Diffusion tensor imaging of cerebral white matter integrity in cognitive aging

Abstract

In this article we review recent research on diffusion tensor imaging (DTI) of white matter (WM) integrity and the implications for age-related differences in cognition. Neurobiological mechanisms defined from DTI analyses suggest that a primary dimension of age-related decline in WM is a decline in the structural integrity of myelin, particularly in brain regions that myelinate later developmentally. Research integrating behavioral measures with DTI indicates that WM integrity supports the communication among cortical networks, particularly those involving executive function, perceptual speed, and memory (i.e., fluid cognition). In the absence of significant disease, age shares a substantial portion of the variance associated with the relation between WM integrity and fluid cognition. Current data are consistent with one model in which age-related decline in WM integrity contributes to a decreased efficiency of communication among networks for fluid cognitive abilities. Neurocognitive disorders for which older adults are at risk, such as depression, further modulate the relation between WM and cognition, in ways that are not as yet entirely clear. Developments in DTI technology are providing a new insight into both the neurobiological mechanisms of aging WM and the potential contribution of DTI to understanding functional measures of brain activity. This article is part of a Special Issue entitled: Imaging Brain Aging and Neurodegenerative disease.

Figure 1

Methods for representing diffusion tensor imaging (DTI) data. A = regions of interest (in color) placed directly on DTI image; B = voxel-based morphometry (VBM); C = mean “skeleton” of white matter tracts from tract-based spatial statistics (TBSS); D = fiber tracking of white matter pathways. Image courtesy of Simon Davis, University of Cambridge.

Figure 2

Fiber tracking methodology. A = seed and target regions placed on DTI raw image; B = estimated fibers of the genu of the corpus callosum passing through the seed and target regions; C = “tube” of averaged fibers within the genu; D = voxels within averaged fibers color coded by age group differences in fractional anisotropy (FA), with the magnitude of age-related decline in FA scaled from lower (blue) to higher (red) values [54]. Adapted with permission from Neuroimage, Volume 46/Issue 2, S. W. Davis, N. A. Dennis, N. G. Buchler, L. E. White, D. J Madden, & R. Cabeza, Assessing the effects of age on long white matter tracts using diffusion tensor tractography, pp. 530–541, Copyright 2009, with permission from Elsevier.

Figure 3

A = The spatial extent of five patterns of age-related difference in diffusivity, relative to the total volume of age-related decrease in FA values across regions (55,973 mm2), from Burzynska et al. [53]; B = spatial localization of the main diffusivity pattern: age-related decrease in FA, increase in RD and MD (in red) and additionally an increase in AD (in orange); C = WMH volume accounts for age-related decrease only in a few WM regions. Age-related FA decrease (in yellow) is contrasted with age-related FA decrease after accounting for WMH volume (in red). FA = fractional anisotropy; RD = radial diffusivity; AD = axial diffusivity; MD = mean diffusivity; WMH = white matter Hyperintensity; ALIC = anterior limb of the internal capsule; BCC = body of the corpus callosum; EC = external capsule; FX = fornix; GCC = genu of the corpus callosum; MFG = middle frontal gyrus; SCC = splenium of the corpus callosum. Adapted with permission from Neuroimage, Volume 49/Issue 3, A.Z. Burzynska, C. Preuschhof, L. Backman, L. Nyberg, S. C. Li, U. Lindenberger, & H. R. Heekeren, Age-related differences in white matter microstructure: region-specific patterns of diffusivity, pp. 2104–2112, Copyright 2010, with permission from Elsevier.

Figure 4

Color-coded fractional anisotropy (FA) maps, with red = right–left, green = anterior– posterior, blue = superior–inferior. In three axial slices, the dashed lines and arrows highlight regions with severe susceptibility-induced distortions and eddy current-induced FA errors, respectively [162]. A = no correction. B = static correction; C = dynamic correction. Adapted with permission from Neuroimage, Volume 57/Issue 4, T.-K. Truong, N.-k. Chen, & A. W. Song, Dynamic correction of artifacts due to susceptibility effects and time-varying eddy currents in diffusion tensor imaging, pp. 1343–1347. Copyright 2011, with permission from Elsevier.

Figure 5

A = Diffusion spectrum imaging (DSI) tractography of monkey cerebral cortex showing fibers within the white matter of the cortical gyrus, and radiate fibers within adjacent gyri of the cerebral cortex (arrows); B = DTI tractography of a corresponding region shows fewer fibers within the white matter of the gyrus and no radiate fibers within the cortex [175]. Reprinted from Neuroimage, Volume 41/Issue 4, V.J. Wedeen, R.P. Wang, J.D. Schmahmann, T. Benner, W.Y.I. Tseng, G. Dai, D.N. Pandya, P. Hagmann, H. D'Arceuil, & A.J. de Crespigny, Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers, pp. 1267–1277, Copyright 2008, with permission from Elsevier.