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Profilometry: A new statistical framework for the characterization of white matter pathways, with application to
Michael Dayan1, Elizabeth Monohan2, Sneha Pandya1
1Weill Cornell Medicine, Deparment of Radiology, New York, NY.
Human Brain Mapping
|December 16, 2015
Summary
A new "profilometry" framework enables multimetric analysis of white matter tracts, showing myelin water fraction (MWF) better distinguishes multiple sclerosis (MS) patients from controls than radial diffusivity (RD). This method aids in understanding MS-related white matter changes.
Area of Science:
- Neuroimaging
- Biomedical Engineering
- Radiology
Background:
- White matter tract analysis is crucial for understanding neurological disorders like multiple sclerosis (MS).
- Existing methods for analyzing white matter metrics often lack a unified framework for multimetric and multimodal data integration.
- Standardized analysis accounting for demographic factors like age and gender is needed.
Purpose of the Study:
- To introduce a novel
- profilometry
- framework for the multimetric analysis of white matter tracts.
- To demonstrate the framework's application in differentiating multiple sclerosis (MS) patients using radial diffusivity (RD) and myelin water fraction (MWF).
Main Methods:
- A cohort of 141 MS patients and 15 normal controls (NC) underwent T1, T2 FLAIR, T2 relaxometry, and diffusion MRI (dMRI).
- Tractography was used to compute RD and MWF tract profiles, with statistical analysis employing MANCOVA and LDA, controlling for age and gender.
- Lesion identification was performed using T1 and T2 FLAIR sequences, with a focus on the forceps minor.
Main Results:
- Profilometry visualization revealed distinct deviations in MWF and RD from NC normative data at lesion sites in individual MS patients.
- Group comparisons using MANCOVA showed significant differences in lesion locations and identified age as a significant factor in several tracts.
- Linear discriminant analysis (LDA) indicated that MWF is a more effective discriminator between groups than RD.
Conclusions:
- The developed
- profilometry
- framework offers a unified approach for joint multimodality tract profile analysis, addressing a gap in current methodologies.
- This method provides a promising tool for group comparisons using a single score from MANCOVA and assessing individual metric contributions via LDA.
- The findings highlight the potential of this framework for characterizing white matter abnormalities in MS and potentially other neurological conditions.
