Investigating the Prevalence of Complex Fiber Configurations in White Matter Tissue with Diffusion Magnetic Resonance Imaging

TY - JOUR

T1 - Investigating the Prevalence of Complex Fiber Configurations in White Matter Tissue with Diffusion Magnetic Resonance Imaging

AU - Jeurissen, Ben

AU - Leemans, Alexander

AU - Tournier, Jacques-Donald

AU - Jones, Derek K.

AU - Sijbers, Jan

PY - 2013/11

Y1 - 2013/11

N2 - It has long been recognized that the diffusion tensor model is inappropriate to characterize complex fiber architecture, causing tensor-derived measures such as the primary eigenvector and fractional anisotropy to be unreliable or misleading in these regions. There is however still debate about the impact of this problem in practice. A recent study using a Bayesian automatic relevance detection (ARD) multicompartment model suggested that a third of white matter (WM) voxels contain crossing fibers, a value that, whilst already significant, is likely to be an underestimate. The aim of this study is to provide more robust estimates of the proportion of affected voxels, the number of fiber orientations within each WM voxel, and the impact on tensor-derived analyses, using large, high-quality diffusion-weighted data sets, with reconstruction parameters optimized specifically for this task. Two reconstruction algorithms were used: constrained spherical deconvolution (CSD), and the ARD method used in the previous study. We estimate the proportion of WM voxels containing crossing fibers to be approximate to 90% (using CSD) and 63% (using ARD). Both these values are much higher than previously reported, strongly suggesting that the diffusion tensor model is inadequate in the vast majority of WM regions. This has serious implications for downstream processing applications that depend on this model, particularly tractography, and the interpretation of anisotropy and radial/axial diffusivity measures.

AB - It has long been recognized that the diffusion tensor model is inappropriate to characterize complex fiber architecture, causing tensor-derived measures such as the primary eigenvector and fractional anisotropy to be unreliable or misleading in these regions. There is however still debate about the impact of this problem in practice. A recent study using a Bayesian automatic relevance detection (ARD) multicompartment model suggested that a third of white matter (WM) voxels contain crossing fibers, a value that, whilst already significant, is likely to be an underestimate. The aim of this study is to provide more robust estimates of the proportion of affected voxels, the number of fiber orientations within each WM voxel, and the impact on tensor-derived analyses, using large, high-quality diffusion-weighted data sets, with reconstruction parameters optimized specifically for this task. Two reconstruction algorithms were used: constrained spherical deconvolution (CSD), and the ARD method used in the previous study. We estimate the proportion of WM voxels containing crossing fibers to be approximate to 90% (using CSD) and 63% (using ARD). Both these values are much higher than previously reported, strongly suggesting that the diffusion tensor model is inadequate in the vast majority of WM regions. This has serious implications for downstream processing applications that depend on this model, particularly tractography, and the interpretation of anisotropy and radial/axial diffusivity measures.

KW - high-angular resolution diffusion imaging

KW - white matter

KW - partial volume effect

KW - crossing fibers

KW - constrained spherical deconvolution

KW - residual bootstrap

KW - bedpostx

KW - TENSOR MRI

KW - SPHERICAL DECONVOLUTION

KW - WEIGHTED MRI

KW - HUMAN BRAIN

KW - TRACTOGRAPHY

KW - ANISOTROPY

KW - IMAGES

KW - ARCHITECTURE

KW - NEUROSCIENCE

KW - PRINCIPLES

U2 - 10.1002/hbm.22099

DO - 10.1002/hbm.22099

M3 - Article

SN - 1065-9471

VL - 34

SP - 2747

EP - 2766

JO - Human Brain Mapping

JF - Human Brain Mapping

IS - 11

M1 - N/A

ER -