PURPOSE: To investigate brain-wide white matter structural changes associated with amyotrophic lateral sclerosis (ALS) using an automatic single seed point tractography-based segmentation method, probabilistic neighborhood tractography (PNT), which provides quantitative measures of both tract integrity and shape. MATERIALS AND METHODS: Diffusion MRI data were acquired from 30 patients with ALS (ALS Functional Rating Scale-Revised score > 20) and 30 matched controls. PNT was used to segment 12 major projection, commissural and association fibers, and assess differences in how the shape of an individual subject's tract compares to that of a predefined reference tract, in addition to providing tract-average mean diffusivity (〈D〉) and fractional anisotropy (FA) data. RESULTS: Across all 12 tracts, group-averaged 〈D〉 was larger, while group-averaged FA was equal to or smaller in value for patients than controls. These differences were significant for right cingulum 〈D〉, and left and right corticospinal tract (CST) 〈D〉 and FA (P-values 6 × 10(-5) to 0.03). Tract shape modeling indicated that there were significantly greater topological differences from the reference tract in left and right CST, and right uncinate fasciculus (P-values 0.02 to 0.04) for patients than controls. The rate of disease progression was significantly negatively correlated with bilateral CST FA (P-values 0.01 to 0.02). CONCLUSION: ALS, although particularly affecting CST, is associated with subtle changes in white matter tract integrity and shape in several other major fibers within the brain. Correlations between CST integrity and disease progression rate suggest that quantitative tractography may provide useful biomarkers of disease evolution in ALS. J. Magn. Reson. Imaging 2013;. © 2013 Wiley Periodicals, Inc.
Original languageEnglish
Pages (from-to)1140-1145
Number of pages6
JournalJournal of Magnetic Resonance Imaging
Issue number5
Publication statusPublished - Nov 2013


Dive into the research topics of 'Quantitative tractography and tract shape modeling in amyotrophic lateral sclerosis'. Together they form a unique fingerprint.

Cite this