In layer-fMRI, we spend so much time and effort to achieve high spatial resolutions and small voxel sizes during the acquisition. However, during the evaluation pipeline much of this spatial resolution can be lost during multiple resampling steps.
In this post, I want to discuss sources of signal blurring during spatial resampling steps and potential strategies to account for them.
Continue reading “Unwanted spatial blurring during resampling”
Smoothing within layers can be advantageous for multiple reasons:
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.
This post shows the overview of doing bias field correction in in SPM. Doing this helps me a lot to improve the accuracy of FreeSurfer with MP2RAGE data. The members of Polimeni’s group also use it as an additional per-processing step before giving the data to Freesurfer.
The SPM bis field correction is part of the segmentation pipeline in SPM. I use the following bash script and the following matlab stript:
Bias_field_script_job.m and start_bias_field.sh.
In this blog post, I describe how you can build your own standalone C++ program to analyze nii data without any dependencies beyond C++. Hence, it will work without the libraries from other fMRI software packages, e.g. odin, afni. The only tricky part is to use a stand alone nii I/O to write your own C++ evaluation programs. Here, I do it by incorporating the few files that i borrowed from the afni source code.
Big steps:
This is a description on how to use SPM and/or AFNI for high-resolution SS-SI-VASO data evaluations.
Here it is explained what to do with the raw fMTR data from the scanner to obtain CBV and BOLD signal time courses .
Every other image has a different contrast.
