4.7 Article

The average pathlength map: A diffusion MRI tractography-derived index for studying brain pathology

期刊

NEUROIMAGE
卷 55, 期 1, 页码 133-141

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2010.12.010

关键词

Magnetic resonance; Diffusion weighted imaging; HARDI; Quantitative tractography; Constrained spherical deconvolution

资金

  1. National Health and Medical Research [519220]

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Magnetic resonance diffusion tractography provides a powerful tool for the assessment of white matter architecture in vivo. Quantitative tractography metrics, such as streamline length, have successfully been used in the study of brain pathology. To date, these studies have relied on a priori knowledge of which tracts are affected by injury or pathology and manual delineation of regions of interest (ROIs) for use as waypoints in tractography. This limits the analyses to specific tracts under investigation and relies on the accurate and consistent placement of ROIs. We present a fully automated technique for the voxel-wise analysis of streamline length within the entire brain, the Average Pathlength Map (APM). We highlight the precision and reproducibility of voxel-wise average streamline length over time, and assess normal variability of pathlength values in a cohort of 43 healthy participants. Additionally, we demonstrate the utility of this approach by performing voxel-wise comparison between pathlength values obtained from a patient with a severe traumatic brain injury (TBI, Glasgow Coma Scale Score = 7) and those from contROI participants. Our analysis shows that voxel-wise average pathlength values are comparable to fractional anisotropy (FA) in terms of reproducibility and variability. For the TBI patient, we observed a significant reduction in streamline pathlength in the genu of the corpus callosum and its projections into the frontal lobe. This study demonstrates that the average pathlength map can be used for voxel-based analysis of a quantitative tractography metric within the whole brain, removing both the dependence on a priori knowledge of affected pathways and time-consuming manual delineation of ROIs. (c) 2010 Elsevier Inc. All rights reserved.

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