 30 sites.
This sequence concomitantly acquires fMRI BOLD and blood volume signals. In the SIEMENS reconstruction pipeline, these signals are mixed together within the same time series, which challenges its user friendliness. Specifically: The “raw” dicom2nii-converted time-series are not BIDS compatible (see https://github.com/bids-standard/bids-specification/issues/1001). The order of odd and even BOLD and VASO image TRs is dependent on the nii-converter. Workarounds with 3D distortion correction, results in interpolation artifacts. Workarounds without MOSAIC decorators result in impracticable large data sizes.
The goal of this Hackathon is to extend the 3D-MOSAIC to solve these constraints. This functor is commonly used to sort images by echo-times, by RF-channels, by magnitude and phase in the SIEMENS reconstruction pipeline into sets of mosaics . However currently, this functor does not yet support the dimensionality of SETs. In this project we seek to include SETs into the capabilities of the functor.
In this blog post, I want to summarise the take-home message from the seemingly never ending battle between researchers fighting about the best sequence for layer-fMRI. I seek to do so by means of cynical metaphors in graphical form. Because, why not. There are plenty more serious discussions already elsewhere1,2,3.
In this third event, it will be discussed how the layer-fMRI methodologies might be able to inform Cognitive models. The three speakers are researchers that are working to examine cognitive processes whose study is aided by understanding the structure and function of cortical layers. These cognitive processes could include memory, attention, learning, dreaming, language or cortical predictions (plus many, many more!)
Floris de Lange will give an overview of work done by his group to capture laminar fMRI activity changes in the visual cortex for prediction, attention and bottom-up input. André Bastos will present results of laminar LFP recordings and how feed-forward gamma-band and feedback alpha/beta band modulations help to understand cognitive effects including attention, working memory, and prediction processing. Michelle Moerel will talk about how computational models can be combined with laminar fMRI to understand human auditory processing.
Below you find the important links of the virtual event. Embedded videos of the talks, discussions, and a summary of the hot topics are going to be added on the day after the event.
Are you ever annoyed how hard it is to get brain data off the scanner? The fact that scanners usually contain private information about patients and are thus embedded in maximally restrictive clinical cyber-security environments, makes it quite complicated to get access to the data. Especially when visiting collaborative sites.
In this this Hackathon project, we aim to develop a purely uni-directional (safe) data streaming “hack” to transfer MRI data directly to the cloud by means dynamic QR codes.
In the early days of the Internet, modems (modulator-demodulator) were used to (i) convert digital information into audio streams, (ii) transfer them across telephone lines, and (iii) convert them back into the digital domain. Here, we aim to do the same thing with pixel data of MRI scans. However, instead of audio signal we will use machine-readable visual information: QR codes.
Specific aims of the Brain QR modem
1.) We will develop an ICE-Functor that converts pixel data to QR codes in real time
2.) We will develop an Android app that converts the streamed QR coded into a series of png that are directly streamed to the cloud (Drive folder).
3.) We will develop a LayNii program that converts stacks of PNG images into Nii files.
This project contains many consecutive components of a modem. And will likely take 2-3 rounds of Hackathons to be completed.
This blog post represents a continuation of the manuals regarding VASO acquisition and VASO signal analysis. It deals with the question of quantifying the VASO signal change with respect to the baseline blood volume at rest. In this post, I try to provide an overview of the values of baseline blood volume in the literature, I hypothesise reasons for their discrepancy and conclude by arguing that one should refrain from analyzing VASO in relative units after all.
In this second event, it will be discussed how the research field can bridge the gap between layer-dependent activity measures that are obtained with fMRI and electrophysiology, respectively. Kamil Ugurbil will present the perspective of high resolution for human neuroscience, Lucia Melloni will present the perspective of depth-dependent electrophysiological recordings in humans, and Seong-Gi Kim will talk about the combination of both worlds, layer-fMRI and layer-dependent electrophysiological recordings.
Below you find the important links of the the virtual event. Embedded videos of the talks, discussions, and a summary of the hot topics are going to be added on the day after the event.
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.
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.
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.
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.
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?
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