FreeSurfer & FSL

 

Introduction to Freesurfer

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Typical 3D neuroimaging data utilizes voxels that can either represent anatomical features or functional activity. However, voxels are not efficient and effective when wanting to analyze partial volumes or regions, as they are not able to distinctly distinguish between regions in activity data images. As a result, FreeSurfer is a tool that can be utilized to overcome this challenge. FreeSurfer is a software tool that analyzes cortical and subcortical anatomy and condenses multiple points of information into a single interface to be used for analysis. Freesurfer is a neuroimaging package for the analysis and visualization of neuroimaging data. It is an open-source neuroimaging toolkit for processing, analyzing, and visualizing human brain MRI images. It is meant to make this analysis easy as it is able to preprocess large sums of data all at once. Freesurfer is used especially for cortical and subcortical anatomy analysis. Additionally, FreeSurfer can preprocess data sets and do the preprocessing steps with simple commands and even all at once. This makes preprocessing far easier and can be more efficient and accurate for analysis later. However, it is slow because it takes 6-24 hours for a single subject image, so there are features like parallel that can be used to run multiple subject data at a time and shorten the overall time for preprocessing and analysis.  For more info go to https://surfer.nmr.mgh.harvard.edu/.

 

The Function of Freesurfer

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Freesurfer is used for the creation of computerized models of the brain from MRI data. It is especially useful in the processing of fMRI data. Freesurfer can measure a number of morphometric properties of the brain including cortical thickness, the width of gray matter in the human cortex, and regional volumes. FreeSurfer essentially creates a computerized model of the brain from MRI data, through preprocessing of data including fMRI data. Through its processing, one can measure different brain properties including cortical thickness and regional volumes. Cortical thickness is the measure of the width of gray matter thickness, and regional volumes are volumes of different size regions of the brain like diff. Subcortex. Another FreeSurfer function is to be a single interface through which users can combine and average structural and functional data. This means they can vary between different surface overlays of the brain as seen in the image. Users can also compare two different subject brains which are highly useful in analysis. Freesurfer can also perform inter-subject averaging of structural and functional data and uses a procedure that aligns individuals based on their cortical folding patterns for the optimal alignment of homologous neural regions.

 

 

Input, Analysis, and Preprocessing with Freesurfer

Freesurfer takes in input data from multiple sources in order to create condensed 3D Images with multiple features. It analyzes a weighted 3D image to create different anatomical images. Each voxel is converted into 2D triangles with each vertex containing information like volume, thickness, and activity levels. MRI is preprocessed and a final image is created in a number of steps. First, FreeSurfer removes the skull from the brain volume in the image. Second, gray-white matter segmentation occurs to divide and identify different cortex regions. Third, the boundaries of the different boundaries are determined and outlined. Lastly, this prepped image is inflated to a sphere and then compared to a template sphere called the fsAverage that determines and aligns different parts of the brain.