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 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.
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”
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”
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: renzohuber@gmail.com or faruk.gulban@maastrichtuniversity.nl
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.
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.