Gray-matter Based Spatial Statistics (NODDI-GBSS)

Introduction

Gray matter-based spatial statistics (NODDI-GBSS) is a pipeline to perform voxel-wise statistical analysis on gray matter microstructure. Our method is based on GBSS method primarily introduced by Ball et. al¹ and discribed elswhere in full details^2,3. Unlike the original GBSS method, NODDI-GBSS only uses multi-shell diffusion-weighted images for tissue segmentation and registration^2,3. NODDI-GBSS requires NODDI (http://mig.cs.ucl.ac.uk/index.php?n=Tutorial.NODDImatlab) and DTI images and depends on FSL (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/) and ANTs (http://stnava.github.io/ANTs/).

Overview

gbss_1_reg.sh

Gray matter fraction maps are estimated in the native diffusion space by subtracting CSF fraction (fCSF maps from NODDI) and white matter fraction (estimated by two-tissue class segmentation of FA images using Atropos) from 1 in each voxel. To increase tissue contrasts and enhance between-subject registration steps, partial volume estimation maps for each tissue class are multiplied by their corresponding contrast (0 for CSF, 1 for gray matter, and 2 for white matter) and summed together to generate images with similar contrast to T1-weighted images. The resulting images are then used to build a study-specific template using the buildtemplateparallel.sh script in the Advanced Normalization Tools (ANTs). Gray matter fraction, ODI, and NDI images are warped to the template space using the warp fields estimated during the previous step.

gbss_2_skell.sh

To enhance between-subject alignment of gray matter voxels, GBSS adopts the tract-based spatial statistics (TBSS) algorithm. The average gray matter fraction map was skeletonized and for each individual, diffusion metrics (i.e., ODI and NDI) and gray matter fraction were projected from local voxels with greatest gray matter fraction in the template space onto the skeleton. The final skeleton is generated by keeping only voxels with a gray matter fraction greater than a given threshold (default: 0.65) in more than a given percentage (default: 75%) of the subjects.

gbss_3_fill.sh

The remaining voxels on the subjects’ skeletons with non-satisfactory gray matter fraction (e.g. below 0.65) are filled with the average of the surrounding satisfactory voxels on the skeleton (e.g. gray matter fraction>0.65) weighted by their closeness with a Gaussian kernel (default: σ=2 mm).

Installation:

NODDI-GBSS scripts rely on FSL (v4.1.9 or higher) and ANTs (v2.1). After installing dependencies, simply clone the scripts as follows:

git clone https://github.com/arash-n/GBSS

Add the GBSS/NODDI folder to the $PATH.

Usage:

First, create an output directory containing following folders: FA, CSF, ODI, fIC. Copy respective images into each directory (all should have the same name across the folders: e.g. sub001.nii.gz). Now, you can run the scripts sequentially.

First Step:

For gbss_1_reg.sh, outputs from the NODDI and DTI models should be already available.This script works as follows:

a) The input older containing the following subdirectories: FA, CSF, ODI, fIC. b) Each Folder should contain corresponding image files with the same subject name in all folders.

NOTE: Remove any underline (_) from your filenames.

gbss_1_reg.sh [options] output_directory

Second Step:

NOTE: cd to output_directory

gbss_2_skel.sh

Third Step:

NOTE: cd to stats folder

gbss_3_fill.sh

Potential Alternative Routes:

NODDI Model Fitting:

If you are dealing with large high-resolution multi-shell DWI datasets and have access to computational clusters (+GPUs), I highly recommend using MDT toolbox: https://github.com/robbert-harms/MDT

Tissue Segmentations:

In the original implemenation of NODDI-GBSS, NODDI (Free water maps) and DTI models (FA map--> white matter segmentation) are used to indirectly estimate gray matter probability maps. However, it is possible to use multi-shell data and segment them into different tissue classes based on tissue masks from the structural MRI. This has been implemented as a part of MRtrix3:

dwi2fod msmt_csd dwi.mif wm_response.txt wmfod.mif gm_response.txt gm.mif csf_response.txt csf.mif

Here is the link for more information: https://mrtrix.readthedocs.io/en/latest/constrained_spherical_deconvolution/multi_shell_multi_tissue_csd.html

NOTE: Given the differences in estimation techniques, gray matter probability maps will not be identical with original NODDI-GBSS implementation.

Citations:

1. Ball G, Srinivasan L, Aljabar P, Counsell SJ, Durighel G, Hajnal JV et al. Development of cortical microstructure in the preterm human brain. PNAS; 110(23): 9541-9546.
2. Nazeri A, Chakravarty MM, Rotenberg DJ, Rajji TK, Rathi Y, Michailovich OV et al. Functional Consequences of Neurite Orientation Dispersion and Density in Humans across the Adult Lifespan. J Neurosci 2015; 35(4): 1753-1762.
3. Nazeri A, Mulsant BH, Rajji TK, Levesque ML, Pipitone J, Stefanik L, Shahab S, Roostaei T et al. Gray matter neuritic microstructure deficits in schizophrenia and bipolar disorder. Biological Psychiatry 2017; 82(10): 726-736.

For a primer on diffusion-weighted imaging for characterization of gray matter microstructure and analysis pipelines (in the Supplementary Material), please see:

1. Nazeri A, Schifani C, Anderson JA, Ameis SH, Voineskos AN. In vivo imaging of gray matter microstructure in major psychiatric disorders: Opportunities for clinical translation. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging.