This post lists the background material of the hands-on tutorial about high-resolution EPI on SIEMENS scanners.
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.
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.
In this Blog post, I seek to describe a quick example of how to analyze high-resolution data across layers and columns with LAYNII.
This is a step-by-step description on how to obtain layer profiles from any high-resolution fMRI dataset. It is based on manual delineated ROIs and does not require the tricky analysis steps including distortion correction, registration to whole brain “anatomical” datasets, or automatic tissue type segmentation. Hence this is a very quick way for a first glance of the freshly acquired data.
The important steps are: 1.) Upscaling, 2.) Manual delineation of GM, 3.) Calculation of cortical depths in ROI, 4.) Extracting functional data based on calculated cortical depths.
- Manual aligment of MP2RAGE with EPI (optional when MP2RAGE is acquired in same session)
- ANTS alignment of MP2RAGE and EPI. (part of anatomical_maser.sh, see github)
- Running Freesurfer on MP2RAGE data in EPI space. (part of anatomical_maser.sh, see github)
- Using SUMA to get fine samples tissue borders in EPI-voxel space (in oblique space) (part of anatomical_maser.sh, see github)
- Manual correction of Freesurfer GM-ribbon
- calculating layers from GM-ribbon in neuroDebian.