Novel tractography analysis

Differential tractography


Differential tractography tracks the exact segment with a change in anisotropy to reveal pathways with axonal injury.

Conventional tractography maps all existing pathways regardless of whether they have changed their diffusion property, and thus it is insensitive to subtle diffusion differences until the entire axonal loss happens at the very late stage. In comparison. difference tractography [8] adopts a novel “tracking-the-difference” paradigm. This is realized by first conducting a voxel-level comparison between longitudinal scans to filter out regions with no substantial difference. The axons with substantial differences can then be tracked and enhanced for further localization and quantification 


Correlational tractography (connectometry)




Connectometry adopts a "tracking the correlation" paradigm, which is fundamentally different from the conventional analysis paradigm of "finding the difference in tracks or regions". It uses a nonparametric permutation test to find out the association of white matter pathways with any study factor [1][pdf]. It utilizes a simple linear regression that includes all relevant variables (e.g. sex, site difference) in the model. The results will show "correlational tractography", which only maps the exact segment of pathways exhibiting positive or negative correlation with a variable of interest.

The analysis first identifies voxels that have strong correlations and tracks along axonal fiber directions to identify the consecutive fiber segment that shows continuous positive and negative correlation. The tracking is only limit to regions with high correlation, thus minimizing the noise contributed from other non-correlated regions.

Connectometry can also be used in a longitudinal study to study the difference between paired data (e.g. pre- and post- treatment). Group connectometry use a linear regression model and can include other variables to consider in the regression. Group connectometry uses a linear regression model and can include other variables to consider in the regression.

Why is connectometry a powerful test?
Most studies used either track-based or region-based analysis to compare diffusion data along the tracks or within a given region. These approaches require users to assign pre-defined tracks or regions to sample diffusion indices for further analysis. The assigned regions or tracks, however, will inevitably include regions that have low SNR (near gray matter) or show no correlation at all (irrelevant branches), consequently bringing high variance and making results insignificant.

Connectometry adopts an entirely different mindset to avoid including irrelevant regions. The paradigm behind it can be summarized as "tracking the difference". This is fundamentally different from the conventional paradigm of "finding the difference in tracks or regions". The analysis first identifies voxels that have strong correlations and tracks along axonal fiber directions to identify the consecutive fiber segment that shows a continuous positive and negative correlation. The tracking is only limit to regions with high correlation, thus minimizing the noise contributed from other non-correlated regions.

What is the biophysics behind connnectometry?
Connectometry compares spin distribution function (SDF) between subjects, a density-based measurement of diffusion at different orientations. SDF is different from diffusivity measurements such as FA, ADC, RD. Diffusivity measures how fast water diffuses, whereas SDF measures the density of diffusing water. A recent study [4] showed that SDF provides a unique structural characterization that can reliably identify individuals (termed local connectome fingerprint). Its reproducibility and uniqueness is higher than diffusivity-based measurements. It is noteworthy that since SDF reveals high individuality, the inter-subject variance of SDF can be very high (this does not mean that SDF is unreliable). As a result, connectometry is most suitable for a longitudinal study, though a cross-sectional study can also be benefited by connectometry analysis.


Pros:

1. Connectometry identifies only the affected segment of the fiber tracks
2. Allows for multiple regression, group comparison, paired group comparison.

Cons:
1. Cannot work on distorted brain structure (e.g. brain tumor)
2. Relatively poor at identifying short-ranged focal difference.


Exercises:

1. Download the sample MS patient data at https://pitt.box.com/v/differential-tractography and find the injury track using Differential tractography.
2. Download CMU60 connectometry database at https://pitt.box.com/v/course-group-cnt run Group connectometry analysis to find connections correlated to BMI.

Reference:
[1] Yeh, Fang-Cheng, David Badre, and Timothy Verstynen. "Connectometry: A statistical approach harnessing the analytical potential of the local connectome." NeuroImage 125 (2016): 162-171.
[2] Yeh, Fang-Cheng, Pei-Fang Tang, and Wen-Yih Isaac Tseng. "Diffusion MRI connectometry automatically reveals affected fiber pathways in individuals with chronic stroke." Neuroimage: Clinical 2 (2013): 912-921.




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