Profilometry: A new statistical framework for the characterization of white matter pathways, with application to multiple sclerosis.
| Publication Type | Academic Article |
| Authors | Dayan M, Monohan E, Pandya S, Kuceyeski A, Nguyen T, Raj A, Gauthier S |
| Journal | Hum Brain Mapp |
| Volume | 37 |
| Issue | 3 |
| Pagination | 989-1004 |
| Date Published | 12/15/2015 |
| ISSN | 1097-0193 |
| Keywords | Brain, Diffusion Magnetic Resonance Imaging, Multiple Sclerosis, White Matter |
| Abstract | AIMS: describe a new "profilometry" framework for the multimetric analysis of white matter tracts, and demonstrate its application to multiple sclerosis (MS) with radial diffusivity (RD) and myelin water fraction (MWF). METHODS: A cohort of 15 normal controls (NC) and 141 MS patients were imaged with T1, T2 FLAIR, T2 relaxometry and diffusion MRI (dMRI) sequences. T1 and T2 FLAIR allowed for the identification of patients having lesion(s) on the tracts studied, with a special focus on the forceps minor. T2 relaxometry provided MWF maps, while dMRI data yielded RD maps and the tractography required to compute MWF and RD tract profiles. The statistical framework combined a multivariate analysis of covariance (MANCOVA) and a linear discriminant analysis (LDA) both accounting for age and gender, with multiple comparison corrections. RESULTS: In the single-case case study the profilometry visualization showed a clear departure of MWF and RD from the NC normative data at the lesion location(s). Group comparison from MANCOVA demonstrated significant differences at lesion locations, and a significant age effect in several tracts. The follow-up LDA analysis suggested MWF better discriminates groups than RD. DISCUSSION AND CONCLUSION: While progress has been made in both tract-profiling and metrics for white matter characterization, no single framework for a joint analysis of multimodality tract profiles accounting for age and gender is known to exist. The profilometry analysis and visualization appears to be a promising method to compare groups using a single score from MANCOVA while assessing the contribution of each metric with LDA. |
| DOI | 10.1002/hbm.23082 |
| PubMed ID | 26667008 |
| PubMed Central ID | PMC6867537 |