How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index

Summary

Measuring gyrification (cortical folding) at any age represents a window into early brain development. Hence, we previously developed an algorithm to measure local gyrification at thousands of points over the hemisphere¹. In this paper, we detail the computation of this local gyrification index.

Abstract

Cortical folding (gyrification) is determined during the first months of life, so that adverse events occurring during this period leave traces that will be identifiable at any age. As recently reviewed by Mangin and colleagues², several methods exist to quantify different characteristics of gyrification. For instance, sulcal morphometry can be used to measure shape descriptors such as the depth, length or indices of inter-hemispheric asymmetry³. These geometrical properties have the advantage of being easy to interpret. However, sulcal morphometry tightly relies on the accurate identification of a given set of sulci and hence provides a fragmented description of gyrification. A more fine-grained quantification of gyrification can be achieved with curvature-based measurements, where smoothed absolute mean curvature is typically computed at thousands of points over the cortical surface⁴. The curvature is however not straightforward to comprehend, as it remains unclear if there is any direct relationship between the curvedness and a biologically meaningful correlate such as cortical volume or surface. To address the diverse issues raised by the measurement of cortical folding, we previously developed an algorithm to quantify local gyrification with an exquisite spatial resolution and of simple interpretation. Our method is inspired of the Gyrification Index⁵, a method originally used in comparative neuroanatomy to evaluate the cortical folding differences across species. In our implementation, which we name local Gyrification Index (lGI¹), we measure the amount of cortex buried within the sulcal folds as compared with the amount of visible cortex in circular regions of interest. Given that the cortex grows primarily through radial expansion⁶, our method was specifically designed to identify early defects of cortical development.

In this article, we detail the computation of local Gyrification Index, which is now freely distributed as a part of the FreeSurfer Software (http://surfer.nmr.mgh.harvard.edu/, Martinos Center for Biomedical Imaging, Massachusetts General Hospital). FreeSurfer provides a set of automated reconstruction tools of the brain's cortical surface from structural MRI data. The cortical surface extracted in the native space of the images with sub-millimeter accuracy is then further used for the creation of an outer surface, which will serve as a basis for the lGI calculation. A circular region of interest is then delineated on the outer surface, and its corresponding region of interest on the cortical surface is identified using a matching algorithm as described in our validation study¹. This process is repeatedly iterated with largely overlapping regions of interest, resulting in cortical maps of gyrification for subsequent statistical comparisons (Fig. 1). Of note, another measurement of local gyrification with a similar inspiration was proposed by Toro and colleagues⁷, where the folding index at each point is computed as the ratio of the cortical area contained in a sphere divided by the area of a disc with the same radius. The two implementations differ in that the one by Toro et al. is based on Euclidian distances and thus considers discontinuous patches of cortical area, whereas ours uses a strict geodesic algorithm and include only the continuous patch of cortical area opening at the brain surface in a circular region of interest.

Protocol

1. Reconstruct the 3D cortical surfaces

This first part of the protocol uses the standard FreeSurfer pipeline as described in the Wiki (http://surfer.nmr.mgh.harvard.edu/fswiki). Note that the commands detailed here describe one way of achieving the cortical surface reconstructions, but equivalent commands may also be used.

1. Import the raw MRI DICOM into FreeSurfer and verify the quality of the image (e.g. that the orientation is correct, the contrast sufficient and the images not moved). This process uses the following commands (replace the t....

Discussion

The protocol above describes how to measure local Gyrification Index based on cerebral T1-weighted MRI and conduct statistical group comparisons. Our method has been specifically designed to localize early disruption in the cortical expansion process and as such is of particular interest in many neurodevelopmental or psychiatric conditions. Examples of group comparisons in clinical samples can be found in publications by our group^1,12 or by others^13-16. The process is fully automated and requ.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Material: a Unix or Mac workstation with a processor of 2GHz or faster and a minimum of 4GB of RAM, with FreeSurfer installed (http://surfer.nmr.mgh.harvard.edu/fswiki, preferably the latest version, but no older than version 4.0.3). In order to compute the local Gyrification Index, MATLAB is also required (http://www.mathworks.com/) along with the Image Processing Toolbox.
Data: A sample of good quality (high-resolution, high contrast) cerebral MRI T1-weighted dataset. Your group of subjects must be preferably matched for age and gender. Given the normal inter-individual variability in cerebral morphology, the number of subjects in each group should be sufficient to identify an existing group difference (the more - the better). A reasonable minimum sample size would be around 20 subjects per group (although you can probably go for less if the intensity of changes is large and if your groups are tightly matched for gender and age).
FreeSurfer	Martinos Center for Biomedical Imaging, MGH		Version newer than 4.0.3
Matlab	Mathworks		Image Processing Toolbox