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.Continue reading “Baseline CBV and it’s role for the interpretation of layer-dependent VASO signals.”
On Sept 28th 2020, the second virtual layer-fMRI event is scheduled.
This meeting is held as a succession of the first virtual dinner in May 2020: https://layerfmri.com/virtualevent1/
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.
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.
This blog post summarizes the FENS webinar entitled: Multiscale, multimethod human brain imaging. Organised by the Human Brain Mapping.
Organized by Lars Muckli and Lucy Petro.
The purpose of this blog post is to provide guidance on how to get started with the layer-fMRI analysis suite: LAYNII. This post is an extended version of the LAYNII README.
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
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): https://maastrichtuniversity.zoom.us/meeting/register/tJAvcu-qpj8sHNVD71Vcu95et-R14QKRs22T
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”
This post summarizes the presentation, tutorials, and discussions of the 2019 UHF Minnesota Workshop on Cortical Depth-Resolved fMRI Methods, Nov 12th-Nov 13th.
This page lists all open layer-fMRI job opportunities.
Suggestions are welcome to layerfMRI@gmail.com
- 2020 PhD position in Leipzig and Juelich on high resolution functional connectivity at 7T with Sofie Valk.
- 2020 Vadim Axelrod will host a postdoc for 7T layer-fMRI research in Israel. Applicable for PhD graduates from EU countries and Canada. email@example.com
- 2020 3 Post doc positions at Freie Universitaet Berlin, on layer-fMRI. As part of ERC grant awarded to Radek Cichy.
- 2020 Faculty position for 7T in RIKEN (Japan)
- 2020 PhD student position to work in Maastricht with Federiko DeMartino on layer-fMRI in the auditory cortex.
- 2020 Post Doc or Pre Doc position at UCL with Callaghan, Nadine Grädel, Nadège Corbin, and Ali Aghaeifar on MRI methods, including layer-fMRI with VASO.
- 2020 Post Doc position in Leipzig (Max-Planck) on layer-specific real-time fMRI with Nick Weiskopf and Romy Lorenz.
- 2020 Post Doc position in Liege (Belgium) on high-resolution fMRI with Eveline Balteau and Christophe Phillips.
- 2020 Post Doc position in the new lab from Kawin Setsompop in Stanford US, on advanced (f) MRI acquisition and reconstruction. Also in collaboration with the Feinbergatron layer-scanner in Berkeley.
- 2020 Staff scientist position in the new lab from Kawin Setsompop in Stanford US, on advanced (f) MRI acquisition and reconstruction. Also in collaboration with the Feinbergatron layer-scanner in Berkeley.
- 2020 PhD position for layer fMRI in Maastricht with Rainer Goebel (Brain Innovations) and Renzo Huber.
- 2020 Post-doc on high-resolution fMRI at 7T with Alexander Puckett at the University of Queensland, Australia
- 2020 Layer-fMRI postdoc, with Nikolai Axmacher at Ruhr University Bochum
- 2020 Layer-fMRI postdoc, with Blake Butler, in London Ontario
- 2020 Lausanne, Switzerland, with Ileana Jelescu on diffusion functional fMRI at 7T
- 2020 Brisbane, Australia, with Alex Puckett, Post-doc and PhD positions, on layer-fMRI
- 2020 Trondheim, in Systems Neuroscience, Faculty Associate Professor with 7T
- 2020 Glasgow, with Jozien Goense on layer-fMRI applications, two post-docs and a faculty position.
- 2019 Suwon, Korea, with Kamil Uludag, Post-doc position on layer-fMRI methods.
- 2019 The Veterans Health Research Institute, with An Vu, about microscale fMRI
- 2019 Maastricht, with Benedikt Poser, Post-doc, on UHF advanced imaging.
- 2019 Glasgow, with Lars Muckli, on layerfMRI and computational modeling
- 2019 Magdeburg, with layerfMRI-expert Esther Kuehn, PdH-Position on Neuronal Mechanisms of Autosuggestion
- 2019 NIH, Bethesda, with Peter Bandettini, multiple positions on fMRI methods, including layerfMRI.
- 2019 UCSF, with Duan Xu, on advances imaging methods at with a GE 7T scanner, Post-doc position.
- 2019 Aalto, Finland, Faculty position on NeuroImaging (7T Terra might come soon).
- 2019 Montreal, with Rick Hoge at their new 7T Terra, Research Associate position on advanced UHF functional imaging.
- 2019 Liege, with 7T Terra, Post-doc MRI physicist
- 2019 Maastricht, with Federiko de Martino, on layer-fMRI, Post-doc
This page is created with respect to efforts of the layer-fMRI network meeting in 2019 in Minnesota.
This post lists the background material of the hands-on tutorial about high-resolution EPI on SIEMENS scanners.
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 (layerfMRI@gmail.com).
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/K28. Poster, T. LIU: Affective processing of face stimuli in human primary visual cortex.
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:
- Background noise.
- Foldover artifact at very high resolutions.
- Usage of double-partial Fourier imaging.
- Inflow into arterial vessels.
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).
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.
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.