Region-based and track-based analysis

Introduction

Most of the diffusion MRI studies used diffusion measures from each voxel and calculate statistics (e.g. mean, variance). A region or a track can be used to sample the voxels, whereas further clustering approach can be used to consider multiple comparison. 

Region-based analysis



source: Zhang, Junying, Yunxia Wang, Jun Wang, Xiaoqing Zhou, Ni Shu, Yongyan Wang, and Zhanjun Zhang. "White matter integrity disruptions associated with cognitive impairments in type 2 diabetic patients." Diabetes 63, no. 11 (2014): 3596-3605.

Manually or using an atlas to define a study region and analysis the average of diffusion indices

Pros: 
1. Simple and straightforward.e
2. There is no need to correct for multiple comparison.

Cons:
1. White matter region is often ill-defined. Multiple fiber pathways may share the same region, making result interpretation difficult. 
2. Manually defining the region can be time consuming.


Tract-based analysis

source: Advanced NTUH MRI Lab, http://abmri.mc.ntu.edu.tw/en/technique.php

Use diffusion MRI fiber tracking to obtain the track trajectories and average diffusion indices along the pathways.

Pros: 
1. More accurate way to define white matter region than ROI-based analysis
2. There is no need to correct for multiple comparison.

Cons: 
1.The analysis is often time-consuming.
2.The accuracy depends on fiber tracking and subject to human error. 
3.The finding be limited to a local segment of a track. Averaging the indices along the pathway may decrease the significance of the findings.

Voxel-based analysis (VBA)


SOURCE: Zhang, Yu, Norbert Schuff, An-Tao Du, Howard J. Rosen, Joel H. Kramer, Maria Luisa Gorno-Tempini, Bruce L. Miller, and Michael W. Weiner. "White matter damage in frontotemporal dementia and Alzheimer's disease measured by diffusion MRI." Brain 132, no. 9 (2009): 2579-2592.

Spatially transform the mapping of diffusion indices to the standard space and calculate statistics voxel-by-voxel.

Pros: 
1. Simple and straightforward.

Cons: 
1. After correction for family wise error, the significance is usually very low due to low SNR of diffusion MRI
2. Most spatial transformation method is designed for gray matter regions.

Tract-based spatial statistics (TBSS)

source: Haller, Sven, Pascal Missonnier, F. R. Herrmann, Cristelle Rodriguez, M-P. Deiber, Duy Nguyen, Gabriel Gold, K-O. Lovblad, and Panteleimon Giannakopoulos. "Individual classification of mild cognitive impairment subtypes by support vector machine analysis of white matter DTI." American Journal of Neuroradiology 34, no. 2 (2013): 283-291.

TBSS projects voxel-based indices to a "skeleton" and analyze them. It handles the 
Pros:
1. Fully automatic

Cons:
1. TBSS is only available in FSL
2. Interpreting the result on the skeleton can be challenging since there can be multiple pathways passing around the skeleton.

Exercise:

1. Use region-based analysis to obtain mean FA, ADC, MD, RD of the corpus callosum
2. Use tracks trajectories to sample mean anisotropy values and diffusivity (see Tract-specific analysis). Use a white matter region to do voxel-based analysis (see How to analyze diffusion data?). Examine whether it gives the same result as track-specific analysis.
3. Use track trajectories to map along tracks diffusion indices (see Tract-specific analysis).

Reference:

Smith, Stephen M., Mark Jenkinson, Heidi Johansen-Berg, Daniel Rueckert, Thomas E. Nichols, Clare E. Mackay, Kate E. Watkins et al. "Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data." Neuroimage 31, no. 4 (2006): 1487-1505.
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